As healthcare institutions and their partners embark on the journey of integrating autonomous artificial intelligence and various AI technologies, it's vital for them to perform a comprehensive assessment of their data assets. Understanding the permissible uses of this data under HIPAA and associated regulations is crucial, stated attorney Jordan Cohen from Akerman LLP.

In an insightful discussion with ISMG, Cohen highlighted the implications of straying from legal data usage. He pointed out that if protected health information (PHI) is mishandled, it could result in a reportable breach.

Cohen emphasized that many of the essential strategies HIPAA-compliant entities should adopt during their implementation of autonomous AI are not exclusively tied to AI. A meticulous inventory of data flow is essential. He advised that organizations should map out and account for how data is collected, processed, stored, and transmitted, including how vendors interact with it. "If you lack clarity on what data exists, its location, and who has access to it, securing that data and ensuring patient privacy becomes a formidable challenge," he remarked.

While these data management practices have been promoted for years, their importance has grown exponentially in the era of autonomous AI deployments.

In his video interview with ISMG, Cohen further explored several topics, including:

- The prevalent applications of autonomous AI in clinical and administrative settings within healthcare, along with the types of PHI, electronic health records, and other data being commonly utilized.
- Additional legal and regulatory considerations relevant to the deployment of AI technologies.
- Key considerations for AI, such as implementing technical safeguards, effective incident response strategies, ongoing monitoring, and ensuring transparency and patient consent.
- The potential for AI to enhance data privacy and security measures across healthcare and other industries.

Cohen serves as a partner at Akerman LLP and leads the firm’s digital health practice, where he provides legal guidance to clients regarding transactions involving healthcare providers and related platforms. His expertise encompasses issues of federal and state privacy compliance, including adherence to HIPAA's provisions regarding Privacy, Security, and Breach Notification, along with navigating state-specific breach notification laws. Additionally, he advises on a wide array of regulatory matters pertinent to healthcare, such as compliance with fraud and abuse regulations like the Anti-Kickback Statute and the Stark Law.

The Need for Enhanced Data Oversight in AI Healthcare Applications

A quiet hospital data hub at dawn: a physician in a crisp white coat stands before a sleek workstation where cascading, semi-transparent streams of medical records and AI-generated charts swirl around a glowing red padlock icon, natural morning light filtering through tall windows to underscore the urgent need for stronger data oversight in AI-powered healthcare.

Why AI in Healthcare Demands Stronger Data Oversight - BankInfoSecurity

- Healthcare organizations must inventory data for HIPAA compliance amid AI integration challenges.  
- Failures in data usage can lead to breaches involving protected health information.  
- Critical practices include understanding data flow and safeguarding patient privacy in AI deployments.  

Artificial Intelligence & Machine Learning , HIPAA/HITECH , Next-Generation Technologies & Secure Development

Why AI in Healthcare Demands Stronger Data Oversight

Attorney Jordan Cohen of Akerman on AI Challenges Ahead

Jordan Cohen, partner, Akerman LLP

As healthcare organizations and their vendors develop and implement agentic artificial intelligence and other AI technology, they should conduct a thorough data inventory and have a solid understanding of what data can and cannot be used under HIPAA and other laws, said attorney Jordan Cohen of law firm Akerman LLP.

See Also: Know Thy Enemy: Threats to Cyber Resilience

"If you fall outside of a permissible use, then technically, if protected health information is involved, that can be considered a reportable breach," he said in an interview with ISMG.

Many of the steps that HIPAA-regulated enterprises should take in their agentic AI implementations aren't really "AI specific," he said.

"A data flow inventory is going to be really important. So diagramming and accounting for how you're ingesting data, processing it, storing it and how it's leaving your systems, how vendors are touching it and what they're doing to that data is going to be critical," he said.

"Because if you don't know what data you have, where it lives, who's accessing it, then it's really difficult to secure it and to protect patient privacy," he said.

"These are practices that we've been discussing for years," he said. But in the age of agentic AI and other AI deployments, they are especially important.

In this video interview with ISMG, Cohen also discussed:

Most common current uses of agentic AI for clinical and administrative purposes in healthcare, and the types of PHI, electronic health record and other data frequently being utilized;

Other legal and regulatory issues to consider in the deployment of AI;

Other critical AI issues to consider, including technical safeguards, incident, response, monitoring, transparency and consent of patients;

Opportunities for AI to improve data privacy and security in healthcare and other sectors.

Cohen is a partner at Akerman LLP and team leader of the firm's digital health practice. He advises clients on transactions involving healthcare providers and other healthcare-related platforms. That includes providing legal counsel related to federal and state privacy and data security matters, including compliance with HIPAA's Privacy, Security and Breach Notification Rules, as well as compliance with state breach notification laws. He also provides guidance on a broad range of healthcare regulatory matters, including compliance with fraud and abuse laws including the Anti-Kickback Statute and the Stark Law.

Why AI in Healthcare Demands Stronger Data Oversight

Recent advancements in artificial intelligence (AI) within the healthcare sector have sparked a paradigm shift towards integrating these technologies on a larger scale. As organizations pivot from small trials to expansive AI-driven transformation initiatives, they face a critical inquiry: has sufficient value been derived from these tools to warrant increased investments? Furthermore, they must continually reassess how to gauge that value in clinical environments.

The degree of value realization from AI investments can differ significantly across the healthcare landscape. For instance, in July, UnitedHealth Group, a major player in the insurance market, announced plans to allocate nearly $1.5 billion towards AI projects across various areas such as insurance operations, administrative automation, and patient care technologies. Similarly, competitors like Humana and Centene are making substantial investments, recognizing the potential for long-term efficiency and success these technologies can foster.

As for return on investment (ROI), emerging evidence suggests that significant value can be unlocked if AI tools are effectively integrated into existing workflows. A report from Productive/Edge in 2026 revealed that every dollar spent on AI tools could yield a return of approximately $3.20 over a span of 14 months. Furthermore, the study highlighted that organizations typically realized around 147% ROI within three years, with 45% of them achieving noticeable and positive ROI within just the first 12 months.

However, not all findings are as optimistic. Past reports have pointed out a stark contrast, with some indicating that AI investments often struggle to deliver a satisfactory ROI. A notable study by MIT last year claimed that nearly 95% of AI pilot projects failed to produce measurable benefits, while another McKinsey report stated that although around 80% of companies embraced some form of generative AI, many reported negligible improvements in their financial outcomes.

This divergence underscores a fundamental challenge in the adoption of innovative technologies: conventional methods may fall short in capturing ROI. The two contrasting perspectives on AI's value seem to reflect differing definitions of "value," underscoring the need for clarity in how it is measured.

For instance, the use of ambient scribing technology is demonstrating a promising upside. A recent study published in JAMA found that adopting AI scribes resulted in a reduction of 13.4 minutes of electronic health record (EHR) time and 16.0 fewer minutes of documentation for each patient encounter, alongside a boost of 0.49 additional visits each week. While the minutes saved per appointment may appear minor, when multiplied across multiple clinicians and facilities, the cumulative impact becomes significant. In radiology, the efficiency gains from AI were even more pronounced, shortening interpretation times for various conditions by substantial margins.

Even though these enhancements may not lead directly to obvious financial savings or a surge in patient encounters, they nonetheless convey important insights on the necessity of context in measuring return on investment. Importantly, one aspect worth considering is physician satisfaction. Saving a few minutes per patient can greatly enhance a physician's quality of life, especially in light of rising burnout levels in the healthcare profession. With AI tools becoming indispensable in medical education and hospital operations, there is a real potential for these technologies to become standard practice. Organizations that hesitate in implementing AI solutions may risk falling behind, hindering their ability to attract and retain talent in an already strained workforce.

The financial pressures on healthcare institutions are well-acknowledged, with profit margins traditionally being very tight. For instance, a report noted that the median operational margin for U.S. health systems hovered around -0.1% in 2023, improved to 1.6% in 2024, but then declined to 0.4% early in 2026. Given this financial landscape, healthcare organizations must be judicious with investments, particularly in expensive AI initiatives.

Historically, when transformative technologies emerge, conventional metrics for assessing value often struggle to align with new realities. AI represents one of the most significant technological revolutions in both human history and contemporary healthcare. To navigate this shift effectively, organizations will need to rethink outdated approaches to financial investment and strategically define what "value" looks like in today's fast-evolving healthcare environment.

Assessing ROI for Healthcare AI Might Need an Alternative Strategy

A modern hospital conference room bathed in soft morning sunlight. At its center, a senior doctor in a crisp white coat points to a large glass display etched with a heartbeat-shaped ROI graph intertwined with subtle circuit patterns and coin icons. On the polished wood table below, a stethoscope rests beside a measuring tape unraveling into trails of binary code, while a small group of physicians lean forward, captivated by this innovative new approach.

