The following states and territories in the United States, including various regions in Canada, are recognized across multiple listings: 

- Alabama
- Alaska
- Arizona
- Arkansas
- California
- Colorado
- Connecticut
- Delaware
- Florida
- Georgia
- Hawaii
- Idaho
- Illinois
- Indiana
- Iowa
- Kansas
- Kentucky
- Louisiana
- Maine
- Maryland
- Massachusetts
- Michigan
- Minnesota
- Mississippi
- Missouri
- Montana
- Nebraska
- Nevada
- New Hampshire
- New Jersey
- New Mexico
- New York
- North Carolina
- North Dakota
- Ohio
- Oklahoma
- Oregon
- Pennsylvania
- Rhode Island
- South Carolina
- South Dakota
- Tennessee
- Texas
- Utah
- Vermont
- Virginia
- Washington
- Washington D.C.
- West Virginia
- Wisconsin
- Wyoming
- Puerto Rico
- U.S. Virgin Islands
- Armed Forces Americas
- Armed Forces Pacific
- Armed Forces Europe
- Northern Mariana Islands
- Marshall Islands
- American Samoa
- Federated States of Micronesia
- Guam
- Palau

In Canada, the regions include:

- Alberta
- British Columbia
- Manitoba
- New Brunswick
- Newfoundland
- Nova Scotia
- Northwest Territories
- Nunavut
- Ontario
- Prince Edward Island
- Quebec
- Saskatchewan
- Yukon Territory

This comprehensive list highlights the various states and territories, showcasing their geographical diversity. Each location offers its unique culture and character, contributing to the rich tapestry of North American life.

Missouri law aims to safeguard patients from AI impersonating healthcare.

A sunlit clinic consultation scene: a caring doctor in a white coat sits across from an adult patient at a wooden desk; on the desk rests a leather-bound book titled “Missouri AI Healthcare Act” and a small gavel; behind them a Missouri state flag hangs gently; in the corner is a faint grey robot silhouette overlaid with a red circle-slash symbol; warm, natural light fills the room.

Missouri law will serve patients and protect them from AI posing as healthcare - Columbia Missourian

- Several states are considering changes to zip code systems to enhance delivery efficiency.  
- Proposed changes aim to address growing population and e-commerce demands in urban areas.  
- Experts emphasize potential benefits, including improved routing and reduced delivery times.

State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington Washington D.C. West Virginia Wisconsin Wyoming Puerto Rico US Virgin Islands Armed Forces Americas Armed Forces Pacific Armed Forces Europe Northern Mariana Islands Marshall Islands American Samoa Federated States of Micronesia Guam Palau Alberta, Canada British Columbia, Canada Manitoba, Canada New Brunswick, Canada Newfoundland, Canada Nova Scotia, Canada Northwest Territories, Canada Nunavut, Canada Ontario, Canada Prince Edward Island, Canada Quebec, Canada Saskatchewan, Canada Yukon Territory, Canada

Zip Code

Missouri law will serve patients and protect them from AI posing as healthcare

Texas A&M University is embracing a compassionate approach to artificial intelligence in healthcare, led by Dr. Mark Benden and his research team. They have developed a range of empathetic AI solutions designed to engage with patients and healthcare professionals, enhancing interactions at important moments in medical care.

The initial lineup of these AI assistants includes a medical receptionist that can communicate in over 100 languages, an assessment tool for identifying early symptoms of dementia, and a pediatric companion that provides comfort to children during stressful medical procedures. These innovative products have been licensed to a startup called Humanate, which continues to broaden the offerings.

Dr. Benden emphasizes that conversational AI has evolved significantly, capable of maintaining context, recalling relevant details, and providing timely responses. This human-like interaction is especially beneficial for individuals facing challenges such as language barriers or the anxiety that accompanies medical visits.

These pioneering applications highlight a future where AI can address persistent issues stemming from workforce shortages while ensuring patients have more meaningful interactions with their doctors, a breakthrough that contributed to Texas A&M receiving the Fast Company Innovation by Design 2026 award.

One standout development is "Oliver," a friendly, cartoon-style AI companion designed to support pediatric patients who often feel apprehensive. In many cases, parents cannot be present, so Oliver explains medical procedures in child-friendly language, demonstrates calming techniques, and remains with the child throughout their experience via tablet or display. The design focuses on warmth, clarity, and continuity, guiding children from the waiting room through recovery.

Benden notes that providing a consistent and friendly digital presence can alleviate anxiety, enhancing the child’s overall experience. Preliminary feedback suggests that maintaining a familiar and supportive voice helps keep procedures on track.

Another significant project involves a virtual interviewer developed for the early detection of dementia. This tool engages patients with structured questions while analyzing their facial expressions and other biometric data to create a nuanced "Digital Apathy Signature," helping to identify early signs of neurodegenerative conditions. This method aims to capture subtle behavioral changes that traditional checklists may overlook.

The team’s most sophisticated creation is an AI medical receptionist named "Cassie," also part of Humanate’s offerings. Cassie, displayed on a monitor or kiosk, assists patients with check-in, paperwork, and information requests. Unlike traditional kiosks, Cassie can interpret facial expressions and modify her tone and language accordingly, even integrating humor when appropriate.

Dr. Benden highlights that the goal is not to replace human staff but to create helpful partners that handle routine tasks, allowing healthcare professionals to concentrate on patient care. By improving the check-in process and offering multilingual support—including American Sign Language—Cassie aims to alleviate front desk congestion and enhance the patient experience.

Currently being piloted in Texas and Mississippi, the AI receptionist has already garnered positive feedback, particularly from older adults who have interacted with it, demonstrating a strong appeal across diverse age groups.

The development of these technologies comes at a time when healthcare systems are grappling with ongoing staffing shortages, making the need for efficient, supportive tools more pressing. Dr. Benden envisions these AI innovations as valuable support systems that streamline processes while ensuring secure and effective communication, escalating issues to human staff whenever necessary.

"The ultimate aim is to make these tools seamlessly integrate into healthcare settings," Benden states. "Patients should notice an improved experience, feeling heard and understood while spending more quality time with their clinicians." The design team is committed to refining these solutions based on real-world feedback, ensuring that technology truly enhances the human element of healthcare rather than detracting from it.

The efforts of agentic AI to improve a failing healthcare system – Texas A&M Stories

A bright hospital corridor flooded with warm natural sunlight, where a sleek white service robot with touchscreen interface gently wheels a patient toward a treatment room. A doctor in scrubs stands nearby tapping data on a wall-mounted tablet showing patient vitals and AI-generated care plans. In the background, a tasteful Texas A&M pennant hangs on the wall, symbolizing the research driving this agentic AI revolution in healthcare.

How agentic AI is working to fix a broken healthcare system - stories.tamu.edu

- Texas A&M has developed empathetic AI assistants to enhance patient and clinician interactions.  
- “Oliver,” a pediatric companion, supports children during medical procedures with comforting explanations.  
- An AI medical receptionist named "Cassie" is designed to ease patient check-in processes efficiently.  

Texas A&M University is taking an empathetic, human approach to artificial intelligence.

Dr. Mark Benden and his team have created a portfolio of agentic AI designed to converse, read facial expressions, capture biometrics and more, to support patients and clinicians at critical points in care.

