Andrew Dudum, the co-founder and CEO of Hims & Hers, recently appeared on 'Squawk Box' to share insights on several pressing topics related to healthcare innovation and affordability. He highlighted the company's significant investment in artificial intelligence and its potential to revolutionize the healthcare landscape. Dudum also touched upon the strategic pivot the company is taking regarding GLP-1 medications, addressing the ongoing Federal Trade Commission lawsuit, and discussing strategies aimed at reducing healthcare costs. Through these initiatives, Hims & Hers seeks to enhance accessibility and affordability in the healthcare sector.

Hims & Hers CEO discusses AI's role in healthcare, GLP-1 medications, and addressing healthcare costs.

A bright, sunlit modern conference room with floor-to-ceiling windows. A confident executive stands at a sleek glass lectern, the Hims & Hers logo prominently behind him on a white wall. To his right, a large digital touchscreen shows an AI neural-network graphic seamlessly morphing into a 3D molecular model labeled “GLP-1.” On a nearby table, open folders stamped with dollar-sign icons and a small pill bottle hint at plans to make treatments more affordable. The CEO gestures passionately as reporters sit before him, notebooks open.

Hims & Hers CEO on AI in healthcare, GLP-1 drugs and tackling healthcare affordability - CNBC

- Hims & Hers CEO Andrew Dudum discusses AI's transformative potential in healthcare.  
- Company is making a strategic shift regarding GLP-1 drugs for weight management.  
- Dudum addresses healthcare affordability and the impact of an FTC lawsuit on operations.  

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Hims & Hers CEO on AI in healthcare, GLP-1 drugs and tackling healthcare affordability

Hims & Hers co-founder and CEO Andrew Dudum joins 'Squawk Box' to discuss the company's AI investment, how AI will transform healthcare, strategic shift on GLP-1s, FTC lawsuit, bringing down the cost of healthcare, and more.

Hims & Hers CEO on AI in healthcare, GLP-1 drugs and tackling healthcare affordability

A recent study conducted by Cotiviti in collaboration with MedCity News indicates that the primary challenge surrounding artificial intelligence in healthcare has shifted from adoption to governance. As AI becomes integrated into various operational facets—clinical, administrative, and financial—healthcare executives are now prioritizing security, governance, and accountability to ensure responsible usage. The 2026 Healthcare AI Readiness Index collected insights from 70 healthcare leaders during the summer of 2026.

The findings reveal that over 70% of participants have started implementing AI tools, with nearly 40% of health insurers identifying AI as a fundamental component of their operations. However, healthcare providers appear to be at an earlier adoption stage, with more than 70% still navigating the initial phases of AI integration.

Despite the advancement in AI application, the necessary governance frameworks are still in the works. Only 42% of payers and 32% of providers express feeling “very prepared” to tackle AI-related cyber threats. Additionally, a significant portion of the workforce is engaging with unauthorized or non-integrated "shadow AI" tools, with 60% of payers and 64% of providers reporting such incidents. Alarmingly, less than 40% have established comprehensive policies to oversee employee use of AI technologies.

Ric Sinclair, CEO of Cotiviti, emphasized the urgent need for enhanced security and governance as AI becomes a staple in healthcare infrastructure. He stated, “While AI is increasingly embedded in critical workflows, the measures for safety and oversight must evolve equally fast. It’s crucial for health plans and organizations to ensure that the technology they leverage and their strategic partners uphold the highest standards for security, governance, and responsible usage. Building trust will be essential for fully unlocking AI’s potential in the healthcare sector.”

The Index not only assesses the current adoption levels but also investigates how healthcare leaders are constructing the operational frameworks essential for the ethical use of AI.

Survey Shows Rapid AI Adoption in Healthcare, Yet Readiness Lags

Modern hospital ward bathed in warm natural light from tall windows. In the foreground, two faceless medical professionals in scrubs study a wall‐mounted digital dashboard: a green bar graph labeled “AI Adoption” soars skyward, while beside it a round readiness gauge is pegged deep red. On a stainless steel cart, a sleek AI diagnostic robot arm, its surface etched with circuit patterns, holds a stethoscope as if offering it to the staff. The scene feels hopeful yet tense, underscoring rapid AI growth amid lagging preparedness.

Survey Reveals AI Adoption is Accelerating in Healthcare, But Readiness is Not - MedCity News

- Over 70% of healthcare leaders have started using AI tools for operations.  
- Only 42% of payers and 32% of providers are prepared for AI-related cyberattacks.  
- Many employees utilize unauthorized "shadow AI" tools, indicating weak governance policies.  

A new report from Cotiviti and MedCity News suggests healthcare’s biggest AI challenge is no longer adoption. As AI becomes embedded in clinical, administrative and financial workflows, healthcare leaders are increasingly focused on the governance, security and accountability needed to use it responsibly. The 2026 Healthcare AI Readiness Index surveyed 70 healthcare leaders over the summer of 2026.

More than 70% of respondents said they have begun using AI tools, with nearly 40% of health insurers describing AI as a core aspect of their business. Providers, however, are earlier in the process, with more than 70% still in the early stages of adoption.

Yet the systems needed to govern AI use are still developing. Only 42% of payers and 32% of providers report being “very prepared” to respond to AI-assisted cyberattacks. More than half of payers (60%) and 64% of providers report employees are using unauthorized or non-integrated “shadow AI” tools, while fewer than 40% have detailed policies governing employee use of AI.

“AI is quickly becoming part of the infrastructure of healthcare, but the security and governance around it have to advance just as quickly,” said Ric Sinclair, CEO of Cotiviti. “As health plans and healthcare organizations rely on AI across more critical workflows, they need confidence that the technology and partners throughout their ecosystems meet the same high standards for security, governance and responsible use. Trust will be foundational to realizing AI’s full potential in healthcare.”

Beyond measuring adoption rates, the Index explores how healthcare leaders are building the operational foundations required for responsible AI use.

To access the 2026 Healthcare AI Readiness Index, please fill in the form below:

Survey Reveals AI Adoption is Accelerating in Healthcare, But Readiness is Not

As the integration of artificial intelligence in healthcare accelerates, discussions are evolving from merely assessing AI’s technical capabilities to evaluating the preparedness of organizations as a whole. While many healthcare institutions acknowledge AI's transformative potential, several are still struggling with essential data management practices that enable effective implementation, scaling, and operational functionality.

Transitioning from isolated AI applications to comprehensive growth necessitates a solid base of reliable, integrated, and actionable data. The core issue often lies not with the AI technologies themselves but with the ability to utilize data confidently across diverse platforms and areas—ranging from clinical applications to financial assessments and performance metrics.

Widening the focus beyond just data quality

Historically, the discourse around AI in healthcare has centered on data quality, an undeniably vital aspect of the technology. Flawed, incomplete, or inconsistent data can lead to inaccurate AI outcomes and negatively affect patient care. While healthcare organizations have dedicated substantial resources over the years to enhance data quality, this focus alone is no longer sufficient—it is now a fundamental requirement.

To effectively scale AI initiatives, organizations must consider several key factors:

1. **Robust Governance**: Establishing a well-defined framework that outlines the necessary processes, roles, standards, responsibilities, and metrics associated with the AI tools and models, along with the input data that drives them.