Measuring ROI For Healthcare AI May Require A New Approach - Forbes

- Major healthcare organizations like UnitedHealth Group are investing billions in AI technology.  
- Initial studies show promising ROI potential, but many pilots yield disappointing results.  
- Measuring AI value and ROI requires new metrics reflecting healthcare's evolving landscape.  

AI tools in healthcare may add value in novel and non-traditional ways. getty

While the last two years have seen significant progress and uptick in AI implementation, many organizations are contemplating the transition from small, limited pilots to large-scale implementations and AI enabled transformation programs. A big part of this transition is answering a key question: has value been captured from these tools sufficiently to justify increased investment, and if so, how should value from AI in healthcare settings continue to be measured?

This question of investment and value capture varies markedly across the industry. In July, UnitedHealth Group, one of the largest payor organizations in the world, announced that it would be investing nearly $1.5 billion in AI across its different business units, ranging from insurance and back-office automation functions to care delivery and technology operations. Competitors such as Humana and Centene are investing similarly, understanding that these initiatives will likely lead to long term productivity gains and operational success.

With regard to the return on investment (ROI), evidence is slowly emerging that there is significant value to be captured if these tools are incorporated correctly into existing workflows. In fact, a 2026 Productive/Edge report indicates that for every $1 dollar invested in AI tools, there is roughly $3.20 captured in return over 14 months; additionally, per the study, organizations were found to achieve nearly 147% ROI within 3 years, with 45% of organizations achieving measurable and positive ROI within just 12 months.

Not all studies have been so encouraging, however. These numbers are in stark contrast to many earlier reports which highlighted that AI investments frequently fail in returning value on investment. A key study released by MIT last year caused significant uproar across the industry after finding that nearly 95% of AI based pilots were failing to deliver measurable returns. Another landmark McKinsey study indicated that while nearly 8 in ten companies have invested in some type of Gen AI functions, the same amount reported no significant improvement or impact to their bottom line.

Therefore, the ROI capture has not been so clear.

However, this highlights a fundamental problem with novel innovation and technology adoption: ROI cannot be measured using conventional means. While the two opposing camps regarding the return on investment for AI may seemingly propose opposing views on the value of the technology, the reality is that they are just describing different measures of capturing value. The word “value” therefore, has to be incredibly well defined.

Take for example ambient scribing. Studies are increasingly showing that there is indeed significant upside to using this technology. A recent JAMA study found that “AI scribe adoption was associated with 13.4 fewer minutes of EHR time, 16.0 fewer minutes of documentation time, and 0.49 additional weekly visits delivered.” While the minutes saved may seem minimal, over the course of multiple physicians across a clinic, and multiple clinics across a health system, the numbers add up. Another study found that “in radiology, AI-based interpretation reduced the interpretation time for abnormal contrast-enhanced brain CT lesions by 11.23%, the interpretation time for lung lesions by 52.82%, and the analysis time for peripheral blood smears by 61%."

While these numbers are not always jaw-dropping and may not add up to significant, tangible cost savings or increased visits/encounters completed, they are still important, and indicate that return on investment needs to be measured with specific context in mind. One such novel measurement must be a focus on physician satisfaction. Across 17-22 patients per day, a few minutes saved per patient encounter can mean a huge quality of life uplift for a physician. Although not long enough to “squeeze another patient in,” there are still significant benefits to this, given that healthcare attrition and burnout are at all time highs. Additionally, these tools have increasingly becoming table-stakes for physicians. As medical education programs and hospitals are increasingly adopting AI tools in their daily workflows, there will soon be a critical mass reached where these tools will become a mainstay of “modern medical practice.” Systems that thereby do not adopt these tools may fall behind the curve, leading to poor talent attraction and challenges in maintaining a sustainable workforce.

It has been well documented that hospital and healthcare margins are razor-thin: “The median operating margin across U.S. health systems ran -0.1% in 2023, recovered to 1.6% in 2024, slid to 1.0% in 2025, and has fallen to a 0.4% year-to-date median as of March 2026.” Therefore, organizations cannot invest in multibillion dollar AI adoption programs without determining tangible ROI. However, as witnessed many times in decades past, when industry upending technologies arrive, traditional value models often do not conform with the realities of modernization. AI is undoubtedly one of the largest technological shifts in the history of humanity and in modern medicine. Therefore, organizations have to reform traditional and outdated financial and investment methods, and instead, must be incredibly strategic and thoughtful about what “value” truly means in this day and age.

Measuring ROI For Healthcare AI May Require A New Approach

Concerns surrounding the use of artificial intelligence (AI) in healthcare are widespread among Americans, particularly regarding privacy and data security risks associated with open-source AI models. Despite this unease, many individuals are increasingly turning to AI chatbots for medical guidance.

Recent findings from the Pew Research Center reveal that approximately one-third of American adults have utilized AI chatbots for health-related diagnoses and treatments, with a significant majority finding these services beneficial.

As governments worldwide grapple with the implications of generative AI, the technology has become an integral part of everyday life. While critics highlight its potential for misuse, proponents argue that AI has the potential to transform healthcare delivery—though opinions are divided on whether these changes are positive or negative.

The latest Pew data indicates that while some users have replaced traditional healthcare consultations with AI tools, a considerable number of people express concern regarding the extent of AI's integration into medical services. The survey noted that over 70% of respondents feel it is crucial for their healthcare providers to disclose any AI usage during their treatment.

In contrast, only 22% of participants reported a clear understanding of how AI technology is implemented in their healthcare, with nearly half uncertain about its use. More than 50% of respondents wish they had a greater say in whether AI is employed in their medical care.

The public’s awareness of AI surged with the launch of ChatGPT in 2022, but within healthcare, AI encompasses a far broader array of technologies. Tim Curry, a public health lecturer, emphasized that his research into machine learning applications in medicine has been ongoing for decades. He highlighted a disconnect between public perception and the realities of AI's role in healthcare.

Despite the lack of trust in AI among some demographics, a substantial percentage of people—over 80%—want to be informed about AI's role in the interpretation of medical scans and diagnoses. Jyoti Pathak, dean of Arizona State University’s School of Technology for Public Health, underscored the necessity of clear communication, stating that patient trust is fundamental to the widespread acceptance of AI in healthcare settings.

AI’s applications in diagnostics pose the greatest risks, according to Pathak, necessitating rigorous FDA regulation, clinical validation, and transparency. In contrast, administrative AI used for tasks like scheduling carries lower risks but still requires patient education and consent.

Despite skepticism, surveys indicate that many individuals are opting for AI chatbots over professional medical consultations. More than one-third of over 4,000 surveyed Americans reported using AI chatbots for medical inquiries, often citing affordability as a motivating factor.

Younger adults, particularly those aged 18-29, show the highest engagement with AI chatbots, despite their general apprehension toward AI. This demographic is significantly affected by rising healthcare costs and lower insurance coverage compared to other age groups.

However, relying on AI chatbots can be problematic, as many services require users to agree to terms that allow their interactions to contribute to future AI training, potentially compromising personal health data. Moreover, the current development stage of these chatbots may not guarantee reliable medical advice, leading to misinformation.

Pathak cautioned that without access to appropriate medical histories, AI chatbots could mislead users, instilling either unwarranted optimism or unnecessary alarm. Curry proposed that medical practices might soon create their own AI systems to handle health-related inquiries, which would be HIPAA compliant, unlike existing open-source models.

Until such solutions are available, Curry advises patients who cannot visit a doctor to consult credible sources like Mayo Clinic and Cleveland Clinic for peer-reviewed health information.

Pew: Many Americans uncertain about AI in healthcare

Inside a warmly lit doctor’s exam room, a physician in scrubs and a middle-aged patient lean over a tablet screen showing a simple AI-brain entwined with a stethoscope icon; both wear uncertain, furrowed expressions. A small U.S. flag hangs on the wall behind them, and on the desk lies a stethoscope and patient chart—capturing cautious curiosity about AI in healthcare.

Pew: Americans largely unsure about AI in healthcare - Courthouse News

- A third of Americans use AI chatbots for medical advice despite privacy concerns.  
- Over 70% want transparency about AI use in their healthcare experiences.  
- Many AI chatbots risk misinformation and lack access to individual medical histories.  

Because open-source AI models are not subject to HIPAA, disclosing medical information can open users up to data privacy risks.

(CN) â Most Americans are wary of generative AI in healthcare, but a growing number use AI chatbots for medical information and advice.

According to the Pew Research Center, nearly a third of Americans have used AI chatbots for medical diagnosis and treatment â and nearly all who do say it’s helpful.

As world governments race to balance the risks and rewards of generative artificial intelligence, it has only become more commonplace in daily life. While opponents criticize its potential for criminal harm, AI is ârevolutionizing healthcare delivery,â for better or for worse.