Their early suite of empathetic AI assistants includes a medical receptionist that greets patients in more than 100 languages; an assessment tool aimed at detecting early signs of dementia; and a pediatric companion that helps children feel safe during intimidating procedures. These products have been licensed by Texas A&M to a startup company, Humanate, which is regularly expanding the portfolio.

“Conversational AI has matured to the point where it can follow context, remember relevant details and respond appropriately in real time,” Benden said. “Marrying that capability with a relatable on‑screen presence makes the interaction feel less transactional and more like a conversation — especially for people who struggle with forms, language barriers or the stress of the moment.”

Together, these pilots point to a future where AI fills persistent gaps caused by workforce shortages and time pressures while giving people more time with their doctors. It’s breakthroughs like this that earned Texas A&M the Fast Company Innovation by Design 2026 award.

Friendly guide for pediatric patients

“Oliver” is a cartoon-style AI companion that reaches patients who often need the most reassurance: children undergoing medical tests and procedures.

In many settings, parents can’t be in the room. Oliver explains what’s happening in kid‑friendly terms, demonstrates breathing and distraction techniques, and stays with the child throughout the experience on a tablet, wall display or mobile device. The team’s prototype focuses on warmth, clarity and continuity, acting as a steady guide from waiting room to recovery.

“If the digital guide is friendly and calm, explains each step and celebrates their bravery, the whole experience changes,” said Benden, a professor in the School of Public Health and director of Center for Worker Health.

Early design sessions with Oliver suggest that a familiar face and consistent voice reduce confusion and help techs and nurses keep the procedure on track.

Early dementia detection

Behind Oliver’s welcoming smile is a broader strategy: apply the same digital‑human interface to thornier problems, where nuance greatly matters. One of those is earlier detection of dementia.

Benden’s group has developed a virtual interviewer that asks structured screening questions while observing facial expressions, noting response times, and capturing biometrics using a patented computer mouse design to create a more objective risk profile for apathy, a known early indicator of neurodegenerative disease. The goal is a “Digital Apathy Signature” sensitive enough to pick up subtle changes over time and practical enough to use at scale in primary care, where precious minutes are limited.

“Dementia detection has relied on checklists and self‑reports for decades, and they’re helpful. But they miss the quiet, early changes,” Benden said. “A digital human can stay perfectly consistent, measure micro‑behaviors we can’t capture in a busy clinic, and flag patterns clinicians want to explore sooner.”

AI medical receptionist

The team’s most advanced prototype is an AI medical receptionist, also marketed by Humanate.

“Cassie” can appear on a computer monitor or kiosk, guiding visitors through check‑in, paperwork and records requests. Unlike a button‑based kiosk, Cassie recognizes facial cues, adjusts tone and language for each patient, and can even crack a friendly joke or adopt a serious cadence when the topic calls for it.

“We’re not building gadgets to replace people,” Benden said. “We’re building approachable teammates that take on repetitive tasks, so the humans on the team can focus on patient care. When the technology is used, patients should feel less rushed and more heard from the moment they walk in.”

Cassie’s multilingual ability (including American Sign Language) and 24/7 availability are aimed at easing front‑desk bottlenecks and freeing staff to focus on complex, human‑only tasks.

The technology is currently being tested at several places in Texas and Mississippi, where staff have dubbed it “Rita.”



Benden notes positive reactions to Rita from patients so far. “We were especially surprised that the most glowing reviews came from older adults,” he said.

This is applied research: we build, deploy, listen and adjust. The north star is simple — technology that makes healthcare feel more human, not less. Dr. Mark Benden Professor School of Public Health

Finding solutions to a healthcare crisis

Health systems face chronic staffing shortages that stretch front desks and care teams alike. The design team envisions agentic AI as gap‑fillers, not replacements for humans, engineered to pass along clean, secure information and escalate to a person whenever the situation warrants it.

“If we do our job right, these tools disappear into the background,” Benden said. “What patients notice is that they got in faster, felt understood and spent more of their visit with their clinician instead of juggling clipboards.”

Design choices are being refined by the team as prototypes roll out in real environments, with feedback loops from patients, clinicians and administrators. “This is applied research: we build, deploy, listen and adjust,” Benden said. “The north star is simple — technology that makes healthcare feel more human, not less.”

How agentic AI is working to fix a broken healthcare system – Texas A&M Stories

AI is rapidly transforming the landscape of healthcare, particularly in administrative functions, as organizations face ongoing financial strain and challenges in recruiting and retaining skilled employees. The integration of AI across various aspects of the healthcare employment lifecycle holds promise, but must be approached with caution to avoid legal pitfalls, maintain employee trust, and safeguard the quality of patient care.

In a complex and demanding environment, healthcare organizations are grappling with workforce shortages, escalating labor costs, and regulatory pressures while striving to improve patient outcomes and ensure access to quality care. AI technologies are increasingly being employed in diverse areas, from diagnostic imaging and clinical documentation to scheduling, revenue cycle management, and patient communication. Generative AI is particularly useful in everyday tasks, enabling healthcare professionals to draft communications, summarize records, and manage information more effectively—tasks that previously required significant time and effort.

As the use of AI expands within the healthcare sector, leaders face challenges that extend beyond merely selecting appropriate technologies. Each implementation must be carefully considered against the backdrop of governance, compliance, accountability, transparency, and trust, particularly in a field where decisions carry direct implications for both operations and patient welfare.

While much discussion surrounding AI in healthcare focuses on its clinical applications—such as early disease detection and treatment support—the administrative benefits are gaining ground. AI is streamlining administrative functions by automating tasks that previously demanded extensive hours from staff. Applications range from ambient documentation tools that facilitate clinical note-taking to predictive analytics that forecast patient volumes and staffing needs. AI-enhanced scheduling platforms optimize workforce allocations, and chatbots handle routine patient queries, alleviating pressure on call centers.

Despite these administrative efficiencies, healthcare organizations must navigate potential legal and organizational challenges associated with new technologies. It’s critical to assess not only the effectiveness of AI tools but also their alignment with regulatory requirements, internal guidelines, and professional standards.

In the realm of workforce management, healthcare employers are deploying AI at every stage, including recruitment, hiring, performance evaluation, and training. When carefully integrated, these tools can improve operational efficiency and optimize resource allocation. However, without adequate oversight, they risk generating significant legal liabilities and undermining employee confidence, ultimately impacting patient care quality.

In recruitment, AI can streamline candidate selection, but organizations must remain vigilant regarding the evaluations these systems generate and any potential biases. AI-driven scheduling solutions may enhance workforce management, yet employers must adhere to labor laws, provide necessary accommodations, and fulfill union contracts. Additionally, AI tools aimed at monitoring productivity can yield beneficial insights but also prompt valid concerns about privacy and transparency.

The integration of AI poses familiar workplace issues in new forms. As governmental agencies implement guidelines and states legislate automated decision-making in employment, healthcare organizations must adapt to an evolving regulatory framework that mirrors technological advancements.

Human judgment remains a crucial component in healthcare, where professionals rely on their training to make informed decisions. While AI can assist, it cannot supplant human judgment. Organizations should prioritize employee training to foster understanding of AI's capabilities and limitations, emphasizing the importance of when human oversight is required and clarifying policies governing the use of AI-generated content.

Effective AI governance is essential for successful implementation, focusing not just on technology but also on creating robust frameworks that encompass legal, compliance, privacy, HR, information security, procurement, and clinical perspectives. Each area provides valuable insight into risk management and supports responsible AI use.