2. **Interoperability**: Given the fragmented and siloed nature of healthcare data, organizations must implement advanced tools that integrate various data sources into a singular, reliable resource for AI models to utilize before making critical clinical decisions.

3. **Data Lineage**: Trust in AI-generated results hinges on the ability to trace the origin of data, understand its transformation during integration, and confirm it as a reliable source for operational and clinical scenarios.

4. **Transparency**: The algorithms and data that inform AI outputs often operate in a “black box” environment, which can jeopardize trust as AI applications become more widespread. Ensuring visibility into the data and processes behind AI insights diminishes error risk, fosters confidence, and helps protect patient and organizational interests.

5. **Detectability**: Organizations must be equipped with tools that can identify and resolve data discrepancies early on, preventing any negative impact on financial or clinical decisions.

Failing to establish these essential pillars may cause AI to exacerbate existing data issues rather than provide substantial benefits. By implementing these structures, organizations can confidently adopt and scale AI across various departments, ultimately enhancing patient care.

The implications for healthcare AI are substantial

With the rise of AI across numerous sectors, discussions surrounding safety and effectiveness are gaining momentum. This is particularly critical in healthcare, where the deployment of AI tools holds far greater stakes than in other fields, as decisions made can have life-or-death consequences for patients. The implications of AI also extend to regulatory compliance and can either reinforce or challenge the fundamental trust between patients, providers, and healthcare institutions striving to deliver high-quality care.

As the utilization of AI advances in healthcare, those entities lacking a robust data framework risk encountering significant and damaging repercussions.

Impact on clinical efficacy

Utilizing AI models that rely on incomplete or fragmented data can lead to subpar clinical decision-making and compromise patient safety. It is imperative that the data used is both comprehensive and precise. Furthermore, AI solutions developed using generic open-source large language models (LLMs) typically fall short in clinical environments since they must be specifically tailored to interpret complex medical language and facilitate nuanced, individualized care provisions. Even in administrative contexts, AI tools must be trained on targeted medical terminologies and billing systems to minimize errors and enhance performance.

Importance of context

Understanding the specific challenges that users aim to address with AI is crucial, and these needs can vary widely among stakeholders. The requirements of a clinically integrated network of providers diverge significantly from those of a self-funded employer seeking different insights. Within a single organization, various users will demand distinct outputs and insights from AI to tackle clinical, financial, and operational issues. For an AI tool to deliver relevant insights, it must be capable of accounting for the unique circumstances surrounding each user's inquiry.

Data security challenges

Healthcare is one of the most regulated industries, where patient privacy is paramount, and violations of the Health Insurance Portability and Accountability Act (HIPAA) can lead to severe penalties. Consequently, AI tools must be designed with robust security measures, particularly those using open-source LLMs, to prevent unauthorized access to sensitive health information (PHI). A strong data foundation is essential to ensure both HIPAA compliance and inherent security within AI systems.

In addition to compliance, organizations face challenges in ensuring their AI models are supported by a substantial and secure data source for learning and refinement. Utilizing open-source LLMs for training can heighten the potential for data breaches, making it critical for healthcare providers to strike a balance between innovation and data security through a purpose-built architecture. Such infrastructure secures data and ensures user interactions are contained within a protected framework, allowing AI to refine its insights over time while safeguarding sensitive information.

Emphasizing outcomes over mere outputs

When healthcare payers, providers, and self-funded employers prioritize a mature, AI-optimized data environment, they enhance their capacity to fulfill vital components of value-based care—such as population health, care management, quality improvement, cost control, and collaborative efforts. This foundation also positions organizations to leverage AI for informed decision-making at scale, where the integration of detailed individual insights alongside wider population trends becomes a critical advantage, reliant on dependable, transparent, and interconnected data throughout the organization.

Ultimately, the future of AI in healthcare hinges not just on an organization's ability to manage data, but also on its commitment to building a foundational data framework that fosters trust, visibility, and operational proficiency. By establishing AI on a secure, compliant, and clinically-informed basis, organizations can transform their data into actionable insights and scalable AI capabilities, propelling advancements in healthcare delivery.

Transforming disjointed healthcare information into smart solutions in the era of AI

A sunlit modern hospital office with natural light pouring through large windows. On a sleek desk lie torn patient charts and floating translucent data panels. A doctor in scrubs watches as an ethereal network of glowing lines emerges from a slim tablet, weaving the fragments into a unified interface. In the background, a wall-mounted monitor displays stable vital signs, symbolizing AI-driven intelligent action in healthcare.

From fragmented healthcare data to intelligent action in the age of AI - Healthcare Dive

- Healthcare organizations recognize AI potential but often lack necessary foundational data management capabilities.  
- Effective AI implementation requires strong governance, interoperability, data lineage, transparency, and detectability.  
- A mature, AI-ready data foundation supports value-based care and enables scalable decision-making.  

As AI adoption accelerates across healthcare, the conversation is increasingly shifting from AI model capabilities to broader organizational readiness. Most healthcare organizations recognize the potential of AI, but many still lack the foundational data management capabilities to effectively implement, scale and operationalize that potential.

Moving from early AI use cases and segmented solutions to strategic growth requires a foundation of trusted, connected, actionable data. This challenge is often less about the AI tools themselves and more about whether you can confidently leverage your data across multiple platforms and domains—from clinical implementation and support to financial and performance measurement.

Priorities must expand beyond data quality

The healthcare AI conversation has long focused on data quality because it is a critical component of this evolving technology. Inaccurate, incomplete, or inconsistent data inevitably leads to flawed AI outputs and poor patient outcomes. But healthcare organizations have spent years investing in data quality, so that alone is no longer a differentiator—it’s table stakes.

Organizations looking to scale AI still need high-quality data, but taking the next step now requires:

Strong governance: A structured, clearly defined framework that includes all the processes, roles, standards, responsibilities and metrics for the AI tools and models in use, as well as the data that feeds into those systems.

Interoperability: Healthcare data is notoriously fragmented and siloed, so organizations must have advanced tools that bring all the disparate sources together into a single, trusted, connected source for AI models to evaluate before making treatment decisions or clinical recommendations.

Data lineage: Before trusting AI outputs, healthcare organizations must be able to track where data originated, how it was transformed in the process of bringing it together and whether it remains a trustworthy source for operational and clinical use cases.

Transparency: The data and calculations underlying AI outputs are often a “black box,” purposefully opaque to maintain a competitive edge, or protect proprietary or sensitive information. But that opacity won’t work as AI use expands in healthcare. The ability to see all the underlying data and validate insights with explainable calculations reduces the risk of errors, improves trust and can prevent patient or organizational harm.

Detectability: Organizations must have tools that detect and resolve data issues early, before they impact any financial or clinical decisions.

Without these critical five pillars, AI may amplify existing data challenges rather than create meaningful value. With them, organizations have the confidence necessary to implement AI at any level and scale it across entire teams, divisions and organizations to improve care.

The stakes are higher for healthcare AI

As AI proliferates in many industries, critical conversations around efficacy and safety are emerging. Nowhere is that more important than in healthcare. Deploying an AI tool in a healthcare setting is not the same as optimizing an e-commerce funnel or streamlining shipping logistics—the stakes are much higher when AI informs decisions that could literally be life and death for a patient. AI in healthcare also affects regulatory compliance and can reinforce or destroy the core trust between patients, providers and the healthcare systems that are supposed to provide exceptional care.