New data from Pew suggests though some have turned to AI partly in place of traditional healthcare, most Americans are concerned about just how much AI is used each time they go to the doctorâs office.

In one of two articles published Tuesday, Pew reports that more than 7 in 10 Americans say itâs extremely or very important that a doctor tell them whether AI is used in their healthcare.

At the same time, only 22% of respondents said they confidently understand how doctors use AI in their healthcare, and nearly 50% said they donât know whether it’s been used.

More than half of the respondents said they donât believe they have a say whether their doctors use AI, and would like to have more control.

The average person became far more aware of just one type of AI with the release of ChatGPT in 2022. But in healthcare, AI refers to a far wider range of technologies than generative AI models.

âWe have been researching uses of machine learning in medical related practices for decades at this point,â public health lecturer Tim Curry told Courthouse News.

Curry said heâs worked with RCG machines equipped with, by todayâs standard, a basic AI model used to interpret patterns.

âI think most of the world is still catching up with what AI really means, how it is really being used, and what the full impact is,â the Northern Arizona University instructor said in an email. âEspecially outside of generative AI.â

Still, more than 80% of respondents say they want to know whether their doctor uses AI to analyze scans and make diagnoses.

âPatients have a fundamental right to know when AI touches their care,â said Jyoti Pathak, dean of Arizona State University’s School of Technology for Public Health. âBroad adoption of healthcare AI requires trust, and trust depends on clear disclosure.â

AI in diagnostic and treatment algorithms comes with the most risk, Pathak said in an email. It requires âstringent FDA oversight, clinical validation, explicit transparency, and strict clinician review.â

Administrative AI models for scheduling and billing, he said, are lower risk and could improve operational efficiency.

Both he and Curry said education and consent are necessary for both.

âThese really are not separate things,â Curry said. âYou would need appointments and testing to obtain the data that an AI model may be trained to help analyze.â

Despite a general lack of trust in AI, more Americans seem to be turning to AI chatbots over medical professionals.

In a survey of more than 4,000 Americans, more than a third of respondents said they have used an AI chatbot for medical information or advice.

Of those respondents, most reported that they used the chatbot for free and affordable diagnoses. A smaller percentage said they used a chatbot to learn more about a diagnosis from a doctor or to better understand lab results.

Despite young people showing more general distrust in AI, the age group most likely to use AI chatbots for medical advice was 18-29. Medical costs are rising at historic rates, and young adults have the lowest insurance rate of any other age group.

AI chatbots may seem like a simple solution, but âmany AI services require users to opt-out of having their chats used for training future AI models, meaning your personal health data is now being used to train the next AI advancement,â Curry said.

Besides, chatbots at their current level of development canât be relied upon for accurate medical advice.

Because open-source tools often lack access to specific patient medical history and clinical contest, Pathak said chatbots are likely to persuasively misinform users, leading to either false reassurance or unnecessary panic

Curry opined that medical practices could soon develop their own AI programs to discuss health information in a more controlled setting. Programs like that would be subject to HIPAA, unlike open-source AI models.

In the meantime, Curry recommended that those who canât immediately see a doctor refer to Mayo Clinic, Cleveland Clinic and other free websites with peer-reviewed information.

Pew: Americans largely unsure about AI in healthcare

The integration of AI chatbots into the healthcare landscape is on the rise, impacting patient care both inside and outside medical facilities. A recent survey from the Pew Research Center reveals that both patients and healthcare professionals are increasingly utilizing AI for diagnosing conditions and recommending treatments, yet concerns about transparency in these interactions persist.

Conducted from June 22 to June 28, the survey involved 3,488 U.S. adults and found that around 25% of participants have employed chatbots to help diagnose their symptoms. The primary motivations for using these tools include their convenience and cost-effectiveness. Many patients turn to AI chatbots for quick access to information, clarification of a healthcare provider’s diagnosis, better comprehension of lab results, and assistance in determining if they need to seek in-person medical attention.

The responses indicate that nearly half of those surveyed (47%) found the health information provided by chatbots to be "extremely" helpful, while 48% found it "somewhat" beneficial. A mere 5% reported that the advice they received was not helpful at all.

AI chatbots function by mimicking conversational exchanges, utilizing extensive datasets from which they have been trained to formulate responses to users’ inquiries. Many users appreciate the 24/7 availability of these chatbots, as they can often access them free of charge and without the need for an account. However, there are concerns regarding their reliability, as chatbots can produce misleading information—often referred to as "hallucinations." The tendency of AI to provide plausible-sounding answers without necessarily being accurate can be especially troubling in health-related contexts. 

In physician offices, the role of generative AI is expanding as well. According to Pew's findings, while many Americans have used chatbots for health-related inquiries, there is escalating anxiety concerning AI's application in clinical settings.

In the realm of physicians, the survey highlights that a significant portion of patients—72%—believe it is crucial for healthcare providers to inform them about any use of AI in their treatment process. Moreover, around 80% of respondents expect to be notified if AI influences their care or medical decisions, including diagnoses or the evaluation of lab tests and imaging results. AI technologies are often employed for triage and have demonstrated superior efficiency to human emergency room physicians, reducing the time required for X-ray assessments from an average of 11.2 days to just 2.7 days.

Despite a growing reliance on AI in healthcare, nearly half of the respondents (47%) were uncertain about whether AI had played a role in their treatment, highlighting a significant gap in patient awareness regarding the application of this technology.

While the convenience of AI chatbots is acknowledged, the survey indicates a lack of full trust among Americans. Comfort levels vary when it comes to sharing personal health information; with only 29% feeling very comfortable doing so, and 26% expressing discomfort.

Thus, even as AI becomes more prevalent in healthcare, public confidence and understanding have not evolved at the same pace as its implementation.

Americans Curious About When Their Healthcare Providers Utilize AI

A bright hospital lobby flooded with warm natural light: an adult patient in a crisp shirt stands before a freestanding digital board labeled “AI-Assisted or Human-Only?” with a small chip icon next to “AI-Assisted.” A friendly doctor in a white coat gestures toward the board as the patient leans in, curious. An American flag hangs on the wall behind them. Realistic style, shallow depth of field.

Americans Want to Know When Their Healthcare Providers Use AI - CNET

- A quarter of US adults use AI chatbots for health diagnosis, valuing convenience and affordability.  
- Transparency in AI usage is crucial; 72% want providers to disclose AI involvement.  
- Americans express mixed feelings about sharing personal health information with AI chatbots.  

AI chatbots are being used in healthcare, both in and out of the doctor’s office, more than ever before. Patients and medical providers alike are turning to AI for treatments and diagnoses, but transparency remains a key concern, a new survey by Pew Research Center shows.

A poll of 3,488 US adults conducted between June 22 and June 28 found that a quarter of respondents use chatbots to diagnose symptoms. Those turning to chatbots for health mainly cite convenience and affordability as their motive. Patients also use AI chatbots to find information quickly and at no cost, better understand a doctor’s diagnosis, interpret lab results and decide whether to seek in-person care.

Of these survey participants, almost half (47%) say the health information generated by the chatbot was “extremely” helpful, and 48% said the information was “somewhat” helpful. Only 5% said the results were not too helpful or not at all helpful.

AI chatbots work by simulating a conversation between two parties and responding to your prompts or inputs using the massive amounts of information the models were trained on. Many people appreciate the convenience they offer, as chatbots are available 24/7 and can often be accessed for free and sometimes without an account. Chatbots, however, are known to “hallucinate,” or generate false information in their responses, which can be particularly dangerous or alarming when your personal health is in question. Among other issues, they’re trained to produce a probable answer, not necessarily one grounded in truth, and they’re likely to sound particularly confident about that answer.

But generative artificial intelligence is also showing up at the doctor’s office — and Pew found that Americans have greater concerns about its use there.

Read also: ChatGPT Health Will See You Now, and It’s a Big Step Forward for Medical Queries

When doctors turn to chatbots

While recent numbers show that about one in three US adults have used chatbots for health-related questions, medical providers are also increasingly turning to AI in the office for note-taking, interpreting exams and more.

While views on AI use in doctors’ offices vary, one factor remains of critical concern to patients: transparency.

Of those surveyed, 72% say it is “extremely or very important” for their medical provider to disclose any use of AI. The survey also found that a vast majority of people (eight in 10) want to be told if AI is being used directly for care or medical decisions, such as diagnoses and the analysis of scans, X-rays and lab results. AI is frequently used for triage purposes — and has even been shown to outperform human ER doctors — as well as to complete clinical tasks more quickly. For instance, AI has been used effectively to reduce the window of X-ray interpretations from 11.2 to 2.7 days.

Although Americans want advance notice when AI is used in their healthcare, nearly half (47%) are unsure whether it has ever played a role in their treatment, underscoring limited awareness of when, where and how the technology is used.