A proactive governance approach begins with addressing essential questions before deploying new technologies: What data does the AI system utilize? How is that data protected? Are there safeguards in place for patient and employee data? Clear internal policies should delineate acceptable AI uses, approval channels for new technologies, and guidelines for human oversight and accountability.

Strong governance not only mitigates risks but also fosters innovation, allowing organizations to adopt new technologies with greater assurance. 

As we look toward the future, AI will enhance the healthcare sector through numerous incremental advancements. The success of these technologies will depend on organizations recognizing AI not merely as software but as an overarching business strategy that permeates patient care, operational efficiency, workforce management, and regulatory compliance.

Ultimately, the future of AI in healthcare hinges on trust. Organizations will need to cultivate trust among employees, patients, and regulators by implementing AI thoughtfully, ensuring transparent communication, and maintaining appropriate human oversight. Those who successfully build this trust will be better equipped to leverage AI’s benefits while navigating the accompanying legal and workplace challenges generated by technological innovation.

AI in Healthcare: Advancements, Changes in Workforce, and Future Directions

A bright, modern hospital ward flooded with natural morning light. In the foreground, a doctor and a nurse examine a large transparent touchscreen table displaying a rotating 3D heart model alongside real-time patient vitals and flowing data streams. Beside them sits a sleek, white AI console with soft LED accents, its screen showing predictive analytics. In the background, a small team of healthcare professionals gathers around a digital whiteboard outlining workflow transformation pathways, symbolizing innovation and the path forward in AI-powered medicine.

AI in Healthcare: Innovation, Workforce Transformation + the Path Forward - Jackson Lewis

- AI's primary near-term impact in healthcare is streamlining administrative tasks amid workforce shortages.  
- Effective implementation of AI requires oversight to prevent legal issues and maintain employee trust.  
- Organizations must integrate AI as a business strategy, emphasizing governance and transparency for success.  

Takeaways

AI’s greatest near-term impact in many healthcare organizations may be in administration, particularly as financial pressures persist and experienced employees continue to be difficult to recruit and retain in the industry.

Healthcare employers are increasingly integrating AI across the employment lifecycle, but when implemented without appropriate oversight, these AI approaches can create significant legal exposure, erode employee trust and ultimately affect the quality of patient care.

Success will go to those organizations that can earn the trust of their employees, patients and regulators by implementing AI thoughtfully, communicating transparently and maintaining appropriate human oversight.

Article

Artificial intelligence (AI) has emerged as a promising force in healthcare, offering a significant potential tool to help healthcare professionals work more efficiently and make better-informed decisions.

Healthcare organizations are navigating a complex and demanding environment. Persistent workforce shortages across clinical and administrative roles, widespread clinician burnout, escalating labor costs and increasing regulatory scrutiny are placing significant strain on these organizations. Despite these challenges, healthcare organizations are expected to deliver better patient outcomes while preserving quality or access to care.

Today, AI is being used to assist with everything from diagnostic imaging and clinical documentation to scheduling, revenue cycle management, claims processing, patient communications and research. Generative AI, in particular, is becoming an integral part of daily workflows, helping healthcare professionals and administrators draft communications, summarize records and organize information more efficiently. Technologies that seemed experimental in the healthcare industry not long ago are helping complete tasks that once required hours of manual effort in a fraction of the time.

As AI becomes more widely used in the healthcare industry, leaders must navigate challenges that go well beyond selecting the right technology. Each new implementation requires thoughtful consideration of governance, compliance, transparency, accountability and trust. These issues are particularly complex in an industry where decisions directly affect not only business operations but patient care and a workforce whose expertise is indispensable for quality care.

AI Is Changing More Than Clinical Care

Much of the public conversation about AI in healthcare focuses on its clinical potential:

Identifying disease earlier;

Supporting treatment decisions; and

Accelerating drug discovery.

Although primary to a healthcare organization’s mission, these developments do not tell the whole story.

In many healthcare organizations, AI’s greatest near-term impact may be in administration. Administrative tasks that once consume significant staff hours are increasingly being automated or augmented through AI technologies. Ambient documentation tools streamline clinical notetaking. Predictive analytics help anticipate patient volumes and staffing requirements. AI-powered scheduling platforms align workforce levels with fluctuating demand. Customer service chatbots answer routine patient inquiries before they reach a call center.

These efficiencies are particularly appealing in an industry where financial pressures persist and experienced employees are difficult to recruit and retain. However, every operational improvement brings new legal and organizational considerations. Healthcare organizations must assess not only an AI’s tool effectiveness, but also whether its use aligns with existing regulatory requirements, internal policies and professional standards.

Workforce Is at Center of AI Conversation

Healthcare employers are increasingly integrating AI across the employment lifecycle, from recruitment and hiring to scheduling, performance management, training and employee support. When implemented thoughtfully, these technologies can streamline administrative processes and help organizations allocate scarce resources more effectively. When implemented without appropriate oversight, they can create significant legal exposure, erode employee trust and ultimately affect the quality of patient care.

Consider the hiring process. AI-assisted screening tools can help employers identify qualified candidates more efficiently, but organizations must understand how these systems evaluate applicants and whether they produce unintended bias. Similarly, AI-powered scheduling software may improve workforce allocation, but healthcare employers remain responsible for complying with wage and hour requirements, providing reasonable accommodations for employees with disabilities, where appropriate, and honoring contractual obligations in unionized workplaces.

Similarly, AI tools designed to measure productivity and monitor workplace activity can offer valuable operational insights. At the same time, they raise important concerns about employee privacy, transparency and the extent to which automated decision-making systems should influence personnel matters.

These are not entirely new employment issues. Rather, AI changes how familiar legal obligations manifest and how employers must address them. As federal agencies issue additional guidance and states adopt laws governing automated decision-making and employment-related AI, healthcare employers can expect the regulatory landscape to evolve alongside the technology itself.

Perhaps just as important as the technology is human judgment. Healthcare professionals are trained to exercise independent judgment. Although AI can support that judgment, it cannot replace it. Organizations implementing AI into clinical or administrative workflows should invest in training that helps employees understand AI tools’ capabilities and limitations. Employees need clear guidance on when AI can serve as a useful tool, when human oversight is essential and how organizational policies govern the use of AI-generated content.

Governance Becoming a Competitive Advantage

The success of AI implementation depends less on the technology itself than on the quality of the governance framework that guides its use.

For many healthcare organizations, this means recognizing that AI is more than an information technology initiative. Effective implementation increasingly requires collaboration among legal, compliance, privacy, human resources, information security, procurement and clinical leaders. Each stakeholder brings a different perspective on risk and plays a critical role in promoting the responsible use of AI.

An effective AI governance program begins with asking the right questions before new technologies are implemented. Organizations should understand what data an AI system uses, how that information is stored and protected, whether patient or employee data may be used to train underlying models, and what contractual safeguards are in place if issues arise. Clear internal policies should also establish acceptable uses of AI, approval processes for new technologies, and expectations for employee or human oversight and accountability.

Good governance is not a barrier to innovation; it is a framework that enables organizations to embrace new technologies with greater confidence while minimum legal and operational risks.

Looking Ahead

AI’s impact on the healthcare industry will be driven by countless incremental improvements that collectively reshape how healthcare organizations operate, how clinicians deliver care and how employees do their jobs.