As AI-enabled healthcare grows, organizations that lack the data infrastructure to support it could encounter serious and detrimental consequences.

Clinical consequences

When an AI model only has access to fragmented and incomplete data, the underlying information it’s using to make decisions is flawed. That can directly impact clinical decisions and patient safety, so precise and complete data must be non-negotiable.

Additionally, AI tools built and trained on generic open-source large language models (LLMs) will almost always fail in a clinical setting. Healthcare AI tools must have training to analyze and decipher complex medical terminology and make very nuanced and personalized care delivery decisions. Even when AI is utilized for healthcare administration, the tools must be trained on industry-specific terminology and billing codes to avoid errors and optimize performance.

Context matters

Context is critical to understand the specific problem that a user is trying to solve with AI—and that varies by stakeholder. The insights and interventions a clinically integrated network of providers needs from their AI tools are vastly different from the information a self-funded employer is seeking. Even within a single organization, different users will need different AI outputs and insights to solve clinical, financial and operational challenges. For a healthcare AI tool to provide actionable and appropriate insights, it must be able to understand the nuances specific to each user’s situation and align its outputs accordingly.

Data security

Few industries in the world are as highly regulated as healthcare. Patient data is personal and protected and HIPAA violations lead to strict penalties and financial liabilities. AI tools that are not properly secured, particularly those using or connected to open-source LLMs, can introduce another opportunity for bad actors to access protected health information (PHI). Thus, the data foundation underlying AI tools must have HIPAA compliance and security architected in from the start.

HIPAA compliance is also not the only AI-related challenge healthcare organizations face. AI models need a robust data source from which to learn and train and a secure feedback loop to help improve insights, workflows and ultimately outcomes. But using open-source LLMs to train and improve AI models increases the risk of data breaches.

Balancing data security with innovation requires a purpose-built architecture that ensures data and interactions are not shared outside an organization’s secure data infrastructure and user prompts or insights are never co-mingled with other organizations. Proper architecture allows AI to learn—generating better insights over time and increasing productivity—without introducing unnecessary risk to the underlying data. It also enables users to develop proprietary AI that is unique to their organization.

Focus on outcomes over outputs

When healthcare payers, providers and self-funded employers invest in a mature, AI-ready data foundation, they can support all the critical aspects of value-based care: population health, care management, quality improvements, cost reduction and collaborative care. They are also better positioned to support AI-enabled decision-making at scale. The ability to combine granular member-level insights with broader population trends will be a significant differentiator, but it’s only possible when the underlying data is reliable, trustworthy, governed and connected across your entire enterprise.

The defining principle for healthcare’s AI future is not whether organizations can manage data. It is whether they are committed to building the necessary data foundation to enhance trust, visibility and operational readiness. AI built on a secure, compliant and clinically-aware foundation will enable users to transform data into actionable intelligence and scalable AI capabilities.

From fragmented healthcare data to intelligent action in the age of AI

The collaboration with AI is not a recent development, yet the rise of foundation models marks a new chapter in the evolution of large language models (LLMs) capable of reasoning. Evidence is mounting that these advanced models can carry out clinical tasks with competence akin to that of human professionals.

India is progressing towards an AI-ready healthcare system, bolstered by governmental support, a vibrant start-up landscape, increased adoption of electronic medical records (EMRs), private investment, and a growing acceptance of AI technologies among clinicians.

Presently, the value generated from AI applications is largely focused on a limited number of use cases, such as workflow automation and ambient scribing. These initiatives aim to enhance efficiency and patient experience, although demonstrating a clear return on investment (ROI) for businesses in the short term proves challenging.

Despite noticeable demand, various promising AI applications still show significant potential due to the wide gaps existing in the market. Opportunities abound in areas like remote patient monitoring, optimization of operating rooms and intensive care units (ICUs), management of chronic diseases following hospital discharge, and claims processing—making these attractive prospects for AI-focused startups.

Nevertheless, the readiness to implement these technologies is inconsistent. Key obstacles such as data quality, effective workflow integration, clinician trust, and managing change hinder large-scale adoption. Successful start-ups typically possess a strong understanding of the healthcare sector combined with robust AI capabilities.

For those interested in further insights, a comprehensive report is available for download (PDF). 

HealthQuad, one of the foremost investment platforms in India focusing on innovative healthcare models, is actively investing from its HealthQuad Fund III. The organization continues the successful strategy and track record established in its previous funds. Founded in 2016, HealthQuad manages assets exceeding US$500 million and operates as the early-growth investment branch of Quadria Group, a prominent healthcare investment firm in Asia. HealthQuad supports outstanding founders who are creating category-defining companies in sectors such as HealthTech, MedTech, Bio/PharmaTech, and innovative care delivery, leveraging deep industry knowledge and operational capabilities to foster growth both in India and on a global scale.

Artificial Intelligence in Healthcare Services in India

A sunlit Indian clinic room where a doctor in a crisp white coat leans over a sleek tablet glowing with colorful neural-network overlays of a patient’s vitals. The adult patient sits calmly on the examination table as soft natural light streams through a nearby window, illuminating charts on the wall and a small Indian flag discreetly displayed. Subtle digital data pulses seem to flow from the tablet into the room, symbolizing AI’s life-saving reach in rural and urban healthcare alike.

AI in Indian Healthcare Delivery - Bain & Company

- The emergence of reasoning LLMs demonstrates human-level competence in clinical tasks.  
- India's AI-ready healthcare infrastructure is enhancing through government and start-up initiatives.  
- Barriers to AI implementation include data quality, clinician trust, and integration challenges. 

Written in collaboration with Written in collaboration with AI is not new, but foundation models are: The agentic era of reasoning large language models (LLMs) is emerging, with mounting evidence that clinical tasks can be performed at a human level of competence.





India’s healthcare infrastructure is becoming AI-ready, supported by government initiatives and a thriving start-up ecosystem, as well as rising electronic medical record (EMR) penetration, private capital, and clinician acceptance.





In AI deployments, value is currently concentrated in a narrow set of use cases (e.g., workflow automation and ambient scribing, among others) directed toward efficiency and patient experience gains while business ROI remains difficult to demonstrate in near-term.





Several high-potential AI use cases present large whitespace despite clear demand—including remote patient monitoring, operation theatre and intensive care unit (ICU) optimization, post-discharge chronic disease management, and claims management—creating compelling opportunities for AI start-ups.





Implementation readiness remains uneven—data quality, workflow integration, clinician trust, and change management remain the primary barriers to scale, favoring start-ups with deep healthcare domain expertise alongside AI capabilities. Download the report (PDF) About HealthQuad HealthQuad is one of India’s leading investment platforms focused on new-age healthcare models, and is currently investing from HealthQuad Fund III, continuing the strategy and track record established across Funds I and II. Founded in 2016, HealthQuad currently has an AUM of over US$500 million, and acts the early-growth investment arm of Quadria Group, one of Asia’s leading dedicated healthcare investment platforms. HealthQuad backs exceptional founders building category-defining companies across HealthTech, MedTech, Bio/PharmaTech and novel care delivery, leveraging deep sector expertise, operating capabilities and Quadria’s extensive healthcare ecosystem to scale in India and globally.