Americans are not fully comfortable with AI

The survey shows that Americans may find AI chatbots convenient, but not entirely trustworthy.

Views are mixed on sharing personal health histories and diagnoses with AI. Among those surveyed, 29% say they are extremely or very comfortable sharing health information with chatbots, while 26% say they are uncomfortable doing so.

The findings suggest that although AI is increasingly used for health information and care, public confidence and understanding have not kept pace with its adoption.

Americans Want to Know When Their Healthcare Providers Use AI

Recent advancements in artificial intelligence (AI) are significantly altering the landscape of healthcare, with applications ranging from interpreting X-rays to aiding in medical documentation. According to a fresh survey conducted by the Pew Research Center, American adults express varying levels of awareness and concern regarding the integration of AI in their medical treatment.

The survey highlighted that a substantial majority of U.S. adults, approximately 72%, find it crucial for their healthcare providers to disclose if AI is utilized in their treatment plans. This sentiment spans across various demographics, transcending age, gender, educational background, and racial or ethnic categories. As discussions intensify around the ethical and legal responsibilities of medical professionals to obtain patient consent before implementing AI tools, many Americans are voicing their preferences.

In fact, around 80% of respondents indicated that they believe it is essential for their healthcare providers to inform them about AI's role in significant areas affecting their care, such as medical diagnostics, lab result interpretations, and imaging analyses. The desire for transparency extends to administrative tasks as well, where a notable 72% want to be informed if AI is involved in note-taking during consultations.

While some administrative functions—like scheduling appointments (56%) and managing prescription refills (64%)—attracted a bit more ambivalence, a significant number still felt it was necessary to be informed about AI’s involvement. Notably, 33% of respondents believed that disclosing AI use in appointment scheduling wasn’t necessary.

The survey also uncovered a sentiment of lack of control, with 53% of participants feeling that they have little or no input regarding AI usage in their healthcare. Reflecting a desire for agency, 63% of those surveyed wish they could influence whether AI is integrated into their medical care, contrasting sharply with only 21% who expressed satisfaction with their current level of involvement.

Interestingly, nearly half of the participants were unsure if AI had ever been utilized in their healthcare, with only 16% affirming that their healthcare providers had used AI in any capacity. This uncertainty was prevalent across educational levels, although those with higher educational qualifications were slightly more likely to say their providers had employed AI.

Among those who were aware of AI usage in their medical encounters, a majority reported only a moderate understanding of how it was implemented, with 44% claiming they understood somewhat well and 32% stating they didn’t grasp it well at all.

This data reveals a clear picture: as AI becomes increasingly common in healthcare settings, patients are not only eager for transparency but also seek more engagement and understanding regarding how AI tools are being used in their care.

Americans seek transparency regarding AI usage in their healthcare.

A bright, sunlit modern examination room in a U.S. hospital: a doctor and a patient sit at a glass table topped by a transparent digital interface showing layered patient charts and vivid AI decision pathways. Warm natural light floods through large windows, illuminating the clean, open space. A small American flag hangs on the wall, underscoring the demand for clarity and trust when AI guides healthcare.

Americans want transparency when AI is used in their healthcare - Pew Research Center

- Seventy-two percent of Americans want doctors to disclose AI usage in their healthcare.  
- Eighty percent prefer notification if AI is used for direct medical care tasks.  
- Many Americans feel they lack control over AI usage in their healthcare decisions.  

(MoMo Productions via Getty Images)

Whether for reading X-rays or taking notes on patient-doctor conversations, artificial intelligence is reshaping healthcare.

A new Pew Research Center survey asked U.S. adults about their views on AI at the doctor’s office. A common thread in their responses is that they’re uncertain about whether it’s used in their healthcare, and they want to be informed if it is. Here are more key takeaways from the survey.

Americans want doctors to tell them if AI is being used in their healthcare

Most Americans say it’s highly important that their healthcare provider tells them if they are using AI % of U.S. adults who think it is __ important that a doctor or other healthcare provider tells them if they are using AI in their healthcare Note: Those who did not answer are not shown. Source: Survey of U.S. adults conducted June 22-28, 2026. PEW RESEARCH CENTER Share this chart Share on X Share on Facebook Most Americans say it’s highly important that their healthcare provider tells them if they are using AI % of U.S. adults who think it is __ important that a doctor or other healthcare provider tells them if they are using AI in their healthcare Extremely/Very Somewhat Not too/Not at all U.S. adults 72 16 7 Download data as .csv Note: Those who did not answer are not shown. Source: Survey of U.S. adults conducted June 22-28, 2026. PEW RESEARCH CENTER Chart Data Download Image Share

About seven-in-ten U.S. adults (72%) say it’s extremely or very important that a doctor or other healthcare provider tells them if they’re using AI in their healthcare. And majorities across age, gender, education, and racial and ethnic groups say this. (Read the appendix for a detailed breakdown.)

This comes as discussion is rapidly evolving about medical professionals’ legal and ethical obligations to get patients’ consent before using AI tools.

Many Americans say doctors should tell them if AI is being used, even for administrative tasks

Eight-in-ten or more want a provider or their office to tell them if AI is used for things that can directly affect care or medical decisions, like analyzing medical scans, diagnosing conditions and explaining lab results.

Majority of Americans say they should be told if AI is being used in their healthcare, even for administrative tasks % of U.S. adults who say a healthcare provider or their office should tell them if AI is being used in their healthcare to … Note: Those who did not answer are not shown. Source: Survey of U.S. adults conducted June 22-28, 2026. PEW RESEARCH CENTER Share this chart Share on X Share on Facebook Majority of Americans say they should be told if AI is being used in their healthcare, even for administrative tasks % of U.S. adults who say a healthcare provider or their office should tell them if AI is being used in their healthcare to … No Yes Not sure Analyze medical scans 10 81 9 Make a diagnosis 10 81 9 Explain lab results 11 80 9 Take notes during a medical appointment 18 72 9 Order prescription refills from a pharmacy 25 64 10 Schedule a medical appointment 33 56 10 Download data as .csv Note: Those who did not answer are not shown. Source: Survey of U.S. adults conducted June 22-28, 2026. PEW RESEARCH CENTER Chart Data Download Image Share

When it comes to AI being used to take notes in the background during an appointment, 72% of Americans say they should be told.

Smaller shares – but still more than half – feel this way about behind-the-scenes administrative activities. These include scheduling medical appointments (56%) and getting prescription refills (64%). Scheduling is the task that the largest share feels they do not need to be told about (33%).

For each of the tasks we asked about, another 9% to 10% say they’re not sure whether they need to be informed about AI use.

White adults were more likely than Black or Hispanic adults to say they want to be told if AI is used for the three tasks we asked about that directly shape medical decisions. (Read the appendix to explore more differences by race and ethnicity.)

A majority of Americans want more say over whether AI is used in their healthcare

Many Americans don’t think they have a say whether AI is used in their healthcare, and would like more % of U.S. adults who say that in general, they think they have __ (of a) say over whether a doctor or other healthcare provider uses AI in their healthcare Download Image % of U.S. adults who say they __ over whether AI is used in their healthcare Download Image Note: Those who did not answer are not shown. Source: Survey of U.S. adults conducted June 22-28, 2026. PEW RESEARCH CENTER Share this chart Share on X Share on Facebook Many Americans don’t think they have a say whether AI is used in their healthcare, and would like more Not too much/None Some A great deal/Quite a bit Not sure % of U.S. adults who say they have ___ (of a) say whether a healthcare provider uses AI in their healthcare… 53 16 17 14 World like more say Are comfortable with how much say they have Not sure % of U.S. adults who say they __ over whether AI is used in their healthcare 63 21 16 Download data as .csv Note: Those who did not answer are not shown. Source: Survey of U.S. adults conducted June 22-28, 2026. PEW RESEARCH CENTER Chart Data Download Image Share

Americans tend to feel like they don’t have a lot of control over AI use in their healthcare. The largest share (53%) say they have not too much or no say about whether a provider uses AI in their care. Another 33% feel they have at least some say.

White adults (60%) are more likely than Hispanic (40%), Black (43%) and Asian adults (42%) to say they have little to no say. (Read the appendix to explore more differences by race and ethnicity.)

In addition, Americans are three times as likely to say that they want more input on whether AI is used in their healthcare as they are to say they’re comfortable with how much input they have (63% vs. 21%). Another 16% aren’t sure.