Success will belong to organizations that move beyond seeing AI as simply another software purchase and instead embrace it as a business strategy that touches every part of the enterprise, from patient care and operational efficiency to workforce management and regulatory compliance.

For healthcare employers, the future of AI is ultimately a matter of trust. Success will also depend on whether organizations can earn the trust of their employees, patients and regulators by implementing AI thoughtfully, communicating transparently and maintaining appropriate human oversight. Those that are trusted will be better positioned to capture AI’s benefits while managing the legal and workplace challenges that inevitably come with innovation.

AI in Healthcare: Innovation, Workforce Transformation + the Path Forward

The healthcare landscape is undergoing a significant transformation with the integration of artificial intelligence (AI) into various aspects of patient care, making previous discussions about potential developments feel outdated by 2026. AI is now actively embedded throughout the patient journey, assisting in early disease detection, locating patients lost within complex healthcare systems, forecasting health declines, supporting treatment choices, and alleviating some of the operational burdens in care management.

The critical question has shifted from whether AI will see application in healthcare to how effectively it can enhance patient outcomes and if healthcare organizations can keep pace with its adoption.

1. **Prediction and Diagnosis**

Innovative strides in healthcare AI are primarily evident even before formal diagnoses are established. Researchers are recognizing valuable clinical signals within existing data collected by healthcare systems for various unrelated purposes.

AI is proving beneficial in analyzing routine ECG data. An AI tool has been shown to identify indicators for heart failure and valvular diseases from standard ECGs in less than two seconds, successfully detecting up to 81% of heart failure cases and 90% of valve disease cases across around 67,000 patients in the U.S. Rather than replacing diagnostic processes, this approach could enhance triage, facilitating quicker access to echocardiograms and specialist evaluations.

AI has also been utilized to extract significant information regarding high blood pressure and stroke risks from mammograms. In a study involving nearly 100,000 mammograms from approximately 30,000 women, researchers found that existing screening frameworks could serve as vital tools for identifying cardiovascular risks.

Cooperation between Yale, Cleveland Clinic, and IBM led to the analysis of over 10,000 overnight sleep studies, revealing connections between sleep patterns and future health risks. The study suggested that certain high-risk groups could face double the mortality rate compared to lower-risk cohorts.

A promising urine test supported by AI for detecting pancreatic cancer is currently under review in Japan by a cohort from Nagoya University. This initiative exemplifies the broader trend of leveraging simple biological samples to identify complex conditions.

In the UK, researchers have established an innovative AI model that detects bladder cancer patterns within routine primary care records, successfully identifying 85% of cases a year in advance.

Swedish researchers have created FRACTURE-ML, an AI model that, utilizing health information from 3.5 million individuals, identifies nearly seven times more individuals at risk of hip fracture compared to conventional methods.

At Stanford, investigations show that language patterns in children's speech may correlate with future risks of anxiety and depression, providing predictive indicators as far out as six years.

Furthermore, Sanius Health is partnering with an undisclosed pharmaceutical company to pioneer an AI initiative aimed at identifying patients with rare and ultra-rare diseases earlier by analyzing real-world healthcare data and deciphering patient journeys that mimic rare disease characteristics.

2. **Treatment and Care**

AI is rapidly advancing beyond diagnosis into the realms of treatment and ongoing patient care, emphasizing personalized approaches that consider individual medical histories and biological data to anticipate future health events.

University of Surrey researchers delved into the recovery pathways of approximately 12,700 individuals post-heart attack, uncovering distinct trajectories, one of which had a 44% mortality rate. This information could provide a critical advantage in targeting high-risk patients for heightened follow-up care.

At Indiana University, researchers designed a six-point AI scoring system to forecast the risk of dangerous bleeding into the heart after severe heart attacks, achieving over 84% accuracy and clarity in contributing factors for these predictions.

A digital twin platform for lung cancer, developed by Hong Kong Polytechnic University, integrates genomic, imaging, and clinical data, reportedly achieving 82.5% accuracy in predicting responses to immunotherapy by reflecting changes in the patient's condition.

Pangaea Data utilizes AI to scan clinical notes and detect patients who may be untreated or inadequately treated, tapping into unstructured data that traditional healthcare databases often overlook.

3. **Generative AI’s Evolution Beyond Documentation**

Generative AI's initial major use case in healthcare involved documentation, but the landscape is broadening rapidly. The industry is evolving from AI that simply records to AI that automates portions of the workflow itself.

Assort Health employs an AI referral system that automates 87% of referral processes and successfully converts 79% into scheduled appointments while often recommending a more appropriate specialist.

Abridge is expanding its capabilities beyond clinical notes to provide decision support across numerous health systems, offering evidence during clinical interactions rather than just generating post-visit summaries.

Oracle Health is enhancing its patient portal with conversational AI features, enabling patients to query their health data in straightforward terms. The company's clinician-focused tools also include functions for coding and clinical workflow assistance.

Suki has introduced a clinical dictation tool that uses AI to streamline documentation processes, enabling clinicians to manage notes via voice commands before transferring information into systems like Epic and Meditech.

Hippocratic AI has established a supervisory mechanism designed to oversee multiple healthcare AI agents, claiming a significant 250 million patient interactions without safety incidents, highlighting the growing importance of governance in AI deployment.

4. **Adoption and Governance Challenges**

The central obstacle in the AI healthcare sector may be organizational rather than technical, as rapid adoption often outpaces regulatory governance.

A recent survey of 1,000 NHS staff revealed that 90% were utilizing AI in their clinical roles, with 65% having developed their own AI usage strategies before official employer guidance was provided.

Healthwatch England has raised concerns that AI-generated clinical documentation can introduce errors, resulting in inaccuracies in patient records, such as misreported medications and diagnoses.

In response to these issues, the FDA is actively soliciting public feedback on regulatory frameworks for generative AI medical devices, grappling with how to govern tools that do not conform neatly to existing regulatory models.

Public sentiment is increasingly leaning towards transparency in healthcare AI, with patients expecting clarity about when and how AI is involved in their care.

The Future of Healthcare AI

The advancement of healthcare AI signifies not just a selection of models or technologies but a transformative shift in where intelligence is interwoven within the patient care continuum. 

From early diagnosis detection to managing fragmented records, influencing clinical interactions, aiding treatment decisions, and streamlining administrative tasks, AI's impact spans across entire patient populations.

For prevalent diseases, this may lead to earlier interventions, while healthcare systems could benefit from reduced administrative burdens. For clinicians, it enhances access to pertinent information, and for life sciences companies, it promises improvements in how patient populations are identified.

In cases of rare diseases, AI's role could be particularly transformative, enabling early identification of interconnected symptoms before healthcare systems can recognize patterns. The key to success will lie in identifying not just the most effective systems but those capable of guiding healthcare to recognize and address patient needs swiftly and efficiently.

AI and Healthcare Research: Latest Developments in Medical Innovation

A modern hospital ward bathed in warm natural sunlight: a female physician leans over a sleek touchscreen console displaying AI‐generated 3D organ scans, vital‐sign graphs, and diagnostic suggestions. An adult patient reclines in the bed, relaxed and reassured by the bright, open‐window view and the fusion of human care with cutting‐edge AI insights.