AI in Indian Healthcare Delivery

Mercy Hospital in Springfield, Missouri, has implemented a cutting-edge program known as Aidoc that enhances how doctors assess patient care and prioritize medical issues, according to Dr. John Mohart, who serves as Mercy’s executive vice president and chief operating officer.

Transforming Diagnosis with AI Technology

Dr. Mohart emphasized that artificial intelligence is proving invaluable in the diagnostic and treatment processes at Mercy by allowing for the prioritization of clinical cases. In the past, patients arriving at the emergency room would have their imaging, such as CT scans, placed in a queue that often resulted in delays. With the introduction of AI, these images are analyzed almost instantly, enabling urgent cases to be elevated in priority for immediate care.

“This advancement significantly improves time to treatment and enhances patient outcomes, all while also identifying unexpected incidental findings in imaging,” Dr. Mohart remarked.

Previously, images like X-rays and ultrasounds would accumulate in a queue, sometimes taking days before a radiologist would assess them. In contrast, AI can analyze these images in under a minute once uploaded, rapidly flagging high-priority cases. 

“For instance, the AI can immediately identify lung clots or other critical issues and alert the radiologist to address them without delay,” he explained.

Identifying Unexpected Findings

Dr. Mohart reported that Mercy has expanded Aidoc's use across all facilities where imaging occurs, including emergency departments and outpatient centers. The AI technology has proven adept at spotting “incidental findings”—additional health concerns discovered that were not the primary reason for the scan.

Over a period of five months, Mercy identified more than 50,000 incidental findings, which might include lung nodules that warrant further investigation or coronary calcium indicative of potential heart disease, even when unrelated imaging was initially requested.

“This allows us to track and direct patients toward the most appropriate follow-up care, ensuring nothing falls through the cracks,” Dr. Mohart noted.

Ensuring Patient Privacy

Dr. Mohart assured that maintaining patient privacy is a critical focus at Mercy. Before any AI solutions are integrated into their systems, they undergo rigorous privacy and security evaluations, similar to any other component added to Mercy’s electronic health record system.

“The data is confidential and remains within our control; we treat patient records with the highest respect,” he stated.

Tackling Workforce Challenges

The integration of AI has enhanced the efficiency of radiologists, a field facing workforce shortages. The technology can handle preliminary assessments of routine images, such as chest X-rays, allowing radiologists to concentrate on more complex cases.

“If AI can enhance productivity by managing straightforward tasks, it allows healthcare professionals to devote their efforts to more critical patient interactions and advanced analyses,” Dr. Mohart explained.

He clarified that AI is designed to assist medical professionals rather than replace them. Each AI application at Mercy is evaluated based on its benefits to both patients and healthcare providers.

Advantages and Hurdles

Dr. Mohart pointed out that by minimizing wait times and Streamlining processes, healthcare providers can dedicate more time to patient care.

“We’ve demonstrated that optimizing turnaround times and increasing workflow efficiencies enables providers to engage more profoundly with patients, which is the foundation of our motivations in healthcare,” he said.

Additionally, AI can facilitate faster decision-making and enhance quality of care, while also alleviating much of the tedious administrative workload.

“Reducing mundane tasks improves both the healthcare provider’s life and greatly enhances the patient’s overall experience,” he added.

One significant challenge remains—gaining acceptance among healthcare providers that this technology can improve workflows without jeopardizing quality or patient safety.

“The key hurdle is ensuring that providers understand AI does not compromise the standards of care or safety,” Dr. Mohart concluded.

AI in healthcare: The utilization of technology by local hospitals and national initiatives

A sunlit modern hospital ward where a diverse team of doctors and a nurse lean over a tablet showing AI-driven patient vitals and predictive graphs in warm tones; behind them, a glass wall map of the country is dotted with softly glowing nodes representing national health programs, with gentle natural light flooding the scene to convey innovation in local and national healthcare AI.

AI in healthcare: How local hospitals and national programs are using the technology - KY3

- Mercy Hospital uses AI to prioritize urgent cases and improve patient treatment timelines.  
- The Aidoc system identifies over 50,000 incidental findings, ensuring better patient follow-up.  
- AI enhances radiologist efficiency, allowing them to focus on complex cases and patient communication.  

SPRINGFIELD, Mo. (KY3) - Mercy Hospital in Springfield is using a program called Aidoc to help doctors evaluate patient needs and determine what should be addressed first, according to Dr. John Mohart, Mercy’s executive vice president and chief operating officer.

How AI is changing diagnosis at Mercy

Mohart said AI has become an asset for diagnosing and treating patients by allowing Mercy to prioritize clinical cases. He said that in the past, when a patient came into the emergency room and received imaging such as a CT scan, the images went into a waiting queue. Now, AI reads the images instantaneously and can escalate care to the top of the list if the case is urgent.

“So it’s absolutely impacting time to treatment and quality outcomes, as well as finding incidental findings and other findings on the images,” Mohart said.

Mohart said that when images such as X-rays, CT scans and ultrasounds are taken, they are stored and placed in a queue for a radiologist, and some can sit for a few days, but AI reads the image in less than a minute after it is uploaded and can flag high-risk cases.

“You could find lung clots and things that you weren’t expecting. It’ll flag that instantaneously and bring it up for the radiologist with a flag saying ‘read me now’,” Mohart said.

Incidental findings

Mohart said Mercy has scaled Aidoc to every location where imaging is performed, including emergency rooms and outpatient facilities. He said the AI system identifies “incidental findings,” or additional issues found on an image beyond what a scan was originally ordered for.

Mohart said Mercy found more than 50,000 additional incidental findings over five months. He said those findings can include lung nodules that need follow-up, or coronary calcium that indicates heart disease, even when a scan was ordered for an unrelated reason.

“So we’re able to report on those and navigate patients to the next best treatment,” Mohart said. “So they’re followed up on and not lost in a file.”

Privacy and HIPAA compliance

Mohart said protecting patient privacy is a priority at Mercy, saying all AI algorithms go through extensive privacy and security review before installation, the same process used for any other attachment to Mercy’s electronic system.

“It’s private and does not get shipped out or sold to anyone data. It’s the patient’s record and we have great respect for that,” Mohart said.

Addressing doctor shortages

Mohart said AI has improved efficiency for radiologists, a field he said has a limited workforce. He said the technology allows lower-level reads, such as basic chest X-rays, to be reviewed by other physicians with an AI over-read, freeing radiologists to handle higher-level images.

“If this can make you more efficient, take care of mundane tasks that you can focus on the more important, higher level task and communication with patients, then it works for both,” Mohart said.

Mohart said physicians and providers will not be replaced by AI. He said Mercy evaluates every AI deployment by asking how it helps the patient and how it helps the provider.

Benefits and challenges

Mohart said reduced turnaround time and improved efficiency allow providers to spend more time with patients.

“We’ve shown that by reducing turnaround time and improving efficiencies, you actually can spend more time with patients, which is why we all went into medicine,” Mohart said.

He said AI can also help automate administrative work and documentation.

“It also can help with faster decisions that improve outcomes and quality, and then getting rid of some of the mundane administrative work and documentation and the care coordination,” Mohart said. “It makes the provider’s lives so much better, but also then the patient’s experience better and more reliable.”