Many Americans are unsure whether and how AI is being used in their healthcare

Nearly half of Americans are not sure if AI has been used in their healthcare % of U.S. adults who say that to the best of their knowledge, doctors or other healthcare providers have/have not used AI in their healthcare Note: Those who did not answer are not shown. Source: Survey of U.S. adults conducted June 22-28, 2026. PEW RESEARCH CENTER Share this chart Share on X Share on Facebook Nearly half of Americans are not sure if AI has been used in their healthcare % of U.S. adults who say that to the best of their knowledge, doctors or other healthcare providers have/have not used AI in their healthcare Yes Not sure No U.S. adults 16 46 37 Download data as .csv Note: Those who did not answer are not shown. Source: Survey of U.S. adults conducted June 22-28, 2026. PEW RESEARCH CENTER Chart Data Download Image Share

A small share (16%) say their doctors or other healthcare providers have ever used AI in their healthcare. Much larger shares say it hasn’t been used (37%) or they’re not sure (46%). This comes as AI use in hospitals and doctor’s offices is increasingly common.

Adults with higher education levels are more likely to say their providers have used AI. For example, 25% of those with a postgraduate degree say this, compared with 11% of those with a high school diploma or less education. Yet across education levels, Americans are similarly likely to say they’re not sure (ranging from 46% among those with a high school diploma or less to 48% of those with a postgraduate degree). (Read the appendix for more details.)

Mixed understanding of how AI is used in their healthcare Among those who say their doctor or healthcare provider has used AI in their healthcare, % of U.S. adults who say they understand __ well how AI has been used Note: Those who did not answer are not shown. Source: Survey of U.S. adults conducted June 22-28, 2026. PEW RESEARCH CENTER Share this chart Share on X Share on Facebook Mixed understanding of how AI is used in their healthcare Among those who say their doctor or healthcare provider has used AI in their healthcare, % of U.S. adults who say they understand __ well how AI has been used Extremely/Very Somewhat Not too/Not at all U.S. adults 22 44 32 Download data as .csv Note: Those who did not answer are not shown. Source: Survey of U.S. adults conducted June 22-28, 2026. PEW RESEARCH CENTER Chart Data Share

Among those who say their providers have used AI in their care, the largest share say they understand somewhat well how it’s been used (44%). But they are more likely to say they understand it not too or not at all well (32%) than extremely or very well (22%).

Note: Here are the survey questions used for this analysis, the detailed responses and the survey methodology.

Appendix

Most Americans across demographic groups say it is highly important their healthcare provider tells them if they are using AI % of U.S. adults who think it is __ important that a doctor or other healthcare provider tells them if they are using AI in their healthcare Extremely/

Very Somewhat Not too/

Not at all U.S. adults 72 16 7 Men 68 18 8 Women 74 14 5 White 75 15 6 Hispanic 66 16 9 Black 67 17 6 Asian* 62 23 8 Ages 18-29 68 14 10 30-49 69 16 7 50-64 73 18 5 65+ 76 14 5 Postgrad 70 17 8 College grad 75 16 6 Some college 75 15 5 HS or less 67 16 8 * Estimates for Asian adults are representative of English speakers only.

Note: Respondents who said “Not sure” or did not answer are not shown. White, Black and Asian adults include those who report being only one race and are not Hispanic; Hispanics are of any race. Source: Survey of U.S. adults conducted June 22-28, 2026. PEW RESEARCH CENTER Chart Share

White adults are more likely than Black or Hispanic adults to say they want to be told if AI is used for diagnoses, medical scans or explaining lab results % of U.S. adults who say a healthcare provider or their office should tell them if AI is being used in their healthcare to … Yes No Not sure Schedule a medical appointment White 57 33 9 Hispanic 60 25 14 Black 46 37 14 Asian* 53 41 6 Take notes during a medical appointment White 75 17 7 Hispanic 69 18 13 Black 62 21 15 Asian* 67 24 9 Make a diagnosis White 86 7 7 Hispanic 72 14 13 Black 64 18 15 Asian* 83 10 6 Analyze medical scans White 85 8 7 Hispanic 78 10 12 Black 65 16 16 Asian* 84 8 8 Order prescription refills White 65 26 8 Hispanic 64 21 15 Black 58 22 18 Asian* 63 31 7 Explain lab results White 84 9 7 Hispanic 75 13 12 Black 65 18 15 Asian* 77 17 7 * Estimates for Asian adults are representative of English speakers only.

Note: Respondents who did not answer are not shown. White, Black and Asian adults include those who report being only one race and are not Hispanic. Hispanic adults are of any race. Source: Survey of U.S. adults conducted June 22-28, 2026. PEW RESEARCH CENTER Chart Share

White adults are more likely than other racial and ethnic groups to say they have little to no say whether AI is used in their healthcare % of U.S. adults who say that in general, they think they have __ (of a) say over whether a doctor or other healthcare provider uses AI in their healthcare A great deal/

Quite a bit Some Not too much/

None Not sure White 12 15 60 12 Hispanic 27 16 40 17 Black 23 16 43 17 Asian* 20 24 42 13 * Estimates for Asian adults are representative of English speakers only

Note: Respondents who did not answer are not shown. White, Black and Asian adults include those who report being only one race and are not Hispanic; Hispanics are of any race. Source: Survey of U.S. adults conducted June 22-28, 2026. PEW RESEARCH CENTER Chart Share

Americans want transparency when AI is used in their healthcare

The integration of AI chatbots in the healthcare sector has seen significant growth, both in clinical settings and for patients seeking support outside of traditional medical environments. A recent survey conducted by the Pew Research Center highlights the increasing reliance on AI for diagnosis and treatment, while also addressing a prominent concern: the need for transparency in its use.

Between June 22 and June 28, the Pew Research Center surveyed 3,488 adults in the United States, revealing that approximately 25% of participants have utilized chatbots to identify symptoms. The main reasons behind choosing these AI tools include convenience and cost-effectiveness. Patients often seek out chatbots for quick access to information, clarification of a doctor’s diagnosis, interpretation of laboratory results, or to determine if in-person medical attention is necessary.

The survey results indicated that nearly half of the respondents (47%) found the health information provided by chatbots to be "extremely" helpful, while 48% considered it "somewhat" helpful. A mere 5% reported that the responses were not very beneficial or entirely unhelpful.

AI chatbots operate by mimicking human conversation, generating responses based on the extensive data they have been trained on. Users value their availability around the clock, along with the fact that many services come at no charge and do not require account creation. Nonetheless, a critical downside is that these chatbots can produce inaccurate or misleading information—an issue known as "hallucination." This creates genuine concerns when it comes to health, as these AI tools often provide replies that sound confident yet may not always align with reality.

Generative AI isn't limited to patient-facing applications; it's increasingly finding a place in medical practices as well. The survey revealed heightened concerns among Americans regarding AI's use in clinical environments compared to its application in patient interactions.

The role of AI in medical offices is evolving, with about one-third of US adults having consulted chatbots for health inquiries. Healthcare providers are also leveraging AI for administrative tasks, such as note-taking and analyzing medical examinations.

Despite the promise AI holds, transparency regarding its usage in healthcare remains paramount for patients. The survey showed a notable 72% of respondents emphasize the importance of being informed when their medical providers employ AI technologies. Furthermore, 80% expressed a desire to be notified if AI is directly involved in their care or influences medical decisions, including diagnoses and the evaluation of scans or lab reports. AI's efficiency has improved processes, with some applications significantly speeding up tasks like interpreting X-rays from an average of 11.2 days down to just 2.7 days.

However, there's a gap in public awareness, as nearly half of those surveyed (47%) are uncertain about whether AI has influenced their treatment history, highlighting a lack of understanding regarding where and how these technologies are applied.

The findings indicate that while many Americans appreciate the convenience provided by AI chatbots, they still struggle with trusting them fully. Opinions are divided on sharing personal health data with AI, as only 29% of respondents felt very comfortable doing so, compared to 26% who felt uneasy about it.

In summary, although AI's presence in healthcare is growing, the public's grasp of its role and reliability has not kept pace with this advancement, illustrating a need for better communication and education around the technology.

Americans Seek Information on AI Usage by Healthcare Providers

A warmly lit modern clinic reception area: an adult patient leans over a desk, signing a brochure titled “AI in Your Care: Disclosure Notice” held up by a receptionist in scrubs. Natural sunlight streams through a window where a small American flag is pinned to the wall; on the brochure’s cover a simple AI circuit icon. The scene conveys curiosity and transparency in healthcare.

- A quarter of Americans use AI chatbots for diagnosing symptoms, citing convenience and affordability.  
- 72% of surveyed individuals want transparency regarding AI's role in their healthcare.  
- Many Americans feel uncomfortable sharing personal health information with AI chatbots despite their usage.

AI chatbots are being used in healthcare, both in and out of the doctor’s office, more than ever before. Patient and medical providers alike are turning to AI for treatments and diagnoses, but transparency remains a key concern, a new survey by Pew Research Center shows.

A poll of 3,488 US adults conducted between June 22 and June 28 found that a quarter of respondents use chatbots to diagnose symptoms. Those turning to chatbots for health mainly cite convenience and affordability as their motive. Patients also use AI chatbots to find information quickly and at no cost, better understand a doctor’s diagnosis, interpret lab results and decide whether to seek in-person care.