AI & AI Research Health News: What’s Advancing in Healthcare - distilledpost.com

- AI is transforming healthcare by enhancing diagnosis, treatment, and patient management processes.  
- Innovative AI applications include detecting diseases from routine tests, speeding up patient identification.  
- Adoption of AI in healthcare is rapid, outpacing governance and raising accuracy and transparency concerns.  

For years, the healthcare AI conversation has been dominated by what might be possible. In 2026, that conversation feels increasingly outdated.

AI is now being deployed across the patient journey, finding disease earlier, identifying patients who have disappeared inside fragmented health systems, predicting deterioration, guiding treatment decisions and increasingly taking on parts of the operational workload surrounding care.

The more important question is no longer whether AI will be used in healthcare. It is where AI genuinely changes an outcome, and whether healthcare systems can govern adoption quickly enough.

1. Prediction & Diagnosis

Some of the most compelling developments in healthcare AI are happening before a diagnosis is ever made. Researchers are increasingly finding clinically useful signals inside data that healthcare systems were already collecting for completely different reasons.

AI from routine ECGs

An AI system has demonstrated the ability to identify signs associated with heart failure and valve disease from routine ECG data in under two seconds. Across approximately 67,000 US patients, the system identified up to 81% of heart failure cases and 90% of valve disease cases.

The potential value is not necessarily replacing diagnosis. It is triage. If routinely collected ECGs can identify people who warrant further investigation, AI could help patients reach echocardiography or specialist review much earlier.

Mammograms could reveal cardiovascular risk

Researchers have shown that AI can extract information about high blood pressure, coronary artery disease and stroke risk from routine mammograms. The work involved almost 100,000 images from around 30,000 women. Existing screening infrastructure could therefore become another route for identifying cardiovascular risk.

Yale, Cleveland Clinic & IBM: sleep as a risk signal

Researchers involving Yale, Cleveland Clinic and IBM have reanalysed more than 10,000 overnight sleep studies. Their models identified associations with five-year mortality risk, cardiovascular disease and cognitive decline. The highest-risk population reportedly experienced approximately twice the mortality of the lowest-risk group.

Nagoya University: urine testing for pancreatic cancer

A Nagoya University spin-off is seeking Japanese approval for an AI-supported urine test designed to detect pancreatic cancer. Validation remains important, but the wider trend is significant: AI is increasingly being combined with relatively simple biological samples to identify complex disease.

AI detecting bladder cancer from GP records

UK researchers have developed an AI model that searches routine primary care records for patterns associated with bladder cancer. Using approximately 70,000 Welsh patient records, the system reportedly identifies 85% of cases reliably a year ahead and signals as much as five years before diagnosis.

FRACTURE-ML: identifying hip fracture risk

Swedish researchers developed FRACTURE-ML using health information from approximately 3.5 million people. The system reportedly identified nearly seven times more people at risk of hip fracture within two years than conventional Swedish screening approaches, using illnesses, medicines and basic records rather than requiring additional scans or appointments.

Stanford: language patterns and future mental health risk

Stanford researchers have reported that the way children construct sentences may contain signals associated with anxiety and depression years later. The model focused less on what children were discussing and more on patterns in how language was structured, with predictive signals reported as far as six years ahead.

Sanius Health: finding rare disease patients earlier

Rare and ultra-rare disease represents an especially important opportunity for AI-enabled patient identification. Sanius Health is working with an undisclosed large pharmaceutical company on an AI-enabled Rare Disease Patient Find programme designed to identify people who may have rare and ultra-rare conditions earlier.

The approach analyses signals across real-world healthcare data to identify patient journeys that may resemble rare disease phenotypes, including cases where the disease has not yet been formally coded or diagnosed.

For patients with rare disease, diagnostic delay is not simply inconvenient. It can mean years of uncertainty, repeated appointments, inappropriate treatment and delayed access to specialist care.

The potential role of AI is therefore much bigger than search. It is about recognising patterns across fragmented patient journeys that individual clinicians or individual healthcare organisations may never see in one place.

Finding the patient healthcare has not yet realised it is looking for.

2. Treatment & Care

AI is also moving rapidly downstream from diagnosis into treatment and ongoing care. The opportunity here is personalisation: using individual trajectories, records and biological data to understand what may happen next.

University of Surrey: understanding recovery after heart attack

University of Surrey researchers analysed approximately 12,700 UK Biobank participants following heart attacks and identified three distinct recovery trajectories across five years. One group, strongly associated with smoking, reportedly experienced a 44% mortality rate, creating an opportunity to identify much earlier which patients may require more intensive follow-up.

Indiana University: predicting dangerous bleeding

Indiana University researchers developed an explainable six-point AI score designed to predict dangerous bleeding into heart muscle following severe heart attacks. The model reportedly achieved more than 84% accuracy and was designed to show which factors contributed to the prediction rather than simply providing an unexplained risk score.

Digital twins for lung cancer

Hong Kong Polytechnic University has developed a digital twin platform for lung cancer that combines genomics, imaging, clinical records and treatment information. The system reportedly achieved 82.5% accuracy in predicting response to immunotherapy during testing. The more important concept is the creation of a digital representation of a patient that changes as the real patient changes.

Pangaea Data: finding untreated patients

Pangaea Data is using AI to analyse clinical notes and identify patients who may be untreated or under-treated despite lacking conventional structured diagnosis codes. This addresses a basic weakness of healthcare databases: important information can sit inside discharge letters, specialist notes, historical records and referral documentation while remaining invisible to traditional database searches.

3. Generative AI Is Leaving the Scribe Behind

The first major clinical use case for generative AI was documentation. That was probably only the opening act. The market is now moving from AI that records work towards AI that performs parts of the workflow itself.

Assort Health: AI managing referrals

Assort Health has deployed an AI referral agent which reportedly automates 87% of referral workflows and converts 79% into booked appointments. It also reportedly identifies a more suitable specialist than the initial referral choice in approximately half of cases.

Abridge: beyond clinical notes

Abridge has expanded from AI-generated clinical documentation into decision support across approximately 300 health systems. The system can surface evidence during clinical encounters rather than simply producing notes afterwards.

Documentation → Decision Support → Workflow Automation

Oracle Health: conversational health records

Oracle Health has introduced AI functionality into its patient portal that allows patients to ask plain-language questions about their own health information. The company has also expanded clinician-facing capabilities including coding, chart review, dictation and clinical workflow support.

Suki: clinicians talking directly to the EHR

Suki has introduced AI-powered clinical dictation alongside its ambient documentation technology. Clinicians can dictate and edit clinical notes using voice before moving information into platforms including Epic and Meditech. Healthcare professionals may increasingly interact with software by speaking rather than clicking.

Hippocratic AI: AI supervising AI

Hippocratic AI has launched a supervisory layer designed to coordinate multiple healthcare AI agents. The company has claimed approximately 250 million patient interactions without safety incidents in production. As AI systems move towards this model, governance becomes considerably more important.

4. Adoption & Governance

Healthcare’s biggest AI challenge may ultimately be organisational rather than technical. Adoption is happening faster than governance.

NHS AI adoption

A survey of 1,000 NHS staff cited in the source material reported that 90% were already using AI in clinical work. More strikingly, 65% reportedly developed their own way of using AI before their employer issued formal guidance.

Healthwatch England: AI errors in clinical records

Healthwatch England has warned that AI-generated clinical documentation can introduce incorrect information into patient records, including incorrect medicines and diagnoses. In one reported case, wording relating to an MRI result was materially changed by AI transcription.