Mohart said one of the biggest challenges is getting acceptance from providers that the technology improves workflow without compromising quality or patient safety.

“I think the biggest challenges are you have to get acceptance from the providers that this doesn’t compromise quality or patient safety,” Mohart said.

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AI in healthcare: How local hospitals and national programs are using the technology

At Mercy Hospital, artificial intelligence (AI) is making significant strides in transforming healthcare delivery and enhancing patient care while prioritizing the security of patient data. Medical professionals at this facility are leveraging AI technologies to improve diagnostic accuracy, streamline administrative tasks, and enhance personalized treatment plans.

By integrating AI into their practices, doctors are better equipped to analyze vast amounts of medical data quickly, allowing for more informed decision-making and timely interventions. These advancements help in early disease detection, enabling healthcare providers to customize treatment based on individual patient needs.

In addition to improving clinical outcomes, Mercy Hospital places a high priority on safeguarding patient information. The institution employs robust cybersecurity measures and adheres to stringent privacy regulations to ensure that sensitive health data remains confidential and secure. This dedication to protecting patient information complements their commitment to harnessing AI's potential in enhancing healthcare services.

Overall, the innovative use of artificial intelligence at Mercy Hospital is setting new benchmarks for quality care, all while maintaining a strong focus on patient privacy and data security.

AI in Healthcare: Mercy Hospital physicians utilize it while safeguarding patient data

A bright hospital ward flooded with warm morning light: two doctors in scrubs stand at a sleek, transparent touchscreen kiosk displaying patient charts overlaid with a glowing shield and padlock icon. On the wall behind them, a polished plaque reads “Mercy Hospital.” Soft medical monitors and a tidy medicine cart frame the scene, conveying advanced AI–guided care and data protection.

Artificial Intelligence in Healthcare: How doctors at Mercy Hospital use it and protect patient information - KY3

- Mercy Hospital integrates AI technology to enhance patient care and treatment efficiency.  
- Doctors utilize AI tools for diagnostics while ensuring strict patient data privacy protocols.  
- Emphasis on balancing technology use with ethical considerations in healthcare practices.

Artificial Intelligence in Healthcare: How doctors at Mercy Hospital use it and protect patient information

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Artificial Intelligence in Healthcare: How doctors at Mercy Hospital use it and protect patient information

DENVER, CO â New legislation to ensure patientsâ continued access to mental healthcare provided by a licensed human professional will go into effect on August 12.Â





âAI chatbots are being mistaken by patients as legitimate therapy, which is why weâre stepping up to ensure Coloradans are protected and informed,â said Rep. Gretchen Rydin, D-Littleton.Â âGoing into effect this month, HB26-1195 establishes reasonable protective measures on AI use in mental and behavioral health care, including prohibiting the use of AI to independently provide therapy to a patient. This law does not hamper licensed professionalsâ use of AI for administrative purposes, but does require all clinical treatment and psychotherapy to be administered by a licensed, human provider.â





âNo AI-generated algorithm can replace the expertise, nuance, and connection that human healthcare professionals utilize to treat their patients,â said Sen. Kyle Mullica, D-Thornton.Â âWith this new law, weâre establishing necessary guardrails to ensure proper access to quality care for those who need it most.â





âHuman-centered care should be at the forefront of healthcare in Colorado,â said Rep. Javier Mabrey, D-Denver.Â âAI chatbots are biased, unlicensed tools, and they should not be used for therapy and treatment recommendations without oversight and transparency. Without this law, there is no clinical standard for AI chatbots, which can ultimately harm patientsâ access to high-quality healthcare. This law protects patients while still allowing providers to take advantage of AI tools for administrative tasks.â





âAs policymakers, we cannot let chatbots, several of which are currently facing major lawsuits due to wrongful and horrifying deaths, replace certified mental health providers,â said Sen. Judy Amabile, D-Boulder.Â âSome AI models serve as bad actors claiming to offer low-cost care â but this new law puts guardrails in place to ensure patients receive the quality, human care they deserve.â





HB26-1195 Â sets standards in clinical settings, limiting the use of artificial intelligence (AI) to administrative tasks with oversight by a licensed professional. Providers must disclose the use of AI for supplementary support, such as recording or transcribing meetings. To ensure patients receive legitimate behavioral healthcare, this new law makes sure that psychotherapy is human-delivered by a licensed professional, such as a social worker, psychologist or addiction counselor.Â





To protect consumers and ensure access to quality care, this legislation will prohibit AI chatbots from being marketed to patients as equivalent to a licensed psychotherapist or counselor. AI chatbots will also be barred from implying patient input is covered by commonplace confidentiality protections like HIPAA.Â





In 2025, researchers at Stanford University recommended Â that Large Language Models (LLMs), which power AI chatbots, âshould not replace therapists.â Additionally, researchers concluded that âLLMs express stigma toward those with mental health conditions and respond inappropriately to certain common (and critical) conditions.âÂ





Top AI companies, including OpenAI, Google, and Character.AI, are all facing lawsuits from families after AI chatbots recommended suicide to a person seeking behavioral health advice or support. Last year, parents of children who died by suicide testified before Congress, stating AI chatbots discouraged their teens from seeking support.

Law to Establish Guardrails for AI in Healthcare Goes into Effect

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Law to Establish Guardrails for AI in Healthcare Goes into Effect - EIN Presswire

As Director of AI in Radiology for SSM Health St. Mary’s Hospital – St. Louis and SSM Health Saint Louis University Hospital, Dr. Michael Moritz isn’t just studying the future of artificial intelligence in healthcare, he’s helping to build it.

An assistant professor of diagnostic radiology at Saint Louis University School of Medicine, Dr. Moritz combines clinical practice, research, and education to advance the responsible use of AI in medicine. His work has earned national recognition through publications in NEJM AI, Nature Biomedical Engineering, and The Lancet, positioning him among a growing group of physician leaders shaping how artificial intelligence will transform patient care.

Most recently, Dr. Moritz co-authored a study published in NEJM AI, the artificial intelligence journal from the New England Journal of Medicine Group. The research introduced a large, expertly annotated dataset linking written radiology findings to their precise locations on chest CT scans in order to train AI models to identify and describe the abnormalities.

The publication reflects Dr. Moritz’s broader mission: helping healthcare responsibly harness AI to improve patient care while enabling clinicians to focus on the work that matters most.

“AI is going to take over a lot of what physicians and administrators now spend their day doing,” Dr. Moritz said. “That doesn’t shrink our role—it moves everyone up a level, toward harder problems and higher-value work, and lets us do more for more patients.”

Advancing AI beyond radiology

The NEJM AI publication is one of several recent scholarly contributions from Moritz exploring how artificial intelligence can reshape healthcare.

In 2025, he served as first author on a paper published in Nature Biomedical Engineering describing how coordinated AI agents could work alongside patients and healthcare professionals to improve communication, decision-making, and care delivery. He also authored a commentary in The Lancet examining the emerging risks of adversarial AI adoption across the healthcare ecosystem, highlighting the importance of thoughtful governance as AI becomes more deeply integrated into medicine.