Of these survey participants, almost half (47%) say the health information generated by the chatbot was “extremely” helpful, and 48% said the information was “somewhat” helpful. Only 5% said the results were not too helpful or not at all helpful.

AI chatbots work by simulating a conversation between two parties and responding to your prompts or inputs using the massive amounts of information the models were trained on. Many people appreciate the convenience they offer, as chatbots are available 24/7 and can often be accessed for free and sometimes without an account. Chatbots, however, are known to “hallucinate,” or generate false information in their responses, which can be particularly dangerous or alarming when your personal health is in question. Among other issues, they’re trained to produce a probable answer, not necessarily one grounded in truth, and they’re likely to sound particularly confident about that answer.

But generative artificial intelligence is also showing up at the doctor’s office — and Pew found that Americans have greater concerns about its use there.

Read also: ChatGPT Health Will See You Now, and It’s a Big Step Forward for Medical Queries

When doctors turn to chatbots

While recent numbers show that about one in three US adults have used chatbots for health-related questions, medical providers are also increasingly turning to AI in the office for note-taking, interpreting exams and more.

While views on AI use in doctors’ offices vary, one factor remains of critical concern to patients: transparency.

Of those surveyed, 72% say it is “extremely or very important” for their medical provider to disclose any use of AI. The survey also found that a vast majority of people (eight in 10) want to be told if AI is being used directly for care or medical decisions, such as diagnoses and the analysis of scans, X-rays and lab results. AI is frequently used for triage purposes — and has even been shown to outperform human ER doctors — as well as to complete clinical tasks more quickly. For instance, AI has been used effectively to reduce the window of X-ray interpretations from 11.2 to 2.7 days.

Although Americans want advance notice when AI is used in their healthcare, nearly half (47%) are unsure whether it has ever played a role in their treatment, underscoring limited awareness of when, where and how the technology is used.

Americans are not fully comfortable with AI

The survey shows that Americans may find AI chatbots convenient, but not entirely trustworthy.

Views are mixed on sharing personal health histories and diagnoses with AI. Among those surveyed, 29% say they are extremely or very comfortable sharing health information with chatbots, while 26% say they are uncomfortable doing so.

The findings suggest that although AI is increasingly used for health information and care, public confidence and understanding have not kept pace with its adoption.

In Watertown, NY, the weather outlook for today features overcast skies accompanied by intermittent rainfall. The temperature is expected to peak at 72°F, with southwest winds blowing at speeds of 10 to 20 mph. There is an 80% likelihood of rain throughout the day.

As we move into the evening, expect continued rain, with temperatures dropping to a low of 56°F. Winds will shift to the south-southwest, maintaining a speed of 10 to 20 mph. The chance of rain increases to 90%, with total precipitation likely reaching close to half an inch.

Patients distrustful of governments and companies promoting AI as a solution for rural healthcare.

A dusky rural road at twilight, lined with cornfields fading into shadows. In the foreground, a small group of adult villagers in work clothes stand tensely around a sleek AI-powered telemedicine kiosk, its screen glowing softly but casting uneasy reflections on their worried faces. Behind them, a distant government building with a fluttering flag and a large roadside billboard reading “AI Health Solutions” loom under darkening skies. Natural, low golden light from the setting sun highlights the tension in the scene.

Patients wary of governments, companies pushing AI as a rural healthcare solution - nny360.com

- Today's weather: cloudy with rain, high temperature of 72°F.  
- Winds are shifting from southwest to south-southwest at 10-20 mph.  
- Tonight's low expected at 56°F with a 90% chance of rain.  

Watertown, NY (13601)

Today

Cloudy with periods of rain. High 72F. Winds SW at 10 to 20 mph. Chance of rain 80%..

Tonight

Periods of rain. Low 56F. Winds SSW at 10 to 20 mph. Chance of rain 90%. Rainfall near a half an inch.

Patients wary of governments, companies pushing AI as a rural healthcare solution

Artificial Intelligence & Machine Learning , Data Privacy , Data Security

Why Healthcare AI Vendor Risk Demands Stronger Oversight

Tom Walsh of tw-Security on Prioritizing High-Risk Vendors and AI Governance

Tom Walsh, founder and principal consultant, tw-Security

Healthcare organizations can't rely solely on vendors' assurances that artificial intelligence tools are secure and trustworthy. As AI adoption accelerates, providers need stronger governance, better oversight of third-party vendors and greater scrutiny of how AI affects patient data and clinical decisions, said Tom Walsh, founder and principal consultant at consultancy tw-Security.

See Also: A Darkening Landscape: AI, Friend and Foe of Cyber Resilience

Many healthcare organizations lack the staff and time to thoroughly assess hundreds of third-party vendors. Walsh recommends prioritizing attention on vendors - including AI technology firms - that pose the greatest risks based on their access to protected health information and personally identifiable information.

Along with those efforts, organizations also need AI governance frameworks, updated vendor assessments and greater transparency into how vendors develop, validate and oversee AI capabilities.

"I would ask for better documentation from the vendors to prove what human oversight they've implemented into their AI products and services," Walsh said. "We do not want something bad happening to a patient because we relied on an AI that wasn't accurate."

Walsh also warned that AI-powered clinical documentation tools - including ambient tools - can introduce errors into electronic health records if clinicians fail to review AI-generated notes before signing them. He advises healthcare organizations to strengthen human oversight, revise business associate agreements and update privacy notices so patients understand how AI collects, processes and uses their information.

"I don't think a lot of patients know or recognize that their conversation with a doctor or clinician is being scribed by an AI agent and then placed into their record," he said. "I think that's one area that patients are not aware of how AI is being used in hospitals or healthcare systems today."

In this video interview with ISMG, Walsh also discussed:

How to prioritize high-risk AI vendors for security and privacy reviews;

Risks related to shadow AI use in healthcare;

How AI-generated clinical documentation can affect data integrity and patient safety.

Walsh is founder, partner and principal consultant of tw-Security, a healthcare privacy and security consulting firm. He is a certified information systems security professional, a nationally recognized speaker and co-author of four books on healthcare information security. Walsh has over 34 years of information security experience. In addition to being an independent consultant, Walsh has been the virtual privacy and information security officer for several healthcare organizations and business associate firms.

Why Healthcare AI Vendor Risk Demands Stronger Oversight

Generate a cinematic news thumbnail for title:Why Healthcare AI Vendor Risk Demands Stronger Oversight. No text in image.

Why Healthcare AI Vendor Risk Demands Stronger Oversight - BankInfoSecurity

Until recently, we spoke of algorithms capable of identifying findings in medical images, prioritizing studies, or supporting physicians in diagnosis. Then came generative artificial intelligence, capable of summarizing information, drafting text, and creating content.

Now, a third stage is emerging: agentic AI. It does not merely generate a response. It can receive an objective, consult multiple sources, interpret context, execute a sequence of tasks, verify results, and escalate exceptions to a human.

In simple terms:

Traditional AI identifies or predicts.

Generative AI creates.

Agentic AI understands an objective, organizes actions, and executes a process.

Its impact on healthcare will not be limited to diagnosis. Agentic AI can also transform the vast universe of documentation surrounding each episode of care, including clinical notes, informed consent forms, referrals, laboratory results, radiology and pathology reports, nursing records, and clinical summaries.

An AI agent could receive documents from multiple sources, determine which patient they belong to, classify them, extract relevant information, identify missing data, integrate them into the appropriate medical record, and trigger alerts or follow-up tasks. It could also consult different systems to generate a preliminary draft of a clinical summary, referral, or discharge note, flag inconsistencies, and submit the result to the responsible healthcare professional for validation.

This is no longer simply about digitizing documents. It is about transforming dispersed content into contextualized, accessible, and actionable clinical information.

Autonomy Requires Governance

An AI agent operating without sufficient context can produce an answer that sounds convincing but is incorrect. If it works with fragmented, outdated, or poor-quality data, it may also automate an organization’s existing errors.

Agentic AI therefore requires interoperability and governed access to reliable information from electronic health records, PACS, RIS, laboratory systems, and document repositories. Its adoption must include clearly defined identities and permissions, traceability of every action, confidentiality safeguards, data provenance validation, human oversight proportional to risk, and mechanisms to stop, correct, or reverse automated actions.

The World Health Organization has emphasized that humans must remain in control of healthcare systems and medical decisions. In healthcare, even a seemingly administrative or documentation-related action can have clinical, legal, financial, and ethical consequences.

For Mexico and Latin America, where technological fragmentation, manual processes, and heavy administrative workloads remain significant challenges, agentic AI represents an opportunity to integrate information, reduce omissions, and give healthcare professionals more time to focus on patients.