FDA: regulation is catching up

The FDA is seeking public input on how generative AI medical devices should be regulated, with a proposed risk framework under consideration and consultation running until 19 October. Conventional medical-device regulation was largely designed around systems that behaved predictably after approval; generative AI may not fit that model neatly.

Patients increasingly want transparency

Pew Research Center has reported strong public demand for transparency around healthcare AI. Trust may therefore become as important as accuracy: patients may accept extensive AI involvement in healthcare, but increasingly expect to know when it is happening.

The Next Phase of Healthcare AI

The most important development in healthcare AI is not any single model, company or algorithm. It is the change in where intelligence sits within the healthcare pathway.

🔮 Before diagnosis

🔍 Across fragmented records

🩺 During clinical encounters

🧬 Inside treatment decisions

📞 Between appointments

🤖 Across administrative workflows

📊 Across entire patient populations

For common diseases, this could mean earlier intervention. For overstretched health systems, it could mean removing significant administrative friction. For clinicians, it could mean having relevant information surfaced before they have to search for it. For life sciences companies, it could transform how appropriate patient populations are identified.

And for rare disease patients, the impact could be much more profound. Instead of spending years waiting for healthcare to connect a collection of seemingly unrelated symptoms, AI may increasingly be capable of identifying the pattern first.

The winners will not simply be the systems capable of generating the best answers. They will be the systems capable of helping healthcare find the right patient, understand them earlier and move them towards the right care faster.

Source note: Prepared from the supplied September 2026 AI and healthcare research news digest. Company performance figures and research findings are described as reported in those source materials.

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AI & AI Research Health News: What’s Advancing in Healthcare

Facebook has introduced a new feature designed to enhance user experience and engagement on its platform. This update aims to make interactions more meaningful by allowing users to connect more deeply with their friends and interests. With a focus on fostering community, the platform is rolling out tools that encourage users to share their thoughts and experiences more freely. 

Additionally, the changes include an improved interface that makes it easier to navigate and discover content that aligns with users' passions. By leveraging advanced algorithms, Facebook will curate personalized feeds, ensuring that relevant posts and updates are front and center. 

This initiative also underscores Facebook's commitment to user privacy and data protection. The new features are developed with transparency in mind, empowering users to manage their settings confidently and decide what to share, with whom, and when.

Overall, these enhancements reflect Facebook's ongoing effort to adapt to the evolving needs of its community, promoting richer interactions while maintaining a safe and user-friendly environment.

Worries over AI in healthcare, Addressing conspiracies: PD Letters

A shadowy clinic office at night: a physician’s hands hold an open envelope stamped “PD Letters.” The desk is strewn with typed pages scrawled with red “Conspiracy” marks and doodled warning symbols. In the corner, a silent AI diagnostic unit looms like a dark machine, its cables snaking like vines. A single warm desk lamp illuminates the letters, while the rest of the room fades into deep shadows.

Concern about AI in healthcare, Calling out conspiracies: PD Letters - The Press Democrat

- Facebook introduces new privacy controls to enhance user data protection and transparency.
- Users can now manage ad preferences directly from their profiles for better customization.
- The platform aims to improve user trust and safety amid increasing regulatory scrutiny.

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Concern about AI in healthcare, Calling out conspiracies: PD Letters

David Shaywitz, an Adjunct Fellow, shared insights on the role of artificial intelligence in healthcare during his appearance on NewsNation’s podcast, ‘Hot Take with Jesse Weber.’ He explored the reliability of AI technology when it comes to making crucial medical decisions, weighing the potentials and limitations it presents within the healthcare sector. Shaywitz’s discussion sheds light on whether AI can be trusted to play a significant role in patient care and clinical outcomes.

Shaywitz Talks about AI in Healthcare on NewsNation's 'Hot Take with Jesse Weber' Podcast

A bright podcast studio bathed in warm natural light, with Dr. Shaywitz and host Jesse Weber seated at a wooden table, speaking into vintage-style microphones. On Dr. Shaywitz’s laptop screen, a softly glowing neural-network graphic in amber tones hints at AI in medicine. Behind them, a large NewsNation logo is prominently displayed on a textured wall, and subtle medical symbols—a stethoscope and caduceus—are integrated into the décor.

Discussing AI in Healthcare: Shaywitz on NewsNation's 'Hot Take with Jesse Weber' Podcast - American Enterprise Institute - AEI

- David Shaywitz explores the reliability of AI technology in making medical decisions.  
- He discusses the potential benefits AI brings to healthcare decision-making processes.  
- The podcast delves into concerns surrounding AI's effectiveness in critical situations.  

Adjunct Fellow David Shaywitz discusses AI in healthcare and if the technology is reliable enough to make critical medical decisions on NewsNation’s ‘Hot Take with Jesse Weber’ Podcast.

Discussing AI in Healthcare: Shaywitz on NewsNation’s ‘Hot Take with Jesse Weber’ Podcast

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NEHEN: Looking Past the Hype: Integrating AI in Healthcare

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NEHEN: Beyond the Hype: Implementing AI Across Healthcare - Foley Hoag

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NEHEN: Beyond the Hype: Implementing AI Across Healthcare

In a recent analysis, JPMorgan's Rajiv Batra has expressed a positive outlook on the performance of emerging-market benchmarks, suggesting that they demonstrate reduced sensitivity to interest rate fluctuations. He emphasizes that these markets are increasingly aligned with sectors such as artificial intelligence (AI) and healthcare. Batra underlines his continued optimism regarding AI investments, even in the face of challenges related to safety, regulatory scrutiny, and financing issues. His insights suggest a strategic shift in how investors might approach these sectors, advocating for the potential benefits of integrating AI and healthcare into their portfolios despite the prevailing economic uncertainties.

JPMorgan: Emerging market benchmarks are less sensitive to interest rates, prefer AI and healthcare sectors

A bright, modern trading floor bathed in warm morning sunlight through floor-to-ceiling windows. In the center, a large JPMorgan logo is etched on a glass pillar. Below it, a sleek digital ticker shows emerging-market benchmarks flattening as two rising charts glow: one stylized with a neural-network motif, the other bearing a subtle medical caduceus. In the corner, an interest-rate gauge sits nearly idle, its needle barely moved—visually narrating the move toward AI and healthcare.

JPMorgan: EM benchmarks less rate-sensitive, favor AI and healthcare - CNBC

- JPMorgan's Rajiv Batra highlights emerging-market benchmarks' resilience to rate changes.  
- He believes equities and higher bond yields can thrive simultaneously.  
- Batra remains optimistic about AI and healthcare despite associated risks.

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JPMorgan: EM benchmarks less rate-sensitive, favor AI and healthcare

JPMorgan’s Rajiv Batra says higher bond yields and equities can coexist, arguing that emerging-market benchmarks are less rate-sensitive and more geared toward AI services and healthcare. He explains why remains bullish on AI despite safety, regulatory and financing concerns.

JPMorgan: EM benchmarks less rate-sensitive, favor AI and healthcare

Suki, a leading platform in Ambient Clinical Intelligence (ACI), has joined forces with MedStar Health’s National Center for Human Factors in Healthcare to embark on a groundbreaking research initiative focused on the integration of ambient AI in healthcare delivery. This partnership aims to delve into the effects of this technology, marking a significant advancement in understanding how ambient AI can reshape clinical environments.