Beyond his research, Dr. Moritz serves on the Society of Imaging Informatics in Medicine’s Artificial Intelligence Committee and directs Saint Louis University School of Medicine’s AI in Radiology clerkship, helping prepare the next generation of physicians to responsibly integrate AI into clinical practice.

Bringing AI into clinical practice

While contributing to AI research on an international stage, Dr. Moritz is also helping integrate AI into everyday radiology practice across SSM Health.

Today, radiology teams use FDA-cleared AI applications to help identify conditions such as stroke and intracranial hemorrhage, while breast imaging specialists use AI-assisted mammography tools to support diagnosis. Dr. Moritz has also collaborated on AI-powered solutions designed to summarize relevant patient history from the electronic health record, giving radiologists faster access to critical clinical information before interpreting imaging studies.

Looking ahead, he believes the next generation of AI will go far beyond identifying a single abnormality.

“Instead of AI looking for one specific finding, it’s going to recognize hundreds or thousands of different findings and generate a draft report for physician review,” Dr. Moritz said. “That has the potential to improve efficiency, consistency and ultimately patient care.”

Addressing healthcare’s biggest challenges

Dr. Moritz believes AI has the potential to help healthcare organizations address many of their greatest challenges, including workforce shortages, rising costs, and expanding access to care.

Rather than replacing healthcare professionals, he envisions AI taking on repetitive and administrative work so physicians, nurses, and other caregivers can devote more time to complex clinical decision-making and meaningful patient interactions.

He also believes AI’s greatest strength will come from its ability to analyze information across multiple sources simultaneously, including medical images, pathology slides, electrocardiograms, and electronic health records, to uncover insights that would be nearly impossible for humans to detect on their own.

At the same time, Dr. Moritz emphasizes that thoughtful implementation remains essential. As AI becomes more widely adopted by patients, healthcare providers, insurers, and other stakeholders, maintaining physician oversight, regulatory safeguards, and patient trust will remain critical to its successful integration into healthcare.

Looking toward the future

Although AI has advanced rapidly over the past few years, Dr. Moritz believes healthcare is still in the early stages of what’s possible.

He expects future AI systems will combine imaging, pathology, laboratory data, electrocardiograms, and electronic health records into large multimodal models capable of uncovering entirely new insights into disease diagnosis and treatment.

For clinicians and healthcare leaders, his advice is simple: stay curious.

“If you tried AI a year ago and weren’t impressed, try it again,” Dr. Moritz said. “The technology is improving incredibly fast, and we’re only beginning to see what’s possible.”

As SSM Health continues exploring innovative ways to improve care, Dr. Moritz believes AI will serve as a powerful tool to support, not replace, the expertise, compassion, and judgment of healthcare professionals, ultimately helping teams deliver better care to more patients.

SSM Health radiologist helping shape AI’s future in healthcare

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SSM Health radiologist helping shape AI’s future in healthcare - SSM Health

A recent analysis has highlighted growing patient apprehensions regarding the implementation of artificial intelligence (AI) tools by healthcare professionals. The findings reveal that a substantial number of patients would like to be kept informed about the deployment of AI in their medical care. Notably, over half of the surveyed patients are unaware of whether AI is currently being utilized in their healthcare interactions.

Conducted by the Pew Research Center in late June 2026, the survey encompassed nearly 5,000 adults across the United States. The results indicated that 72% of patients regard it as crucial or highly important for their healthcare providers to disclose the use of AI tools in patient care. Additionally, 16% feel that this information is somewhat important, while merely 7% indicated a lack of concern about being informed on the topic.

The survey unveiled varying levels of concern based on specific AI applications. The highest levels of worry were associated with AI involvement in making diagnostic decisions (81%), analyzing medical scans (81%), interpreting medical test results (80%), and documenting notes during appointments (72%). Furthermore, more than half of the respondents expressed the belief that they should be informed about the use of AI for auxiliary tasks such as prescription management (64%) and appointment scheduling (56%). However, a significant proportion (33%) felt that disclosure was unnecessary for scheduling activities. In various categories, around 9% to 10% of patients were uncertain if they should be informed, indicating a potential lack of awareness regarding the associated risks.

Despite a strong desire for transparency regarding AI's role in healthcare, nearly half of those surveyed (46%) reported being unaware of whether their healthcare providers employed AI solutions. Only 16% had been informed by a doctor about the utilization of AI in their treatment. Data from the Office of the National Coordinator for Health Information Technology (ONC) shows a marked increase in AI adoption, with 71% of hospitals utilizing AI tools in 2024, up from 66% in 2023. Nevertheless, a notable 33% of respondents presumed that their providers were not employing AI, suggesting a gap in transparency.

As the use of AI continues to rise, patients are increasingly seeking insight and a degree of control over its application in their healthcare. The survey revealed that 53% of participants feel they have little to no influence over AI usage, while only 16% believe they possess some degree of control. Furthermore, a compelling 63% expressed a desire for a greater say in AI-related healthcare decisions, with only 21% feeling comfortable about their current level of influence.

In light of the expanding role of AI in healthcare, it is imperative for providers to communicate clearly how these technologies are being utilized, ensuring that patient consent is obtained to foster trust. Additionally, ongoing evaluations of AI tools—such as transcription software and chatbots—are necessary to confirm their efficacy and accuracy.

Patients Desire Transparency on AI Utilization in Healthcare, According to Survey

A bright, airy doctor’s office flooded with warm natural light. A middle-aged patient and physician lean over a tablet showing an infographic titled “AI in Your Care: When & How,” complete with simple icons—a chip-shaped heart monitor, a magnifying-glass diagnostics symbol, and a data-shield for privacy. The patient’s expression is engaged as the doctor points to a timeline on the screen. In the background, a sleek monitor displays the words “AI Usage in Treatment.” high-detail, realistic photography style.

Survey Reveals Patients Want to Know When and How AI is Used in Healthcare - hipaajournal.com

- 72% of patients want to be informed about AI usage in healthcare settings.  
- 46% of patients are unaware if their providers use AI tools in care.  
- 53% feel they have little control over AI's role in their healthcare decisions.  

Survey Reveals Patients Want to Know When and How AI is Used in Healthcare

A recent survey has revealed that patients are concerned about the use of AI tools by doctors’ offices and other healthcare providers, and the vast majority of patients believe that they should be informed if their healthcare provider is using AI tools in connection with their healthcare. The survey also indicates that more than half of patients are unaware whether AI is currently being used in relation to their healthcare.

The survey was conducted on almost 5,000 U.S. adults in late June 2026 by the Pew Research Center. The survey revealed that 72% of patients believe it is extremely important or very important for their healthcare providers to disclose whether they are using AI tools in connection with healthcare, with 16% of respondents believing that it is somewhat important. Only 7% of respondents said they are not too bothered or not at all bothered about being informed about the use of AI.

Concern varied across different uses of AI, with the greatest concern expressed about AI being used to make diagnostic decisions (81%), analyze medical scans (81%), explain medical test results (80%), and take notes during a medical appointment (72%). More than half of patients believe that they should be informed about behind-the-scenes administrative services such as getting prescription refills (64%) and scheduling medical appointments (56%), although the latter had the largest percentage of patients who do not feel that disclosure is needed (33%). Across all areas of questioning, 9% or 10% of patients were not sure if they should be informed, potentially indicating they are unaware of any risks involved.