However, the strategy should not begin by asking what artificial intelligence can do. It should begin by asking what problem it will solve, what information it will use, what actions it will be authorized to perform, and who will remain accountable for its results.

The next major transformation of AI in healthcare may not take place exclusively in front of a diagnostic image. It will also unfold across the millions of documents and data points accompanying every patient’s care journey.

Artificial intelligence can analyze, generate, and act. Responsibility, however, must remain human.

Norma Contreras is CEO of E+ Healthcare Technology Solutions and CEO of PaxeraHealth Mexico. An expert in healthcare IT, digital transformation, agentic AI, interoperability, cybersecurity, and health information governance, she has more than 25 years of experience leading strategic projects across Mexico and Latin America. She also serves as a business advisor to SMEs.

The Rise of Agentic AI in Healthcare Information Management

Generate a cinematic news thumbnail for title:The Rise of Agentic AI in Healthcare Information Management. No text in image.

The Rise of Agentic AI in Healthcare Information Management - Mexico Business News

Consumer-facing AI can improve public health—but more evidence is needed. Nat. Health 1, 159 (2026).

Plantin, J. -C., Lagoze, C., Edwards, P. N. & Sandvig, C. Infrastructure studies meet platform studies in the age of Google and Facebook. New Media Soc. 20, 293–310 (2018).

OpenAI. Introducing ChatGPT Health. https://openai.com/index/introducing-chatgpt-health/ (2026).

Staff, T. Perplexity health connects medical records and wearables in new AI push. TechRepublic https://www.techrepublic.com/article/news-perplexity-health-ai/ (2026).

Amazon. Introducing Health AI | Personalized insights, human-powered care. Amazon Health AI https://health.amazon.com/health-ai/learn-more

Microsoft. Copilot for Health: your AI Assistant. Microsoft Copilot https://www.microsoft.com/en-us/microsoft-copilot/for-individuals/do-more-with-ai/ai-for-daily-life/copilot-for-health-your-ai-companion-for-smarter-medical-decisions

Ant Group. Ant Group announces major upgrades to its 15-million-MAU AI health app AQ amid wider push into health sector. https://www.antgroup.com/en/news-media/press-releases/1765779300000 (2025).

South China Morning Post. Ant’s AQ health app tops 100 million users amid holiday surge. https://www.scmp.com/tech/article/3344359/ants-aq-health-app-breaks-through-100-million-users-amid-holiday-surge (2026).

Anthropic. Transform healthcare from insight to action. Healthcare | Claude by Anthropic https://claude.com/solutions/healthcare

Mandl, K. D. How AI could reshape health care—rise in direct-to-consumer models. JAMA 333, 1667–1669 (2025).

Lutes, M. E, & Hughes, R. IV. When the front door is an algorithm: guiding consumer use of AI in health care. Health Affairs Forefront https://doi.org/10.1377/forefront.20260302.877526 (2026).

Adler-Milstein, J., Murray, S. G. & Wachter, R. M. The market dynamics for third-party AI tools trying to compete with electronic health record developers. JAMA https://doi.org/10.1001/jama.2026.2394 (2026).

DataReportal. Digital 2026: Global Overview Report — Global Digital Insights. https://datareportal.com/reports/digital-2026-global-overview-report (2025).

Hwang, Y. -M., Ng, M. Y., Pillai, M., Sahai, M. P. & Hernandez-Boussard, T. The landscape of AI implementation in US hospitals. Nat. Health 1, 99–112 (2026).

Rutunda, S. et al. Large language models for frontline healthcare support in low-resource settings. Nat. Health 1, 191–197 (2026).

International Telecommunication Union. Measuring digital development: facts and figures 2025. https://www.itu.int/hub/publication/d-ind-ict_mdd-2025-3/ (2025).

Nimo, C. et al. AfriMed-QA: a pan-african, multi-specialty, medical question-answering benchmark dataset. in Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (eds. Che, W. et al.) 1948–1973 https://doi.org/10.18653/v1/2025.acl-long.96 (Association for Computational Linguistics, 2025).

Ahia, O. et al. Do all languages cost the same? Tokenization in the era of commercial language models. in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (eds. Bouamor, H. et al.) 9904–9923 https://doi.org/10.18653/v1/2023.emnlp-main.614 (Association for Computational Linguistics, 2023).

Designated Health Professional Shortage Areas Statistics. Third Quarter of Fiscal Year 2026 Designated HPSA Quarterly Summary. https://data.hrsa.gov/Default/GenerateHPSAQuarterlyReport/?utm_source=openai (2026).

Journal of Urgent Care Medicine. One Medical: reimaging primary care around the consumer. https://www.jucm.com/one-medical-reimaging-primary-care-around-consumer/ (2016).

Mathis, W. S. et al. Vulnerability index approach to identify pharmacy deserts and keystone pharmacies. JAMA Netw. Open 8, e250715 (2025).

Catalano, G., Khan, M. M. M., Chatzipanagiotou, O. P. & Pawlik, T. Pharmacy accessibility and social vulnerability. JAMA Netw. Open 7, e2429755 (2024).

Fernández, E. V., Mcdaniel, J. & Carroll, N. Examination of the link between medication adherence and use of mail-order pharmacies in chronic disease states. J. Manag. Care Spec. Pharm. 22, 1247–1259 (2016).

Wang, X., Seyler, B. C., Han, W. & Pan, J. An integrated analysis of spatial access to the three-tier healthcare delivery system in China: a case study of Hainan Island. Int. J. Equity Health 20, 60 (2021).

Ye, P. et al. National-scale 1-km maps of hospital travel time and hospital accessibility in China. Sci. Data 11, 1130 (2024).

Du, X., Du, Y., Zhang, Y., Zhu, Y. & Yang, Y. Urban and rural disparities in general hospital accessibility within a Chinese metropolis. Sci. Rep. 14, 23359 (2024).

Huang, M. et al. Primary care quality and provider disparities in China: a standardized-patient-based study. Lancet Reg. Health West. Pac. 50, 101161 (2024).

Xu, J., Powell-Jackson, T. & Mills, A. Effectiveness of primary care gatekeeping: difference-in-differences evaluation of a pilot scheme in China. BMJ Glob. Health 5, e002792 (2020).

Huisman, L. et al. A digital mobile health platform increasing efficiency and transparency towards universal health coverage in low- and middle-income countries. Digit. Health 8, 20552076221092213 (2022).

Lucyk, K., Tang, K. & Quan, H. Barriers to data quality resulting from the process of coding health information to administrative data: a qualitative study. BMC Health Serv. Res. 17, 766 (2017).

Chernew, M. & Mintz, H. Administrative expenses in the US health care system: why so high?. JAMA 326, 1679–1680 (2021).

Yan, A. S., Apathy, N. C. & Chen, J. Adoption of health information technologies by area socioeconomic deprivation among US hospitals. JAMA Health Forum 6, e253035 (2025).

Adler-Milstein, J. & Jha, A. K. HITECH Act drove large gains in hospital electronic health record adoption. Health Affairs 36, 1416–1422 (2017).

Chishtie, J. et al. Use of epic electronic health record system for health care research: scoping review. J. Med. Int. Res. 25, e51003 (2023).

Wong, A. et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern. Med. 181, 1065–1070 (2021).

Capoot, A. Epic Systems is building more than 100 new AI features for doctors and patients. Here’s what’s coming. CNBC https://www.cnbc.com/2024/08/21/epic-systems-ugm-2024-ai-tools-in-mychart-cosmos-.html (2024).

Everson, J., Nong, P. & Richwine, C. Uptake of generative AI integrated with electronic health records in US hospitals. JAMA Netw. Open 8, e2549463 (2025).

Nong, P., Adler-Milstein, J., Apathy, N. C., Holmgren, A. J. & Everson, J. Current use and evaluation of artificial intelligence and predictive models in US hospitals. Health Affairs 44, 90–98 (2025).

Arora, R. K. et al. HealthBench: evaluating large language models towards improved human health. Preprint at https://doi.org/10.48550/arXiv.2505.08775 (2025).

Hicks, R. S. et al. HealthBench Professional: evaluating large language models on real clinician chats. Preprint at https://doi.org/10.48550/arXiv.2604.27470 (2026).

OpenAI. Making ChatGPT better for clinicians. https://openai.com/index/making-chatgpt-better-for-clinicians/ (2026).

Bulusu, P. Amazon launches Health AI agent on Amazon website and app with free 24/7 access to virtual care for Prime members. Amazon News https://www.aboutamazon.com/news/retail/amazon-health-ai-agent-one-medical/ (2026).

Businesswire. Ant Group launches major campaign against health misinformation via AI healthcare app AQ amid wider healthcare push. https://www.businesswire.com/news/home/20250818527127/en/Ant-Group-Launches-Major-Campaign-Against-Health-Misinformation-via-AI-Healthcare-App-AQ-Amid-Wider-Healthcare-Push/ (2025).