Historically, the healthcare sector has concentrated on accelerating the adoption of new technologies. However, many organizations have overlooked the critical science of human factors essential for assessing the success of technology within intricate clinical settings. This collaboration will leverage MedStar Health’s extensive research on the application of AI in patient care while applying comprehensive implementation science principles to introduce Suki's technology across various MedStar Health facilities. The initiative seeks to fill this knowledge gap and enhance the practical effectiveness of AI tools in healthcare.

The research agenda will focus on several vital areas crucial for achieving clinical and operational success:

1. **Facilitating Adoption**: Recognizing that the uptake of ambient AI varies widely among healthcare professionals and organizations, experts in human factors from MedStar Health will design and evaluate interventions aimed at creating a scalable, replicable model for increasing adoption across the sector.

2. **Enhancing the Human Experience**: The study will explore how ambient AI impacts not just clinicians but also trainees and patients. Researchers will assess its influence on administrative workloads, clinical decision-making, and the overall experience of patients and providers in diverse healthcare settings.

3. **Ensuring Patient Safety and Quality of Care**: Part of the investigation will focus on how ambient AI integrates with other clinical workflows, with an emphasis on maintaining safety and care quality while enhancing the experiences of both clinicians and patients.

As health systems transition from small-scale pilot programs to broader implementations, the demand for robust, evidence-based insights is more critical than ever. Through its collaboration with MedStar’s National Center for Human Factors, Suki is making a significant investment in comprehensive research to ascertain how ambient AI can be integrated effectively and safely within complex clinical environments.

Sudha Jayaraman, MD, MSc, FACS, who leads this initiative at Suki, emphasizes the importance of collaboration among clinicians, informaticists, and implementation specialists to ensure that the development and application of ambient AI is undertaken thoughtfully. This sentiment is echoed by Joshua Biro, PhD, the principal investigator for the partnership, who highlights the essential role of rigorous evaluation in actualizing the transformative potential of ambient AI. Biro states that the collaboration seeks to ensure these tools support healthcare professionals in their daily routines while enhancing patient care.

According to Raj Ratwani, PhD, director of the National Center for Human Factors, the successful design and deployment of ambient AI hinges on a human-centered approach. The partnership with Suki is dedicated to creating tools that alleviate burdens on healthcare providers, improve patient engagement, and deliver significant advantages across diverse patient demographics.

The overarching aim of this collaboration is to elevate industry standards for the evaluation and integration of ambient AI into clinical practice. By uniting Suki's expertise in ACI with MedStar Health’s deep-rooted knowledge in safety and human factors, they aspire to set new benchmarks for the way ambient AI technologies are assessed and embraced within the healthcare landscape.

Employing a mixed-methods research approach, the partnership will blend quantitative and qualitative methods to investigate the adoption and performance of ambient AI in real-world clinical scenarios. The insights derived from this research are expected to be published in scientific journals and disseminated throughout the healthcare community, contributing to the shared knowledge base in this evolving field.

Suki and MedStar Health's National Center for Human Factors in Healthcare Collaborate to Advance Ambient AI Adoption Science

A sunlit, state-of-the-art hospital control room where two adult healthcare professionals in crisp white lab coats examine an ambient AI interface seamlessly built into the wall—displaying floating patient vitals, voice waveform cues, and human-centered workflow icons. Warm natural light pours through floor-to-ceiling windows, casting optimistic shadows. On a frosted glass partition behind them, the Suki logo and MedStar Health emblem stand side by side, symbolizing their pioneering partnership.

Suki and MedStar Health’s National Center for Human Factors in Healthcare Partner to Pioneer the Science of Ambient AI Adoption | Newswise - Newswise

- Suki and MedStar Health are collaborating to research ambient AI's impact on healthcare.  
- The partnership aims to improve adoption and integration of AI in clinical environments.  
- Research will evaluate AI's effects on patient care, clinician experience, and care quality.  

Suki and MedStar Health’s National Center for Human Factors in Healthcare Partner to Pioneer the Science of Ambient AI Adoption Source Newsroom: MedStar Health

Newswise — WASHINGTON, D.C. & REDWOOD CITY, CA — Suki, the leading Ambient Clinical Intelligence (ACI) platform, and MedStar Health’s National Center for Human Factors in Healthcare are announcing a strategic research collaboration. This partnership aims to study the impact of ambient AI on healthcare delivery.

The "Science of Use": Moving Beyond Surface Metrics

While the industry has focused on rapid adoption, few institutions use the science of human factors to understand technology success in complex clinical environments. By working with the National Center for Human Factors’s industry-leading research on the use of AI in patient care, this collaboration applies rigorous implementation science to the rollout of Suki across MedStar Health clinical sites to bridge this knowledge gap.

Strategic Research Pillars

The collaboration will execute a high-priority research portfolio focused on the fundamental drivers of clinical and operational success:

Designing for Adoption: With adoption of ambient AI varying significantly across clinicians and health systems, MedStar Health’s human factors experts will design and test interventions to create a replicable blueprint to scale adoption.

With adoption of ambient AI varying significantly across clinicians and health systems, MedStar Health’s human factors experts will design and test interventions to create a replicable blueprint to scale adoption. The Human Experience: Researchers will examine how ambient AI affects clinicians, trainees and patients, including its impact on administrative burden, clinical reasoning and the patient-provider experience across diverse care settings and populations.

Researchers will examine how ambient AI affects clinicians, trainees and patients, including its impact on administrative burden, clinical reasoning and the patient-provider experience across diverse care settings and populations. Patient Safety & Care Quality: Researchers will evaluate how ambient AI performs within clinical workflows, with a focus on safety, care quality and the experience of clinicians and patients.

Why This Matters Now

As health systems move from pilot programs to scaling implementation, the need for credible, rigorous real-world evidence is increasingly important. By partnering with MedStar Health’s National Center for Human Factors, Suki is investing in rigorous research to better understand how ambient AI can be safely and effectively implemented in complex clinical environments.

As Sudha Jayaraman, MD, MSc, FACS, who leads the effort at Suki, explains, "Through the Suki Research Collaborative, we're bringing together clinicians, informaticists, and implementation scientists to ensure that ambient AI is developed, tested, implemented, and evaluated thoughtfully and responsibly."

“Realizing the transformational potential of ambient AI requires thoughtful, rigorous evaluation to ensure it’s implemented safely and effectively at all health systems,” said Joshua Biro, PhD, principal investigator for this partnership and research scientist for the MedStar Health National Center for Human Factors in Healthcare. “By combining human factors research with Suki’s innovation, we can make sure these tools genuinely support clinicians in their daily work and improve the care patients receive.”

“Ambient AI has the potential to fundamentally reshape the patient-provider experience, but only if it is designed and deployed with people at the center,” said Raj Ratwani, PhD, director of the National Center for Human Factors in Healthcare and vice president of Scientific Affairs for MedStar Health Research Institute. “Our work with Suki focuses on ensuring these tools reduce burden without compromising clinical thinking, enhance patient engagement, and deliver benefits across all patient populations.”

A Vision for Standardized Excellence

The goal of this partnership is to move the industry higher up the evidence pyramid. By combining Suki’s expertise in Ambient Clinical Intelligence with MedStar Health’s legacy in safety and human factors, the collaboration will help advance the standards for how ambient AI is evaluated and integrated into clinical care.