While most patients believe that they should be informed about the use of AI in healthcare, almost half of all surveyed patients (46%) said they were unaware whether their doctor’s office and other healthcare providers were using AI solutions, with only 16% of patients saying a doctor has actually told them that AI was used in their care. Adoption of AI in healthcare has grown considerably, with ONC’s figures showing that 71% of hospitals were using AI tools in 2024, up from 66% in 2023. Despite the high level of AI adoption, 33% of respondents believe their healthcare providers are not using AI tools, which suggests a lack of transparency.

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While patients want to be informed and have a say in how AI is used in healthcare, many Americans do not believe they have control over how AI is used. The survey revealed that more than half of respondents (53%) believe they either have no say or not much say in the use of AI in healthcare, with 16% believing they have some say. 63% of respondents to the survey would like more say in how AI is used, and only 21% of respondents said they are comfortable with how much say they currently have.

As AI adoption grows, it is important for healthcare providers to explain to patients how the tools are used and to obtain patient consent in order to maintain trust. It is also important for the tools, including transcription tools and chatbots, to be continuously evaluated to ensure they are fit for purpose and are generating accurate results.

Survey Reveals Patients Want to Know When and How AI is Used in Healthcare

Artificial intelligence (AI) in the healthcare sector is not a singular narrative; rather, it comprises three distinct stories that are advancing at varying paces, a detail often overlooked in mainstream discussions.

In the realm of hospitals and health systems, AI is steadily integrating into daily operations. It assists with essential administrative duties such as documentation, information summarization, and scheduling, all of which typically consume significant time for healthcare professionals. However, the integration of AI in areas like diagnosis and treatment decisions is approached with more caution.

Within the medical technology (medtech) field, the transition involves both regulatory and technical considerations. The FDA currently lists over 1,500 AI-enhanced medical devices, and regulatory bodies are developing frameworks that allow certain AI products to evolve post-launch, within specific, vetted parameters. This is crucial because AI technology is ever-evolving, while regulations for medical devices have traditionally focused on slower-shifting products.

In contrast, the pharmaceutical and biotech industries are following a significantly different trajectory. AI can significantly fast-track processes related to target identification and molecule discovery. However, it cannot bypass the lengthy phases of clinical testing, safety assessments, and regulatory evaluations that follow. While AI may expedite one segment of the development cycle, it doesn't eliminate the entire process.

Understanding this distinction is essential as the focus shifts toward demonstrating the value of AI implementations.

According to PwC's 2026 Global CEO Survey, 56% of CEOs reported that their organizations had not experienced increased revenues or reduced costs due to AI. This does not indicate failure; rather, it underscores that experimentation and value realization are not synonymous.

The pressing question has transitioned from "Can we adopt AI?"—a challenge many organizations have already met—to "Should we implement it in this context, and what quantifiable outcome can we expect?" 

Although this might seem straightforward, it's often where AI projects falter. Many companies mistakenly start with a technological solution in search of a problem; the more effective strategy is to identify the business challenge first.

Before committing to an AI project, clarity on three critical questions is vital:

1. What specific business challenge are we addressing?
2. What metric should improve if the implementation is successful?
3. Who in the organization is responsible for that metric?

If these questions cannot be clearly answered, the project may not be ready for investment.

It's essential to focus on measuring true outcomes rather than just activities. Metrics like "hours saved" or the number of active users do not equate to business value. In healthcare, we should prioritize factors such as cycle time, error rates, quality, revenue, margins, inventory management, customer or patient experiences, along with risk and compliance.

Post-launch, it's also critical to revisit the projected results after 90 and 180 days to assess their alignment with the original business objectives. If the expected metrics haven't shifted, it may be time to revise the solution or withdraw funding. Despite strict capital allocation criteria for technology investments, many organizations lack similar rigor in evaluating their effectiveness.

Currently, some of the quickest benefits from AI in healthcare stem from processes that are repeatable, data-rich, and rules-based. Areas such as supply-chain forecasting, inventory management, quality assurance in manufacturing, predictive maintenance, customer support, clinical documentation, knowledge retrieval, and compliance workflows exemplify this trend.

Furthermore, the human aspect cannot be neglected. Employees may feel anxious about AI's implications for their jobs, and reassurances like “AI will never take your role” are often viewed as unconvincing.

Typically, jobs comprise two categories: repetitive tasks and those requiring human insight. AI excels at automating the former. In healthcare, this translates to reducing the documentation burden for nurses, minimizing data sorting tasks for researchers, or lessening paperwork for quality engineers. The real opportunity lies in providing professionals more time for tasks that demand their unique skills: judgment, problem-solving, creativity, empathy, and relationship-building.

Organizations must also simplify the adoption of safe practices. If employees lack access to approved tools that streamline their work, they might resort to unofficial methods, which can elevate risks.

The skill set required will also evolve. Technology experts will need to focus on integration, governance, monitoring, and vendor management, rather than just model development. For others in the organization, honing the ability to ask the right questions of AI, recognizing potential inaccuracies in responses, and understanding when human evaluation is essential will be crucial.

Ultimately, success in the AI landscape will not belong to those with the most pilot projects or the loudest promotional strategies. It will go to organizations that effectively integrate AI as a reliable, everyday component of their operations and that can demonstrate meaningful improvements as a result of its implementation. 

Zeeshan Tariq serves as the Chief Digital & Information Officer for CooperVision.

The future of AI in healthcare: Transitioning from excitement to tangible business benefits

A bright, modern hospital office bathed in warm natural light. A doctor in a white coat and a business executive in a tailored suit stand side by side, examining a semi-transparent tablet floating before them. On the tablet, a rising bar chart interwoven with medical symbols—a heartbeat line, DNA helix—fades into delicate neural network circuit patterns. Sunlight streams through a large window, illuminating a stethoscope and patient chart resting on a sleek desk. The scene conveys AI in healthcare moving from hype to real business value.

The future of AI in healthcare: Moving from hype to business value | Viewpoint - Rochester Business Journal

- AI in healthcare is currently enhancing operations but faces cautious adoption for diagnosis and treatment.  
- Regulatory agencies are adapting to AI's rapid evolution while ensuring medical device safety and efficacy.  
- Successful AI initiatives must focus on measurable outcomes, not just technology implementation or activity metrics.  

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Artificial intelligence in healthcare is not one story. It is three different stories moving at three different speeds, and that nuance often gets lost in the headlines.

In hospitals and health systems, AI is already becoming part of everyday work. It helps with documentation, summarizing information, scheduling and other administrative tasks that consume enormous amounts of clinicians’ time. But when AI gets closer to diagnosis or treatment decisions, adoption is understandably more deliberate.

In medtech, the shift is as much regulatory as technical. The FDA‘s public list now includes more than 1,500 AI-enabled medical devices, and regulators are creating ways for certain AI-enabled products to improve after launch within predefined, reviewed boundaries. That matters because AI does not stand still, while medical-device regulation has historically been built around products that change much more slowly.

Pharma and biotech are moving at a different speed altogether. AI can dramatically accelerate parts of target identification and molecule discovery. What it cannot do is eliminate the years of clinical testing, safety work and regulatory review that follow. AI may shorten one leg of the relay race but it does not remove the rest of the race.