Anthropic. Advancing Claude in healthcare and the life sciences. https://www.anthropic.com/news/healthcare-life-sciences/ (2026).

Ramaswamy, A. et al. ChatGPT Health performance in a structured test of triage recommendations. Nat. Med. https://doi.org/10.1038/s41591-026-04297-7 (2026).

Center for Devices and Radiological Health. Clinical Decision Support Software. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software/ (2026).

Mello, M. M. & Cohen, I. G. Regulation of health and health care artificial intelligence. JAMA 333, 1769–1770 (2025).

Wang, M. Q. The ethics of Ant Afu: health AI within China’s super-app ecosystem. J. Med. Ethics https://blogs.bmj.com/medical-ethics/2026/01/30/the-ethics-of-ant-afu-health-ai-within-chinas-super-app-ecosystem/ (2026).

Sekalala, S. & Chatikobo, T. Colonialism in the new digital health agenda. BMJ Glob. Health 9, e014131 (2024).

Garg, S., Bebarta, K. K. & Tripathi, N. The Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) after four years of implementation - is it making an impact on quality of inpatient care and financial protection in India?. BMC Health Serv. Res. 24, 919 (2024).

Belenguer, L. AI bias: exploring discriminatory algorithmic decision-making models and the application of possible machine-centric solutions adapted from the pharmaceutical industry. AI Ethics 2, 771–787 (2022).

World Health Organization. Ethics and governance of artificial intelligence for health. https://www.who.int/publications/i/item/9789240029200/ (2021).

Pan, J. et al. COMPASS-GH is a consensus roadmap for defining standards for safe, accurate and equitable AI in general health queries. Nat. Health 1, 162–163 (2026).

Glasgow, R. E., Vogt, T. M. & Boles, S. M. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am. J. Public Health 89, 1322–1327 (1999).

Wang, H. et al. An evaluation framework for ambient digital scribing tools in clinical applications. NPJ Digit. Med. 8, 358 (2025).

Collins, G. S. et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385, e078378 (2024).

World Health Organization. Global strategy on digital health 2020–2025. https://www.who.int/publications/i/item/9789240020924/ (2021).

Greenhalgh, T. et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J. Med. Int. Res. 19, e8775 (2017).

Torabi, F. et al. A common framework for health data governance standards. Nat. Med. 30, 26–29 (2024).

Shaw, J. & Sekalala, S. Health data justice: building new norms for health data governance. NPJ Digit. Med. 6, 30 (2023).

World Bank Group. Implementation know-how brief. Data governance in health. https://documents1.worldbank.org/curated/en/099081723223522777/pdf/P175075036726f0a00832307b7ae23a06a6.pdf (2025).

China Government Website. National Internet Information Office. Interim measures for the management of generative AI services. https://www.gov.cn/zhengce/zhengceku/202307/content_6891752.htm (2023).

Omar, M. et al. Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support. Commun. Med. 5, 330 (2025).

Omiye, J. A., Lester, J. C., Spichak, S., Rotemberg, V. & Daneshjou, R. Large language models propagate race-based medicine. NPJ Digit. Med. 6, 195 (2023).

Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453 (2019).

Wu, E. et al. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat. Med. 27, 582–584 (2021).

Grundy, Q. et al. Data sharing practices of medicines related apps and the mobile ecosystem: traffic, content, and network analysis. BMJ https://doi.org/10.1136/bmj.l920 (2019).

Integration of consumer AI into healthcare pathways

Generate a cinematic news thumbnail for title:Integration of consumer AI into healthcare pathways. No text in image.

Integration of consumer AI into healthcare pathways - Nature

R1, a company that specializes in healthcare revenue cycle management, has announced its intention to acquire Humata Health, which focuses on utilizing artificial intelligence to streamline the prior authorization process. This move is aimed at enhancing efficiency in preapproval requirements that insurers impose for various medical treatments, thereby furthering R1's mission to automate payment processes for healthcare providers.

The acquisition is anticipated to be finalized in the third quarter of this year. Following the transaction, Humata’s team will work on integrating its innovative technology into R1’s existing revenue cycle platform, although specifics regarding the financial aspects of the deal were not disclosed by an R1 representative.

R1 is optimistic that this acquisition will bolster its AI capabilities, assisting healthcare providers in reducing administrative burdens and improving their financial outcomes. Operating out of Utah, R1 processes over 600 million transactions annually through a network of approximately 1,000 providers, utilizing a billing system designed to eliminate the necessity for multiple software applications.

In contrast to R1's diverse approach to claims processing, Humata Health, founded in 2023 by a physician trained at the Mayo Clinic, is dedicated solely to prior authorizations. Humata offers an AI-driven solution that recognizes insurer policies, generates prior authorization requests, and tracks them to final approval, boasting an impressive first-pass approval rate of up to 96%.

R1 CEO Joe Flanagan commented on the acquisition, stating, "Humata significantly enhances our coverage of the authorization process." The prior authorization system has long been a contentious issue within the healthcare sector, with patients expressing that the preapprovals create challenges, and providers pointing out the additional administrative tasks they entail, all while insurers argue that these authorizations are crucial for controlling costs and healthcare spending.

R1 envisions a future where real-time prior authorizations are commonplace, a vision echoed by recent federal regulations set to take effect in 2024 that will require insurers to expedite prior authorization decisions and use technology to facilitate electronic preapprovals. Additionally, during the previous summer, the Trump administration garnered commitments from insurers to ensure that at least 80% of electronic prior authorization requests receive real-time responses by next year.

Meanwhile, other technology firms are also launching solutions aimed at improving real-time prior authorizations. For instance, Epic, a leading electronic health record provider, recently introduced a tool that allows clinicians to quickly verify insurance prior authorization requirements.

R1 focuses on AI prior authorizations through acquisition of Humata.

A sunlit clinical office desk bathed in warm natural light, a heavy drill bit engraved with the R1 logo piercing through a neat stack of paper forms stamped “AI Prior Authorization,” and beside it a tablet screen glowing with the Humata logo; medical files and pens scattered around complete the scene of an acquisition driving deeper AI workflow integration.

R1 drills down on AI prior authorizations with Humata buy - Healthcare Dive

- R1 is acquiring Humata Health to enhance AI capabilities for prior authorizations.  
- The acquisition aims to streamline preapproval processes and reduce administrative burdens for providers.  
- Humata's technology boasts a 96% first-pass approval rate, improving authorization efficiency.  

Dive Brief:

Healthcare revenue cycle management company R1 has agreed to acquire Humata Health, a company specializing in artificial intelligence-powered prior authorizations.

R1 is banking that Humata’s AI expertise will help it streamline the preapproval process required by insurers for certain medical treatments, and further its goal of automating how healthcare providers get paid for their services.

The acquisition is expected to close in the third quarter this year, after which Humata’s team will start integrating its technology into R1’s revenue cycle platform. An R1 spokesperson declined to share the financial terms of the deal.

Dive Insight:

R1 says the acquisition should expand its AI capabilities while helping providers reduce paperwork and improve their bottom lines.

The Utah-based company, which processes over 600 million transactions annually across its network of 1,000 providers, says its billing platform eliminates the need for multiple disparate software tools.

While R1 has a broad focus on claims processing, Florida-based Humata Health is exclusively focused on prior authorizations.

The company, founded in 2023 by a Mayo Clinic-trained physician, offers an AI-powered product that identifies insurer policies, creates prior authorization requests and oversees them through final approval. Humata says its tool has up to a 96% first-pass approval rate on prior authorizations.

“Humata significantly enhances our coverage of the authorization process,” R1 CEO Joe Flanagan said in a statement.

The prior authorization process has long been a headache for the healthcare industry. Patients say the preapprovals are a burden, while providers say they increase administrative work and delay care. But insurers contend that prior authorizations are imperative to saving money and lowering healthcare spending.

R1 says its goal is to make real-time prior authorizations a reality. It’s a goal shared by the federal government — a rule finalized in 2024 requires insurers to speed up prior authorization decisions and implement tech to enable electronic preapprovals.

Last summer, the Trump administration secured voluntary commitments from insurers that at least 80% of electronic prior authorization approvals will be answered in real time next year.

Other IT companies are also debuting products they say help with real-time prior authorizations. Electronic health record company Epic launched a tool this week that enables clinicians to instantaneously check whether an insurer requires prior authorization.

The Need for Enhanced Data Oversight in AI Healthcare Applications

More from AI in healthcare

Assessing ROI for Healthcare AI Might Need an Alternative Strategy

Pew: Many Americans uncertain about AI in healthcare

Americans Curious About When Their Healthcare Providers Utilize AI

Americans seek transparency regarding AI usage in their healthcare.