The Methods & Milestones

The collaboration will use mixed-methods research, combining quantitative and qualitative approaches to study how ambient AI is adopted and performs in real-world clinical environments. Findings from the research are expected to be submitted for scientific publication and shared with the broader healthcare community.

Suki and MedStar Health’s National Center for Human Factors in Healthcare Partner to Pioneer the Science of Ambient AI Adoption

Dive Brief:

Healthcare organizations are rapidly adopting agentic artificial intelligence, but governance frameworks for managing the technology are not evolving at the same pace, according to a new survey from digital identity security firm Imprivata.

More than 85% of leaders responsible for AI strategy at healthcare organizations say they’re confident they have visibility into AI agent activity and are able to fully control and govern an autonomous agent’s actions.

However, 72% of respondents admit AI tools are deployed without IT approval at least occasionally within their organizations, highlighting a disconnect between confidence and reality.

Dive Insight:

Healthcare organizations are embracing agentic AI while simultaneously struggling with fundamental governance and oversight challenges, according to the report.

Agentic AI are AI systems that can act largely independently, making complex multi-step decisions and solving problems with minimal human intervention. The healthcare sector has invested heavily in agentic AI, especially for back office use cases such as prior authorization and revenue cycle management automation.

More than a quarter of leaders have already implemented agentic AI at their organizations, according to market research firm Vanson Bourne, which conducted the survey on Imprivata’s behalf. An additional 44% are piloting or conducting proof-of-concept projects and another 21% plan to implement it in the next year.

The vast majority of respondents expect agentic AI to have a “transformative” impact on clinical and operational workflows.

However, agentic AI presents newfound security risks — especially as use cases expand, giving agents access to a wider variety of internal systems, experts say. That’s because, unlike traditional software that relies on user input to act, agents can act autonomously, traversing different information systems and executing actions on behalf of users.

“If an agent has excessive permissions, operates outside its intended scope, or takes a high-risk action without appropriate oversight, the consequences can directly impact care delivery,” said Dr. Sean Kelly, chief medical and growth officer and senior vice president of customer strategy at Imprivata.

“An agent could access or expose sensitive patient information, enter incorrect information into a medical record, alter a medication or dosage or act under a clinician’s authority in a way that the clinician never intended. Because agents can operate autonomously and at machine speed, a single error could also propagate before someone recognizes what is happening,” Kelly said.

More than half of respondents ranked security among their top concerns when adopting agentic AI. Although leaders expressed confidence in their ability to govern AI agents, instances of shadow AI, when employees use AI tools that haven’t been authorized by their companies, are still rampant.

According to a separate survey by Wolters Kluwer, 40% of medical workers and administrators said they were aware of colleagues using unauthorized AI tools, while nearly 20% reported they have used an unsanctioned tool themselves.

Imprivata’s survey shows that organizations are taking fragmented approaches to managing AI agents. Sometimes information technology departments centrally manage the tools, while security teams manage others. Some organizations report ad hoc or unapproved deployments.

And healthcare leaders are still ironing out the specifics of AI governance and how much humans need to be in the loop when an agent is making decisions, according to the research.

That process varies widely based on where the agent is deployed — whether in an administrative, operational or clinical setting.

“Oversight must match the level of clinical risk. Agents need clearly defined identities, permissions, and boundaries, with human review for higher-risk activities and an audit trail for accountability,” Kelly said. “The more autonomy we give these systems, the more important those safeguards become to detect and contain problems before they create broader operational or patient safety risks.”

The healthcare industry has long sounded the alarm about potential patient safety risks posed by improperly implemented AI. ECRI, a nonprofit focused on healthcare safety and quality, named insufficient governance of AI in healthcare as one of its top 10 patient safety concerns last year.

Various industry consortia have issued guidance to alleviate these concerns. The Coalition for Health AI, a network of thousands of healthcare systems and industry groups, issued AI governance playbooks last year to help health systems responsibly and safely roll out AI tools.

Healthcare’s agentic AI boom is outpacing governance: report

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Healthcare’s agentic AI boom is outpacing governance: report - Healthcare Dive

Illinois State University faculty and student researchers are helping drive the future of healthcare innovation through a newly recognized partnership with Healthy Tray, an Illinois-based digital health startup that is transforming how hospitals measure and manage patient nutrition.

Healthy Tray recently received an Illinois Innovation Voucher from the Illinois Science & Technology Coalition (ISTC), a competitive award designed to connect innovative Illinois companies with the state’s leading research institutions. The award will support the continued development and validation of Healthy Tray’s artificial intelligence-powered food imaging platform through a collaborative effort involving Illinois State University and OSF HealthCare St. Francis Medical Center.

At Illinois State, the project is led by Dr. Julie Schumacher from the Department of Family and Consumer Sciences and Dr. Abdelmounaam Rezgui from the School of Information Technology. The University is collaborating with Healthy Tray on a project titled “An AI-Powered Technology for Accurate Nutrition Intake.”

The technology aims to improve the accuracy and efficiency of nutrition monitoring in healthcare settings. By leveraging artificial intelligence and food imaging technology, the platform is designed to provide real-time insights into patient food consumption, helping healthcare providers better understand nutritional intake and make more informed decisions about patient care.

The Illinois Innovation Voucher award underscores the important role Illinois State University plays in connecting research expertise with industry partners to solve real-world challenges. Through the project, Illinois State faculty and student researchers will contribute their knowledge and technical skills while gaining valuable experience working on an emerging healthcare technology with the potential for widespread clinical impact.

“Healthy Tray is honored and grateful to the Illinois Science & Technology Coalition, Illinois State University, and OSF HealthCare St. Francis Medical Center for their academic and clinical partnership and shared commitment to advancing healthcare innovation,” said Michele Peplinski, founder and CEO of Healthy Tray. “This award is an exciting milestone that demonstrates the power of bringing together entrepreneurial vision, academic research, and clinical expertise to address real-world healthcare challenges. Together, we are working to redefine how nutrition is measured and managed in hospitals, positioning real-time nutrition data as essential to patient care and advancing the future of Food Is Medicine in Illinois and beyond.”

Partnerships such as this demonstrate the University’s commitment to innovation, entrepreneurship, and applied research. By working alongside industry leaders and healthcare professionals, Illinois State researchers are helping accelerate the translation of new ideas into practical solutions that benefit communities throughout Illinois and beyond.

The collaboration also highlights the power of bringing together diverse expertise. Healthy Tray provides entrepreneurial vision and technological innovation, Illinois State University contributes research and academic excellence, and OSF HealthCare St. Francis Medical Center offers clinical expertise and real-world healthcare insight. Together, these partners are working to advance a technology that could redefine how nutrition is measured and managed in hospitals.

As the project moves forward, the ISTC award serves as both recognition of the team’s accomplishments and an investment in the future of healthcare innovation. It reflects the growing impact of Illinois State University faculty and student researchers who continue to play a vital role in developing solutions that address complex challenges and improve lives through research, collaboration, and discovery.

Illinois State University partnership advances AI innovation in healthcare

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Missouri law aims to safeguard patients from AI impersonating healthcare.

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The efforts of agentic AI to improve a failing healthcare system – Texas A&M Stories

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Worries over AI in healthcare, Addressing conspiracies: PD Letters