That distinction matters because we are entering a much more important phase of AI which is the importance of proving value.

PwC‘s 2026 Global CEO Survey found that 56% of CEOs said their companies had seen neither higher revenue nor lower costs from AI. That does not mean AI is failing. It means experimentation and value are not the same thing.

The question is no longer, “Can we use AI?” Most organizations have already proven that. The better question is, “Should we use it here, and what measurable result will be different if we do?”

That sounds simple, but it is where many AI initiatives go wrong. Too often, companies start with a technology and go looking for a problem. The better approach is the opposite.

Before I invest in an AI initiative, I want three questions answered in plain English:

What business problem are we solving?

What number should move if this works?

Who in the business owns that number?

If we cannot answer those questions, the initiative is probably not ready to be funded.

It’s important to be able to measure outcomes, not activity. “Hours saved,” number of users and prompts submitted can be useful indicators, but they are not business value. In healthcare, we should care more about cycle time, error rates, quality, revenue, margin, inventory, customer or patient experience, risk and compliance.

I also believe in going back 90 and 180 days after launch and comparing the results with the original business case. If the number did not move, it’s time to fix the solution or stop funding it. Too many organizations are disciplined about approving technology investments and surprisingly undisciplined about determining whether those investments actually worked.

Today, some of the fastest returns in healthcare are coming from work that is repeatable, data-heavy and rules-based: supply-chain forecasting, inventory optimization, manufacturing quality, predictive maintenance, customer service, clinical documentation, knowledge retrieval, and quality and regulatory workflows.

There is also a human dimension that leaders cannot ignore. Employees are understandably worried about what AI means for their jobs, and telling people, “AI will never replace you,” is not a credible answer.

Most jobs contain two kinds of work: repetitive work and work requiring human judgment. AI is increasingly good at the first. In healthcare, that can mean giving a nurse less documentation, a scientist less data sorting, or a quality engineer less paperwork. The opportunity is to give people more time for the work that actually requires them: judgment, problem-solving, empathy, creativity and relationships.

Organizations also need to make the safe path the easy path. If employees do not have approved tools that help them get their work done, some will inevitably find their own. That creates more risk, not less.

The skills we need will change too. Technology professionals will increasingly need to be good at integration, governance, monitoring and vendor management – not simply building models. For everyone else, the critical skill is judgment: knowing how to ask AI the right question, recognizing when the answer may be wrong, and understanding when human review is required.

In the end, the winners in AI will not be the organizations with the most pilots or the loudest strategy. They will be the ones that make AI a practical, trusted part of how the business works – and can prove that something important got better because of it.

Zeeshan Tariq is Chief Digital & Information Officer for CooperVision.

The future of AI in healthcare: Moving from hype to business value

A recent survey conducted by the Pew Research Center reveals a significant demand for transparency regarding the use of artificial intelligence (AI) in healthcare. Over 70% of nearly 5,000 American adults surveyed emphasized the necessity for healthcare providers to disclose when AI technology is utilized in medical decisions, such as interpreting medical scans, diagnosing health conditions, and clarifying lab results.

Patient awareness extends beyond core functions; a majority of respondents also indicated a desire to be informed when AI is employed for tasks like note-taking during appointments, managing prescription refills, or scheduling visits.

Trust plays a critical role in shaping public perception of AI in healthcare, according to Andrew Crawford, senior counsel of data and privacy at the Center for Democracy and Technology. While the information that physicians input into AI systems—be it for scribing or diagnostics—is subject to the privacy regulations of the Health Insurance Portability and Accountability Act (HIPAA), it’s crucial that patients comprehend these safeguards and feel confident that their data isn’t being exploited for advertising purposes or sold to third-party data brokers.

Crawford stressed the importance of providers communicating the tools they utilize, particularly innovative technologies like AI, so patients can make informed choices about their care. Nearly half of those surveyed expressed uncertainty about whether AI is utilized in their healthcare settings.

To foster trust, patients should feel empowered to inquire about the role of AI in their treatment, its functioning, the extent of the physician's involvement, and the options available should they prefer to avoid AI applications in their care.

An ongoing class action lawsuit against Sutter Health and Memorial Care underscores patient concerns regarding AI’s role in recording sensitive patient-provider interactions. The lawsuit alleges that medical personnel used AI tools to document confidential communications without gaining prior consent, potentially breaching state and federal laws.

Abridge AI, the clinical documentation service at the heart of these legal challenges, is currently employed by numerous health systems across the country. For instance, Kaiser Permanente implemented it in 40 hospitals and over 600 medical offices in 2024, while the Mayo Clinic incorporated it for roughly 2,000 clinicians serving over 1 million patients in early 2025.

Kaiser Permanente asserts that their usage of the tool mandates patient consent, and that all AI-generated notes will undergo human review before being added to medical records.

There are also concerns about the accuracy of AI systems, as Crawford noted. AI scribes can enable physicians to focus more intently on patient conversations, but inaccuracies in transcription could jeopardize patient safety through misdiagnoses or incorrect prescriptions. Crawford urged that clinicians using these technologies, even for administrative tasks, must ensure their accuracy and undergo thorough testing and evaluation, both prior to and following their deployment, to confirm they serve the intended purpose effectively.

Poll Reveals Patients Desire Transparency About AI in Healthcare

A bright, sunlit clinic waiting room: an adult patient sits at a small table reading a consent form boldly titled “AI Diagnostics Disclosure,” with a subtle graphic sidebar of circuit-like lines. A friendly doctor stands beside them, holding a tablet and offering a reassuring smile. On the wall behind, a medical chart’s heartbeat line gently morphs into a faint circuit pattern. Warm, natural light floods the scene, conveying transparency and trust.

Survey: Patients Want Disclosure on AI Use in Healthcare - bankinfosecurity.com

- Over 70% of Americans want clear disclosure of AI usage in healthcare settings.  
- Many patients are unaware of AI's role in their medical experiences and decisions.  
- Trust in AI's accuracy is crucial, impacting patient comfort and potential health outcomes.  

Artificial Intelligence & Machine Learning , Data Privacy , Data Security

Survey: Patients Want Disclosure on AI Use in Healthcare

More Than 70% Say AI Use in Any Medical Scenario Should Be Disclosed

Image: Shutterstock

Most Americans want transparency about how doctors use artificial intelligence in their health care, but many feel they have little say in how AI is brought into vulnerable moments of their lives, shows a June survey of almost 5,000 U.S. adults.

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

More than 70% of Pew Research Center survey participants said it is extremely important for healthcare providers to disclose AI use in diagnostic decisions, such as analyzing medical scans, diagnosing conditions and explaining lab results.

Even outside core medical functions, the majority of participants said they want to be told when AI is used to take notes during an appointment, order prescription refills from a pharmacy or schedule medical visits.

Trust, above all, shapes Americans' views on AI in healthcare, said Andrew Crawford, senior counsel of data and privacy at the Center for Democracy and Technology.

Patient information that doctors enter into AI systems such as an AI scribe or a diagnostic tool is covered by the Health Insurance Portability and Accountability Act privacy rule, but patients need to understand those protections and trust that their data won't be used for advertising or sold to data brokers, Crawford said.

"So knowing what tools providers are deploying, especially new tools like AI, is really important. And then patients can make sure that they're comfortable with those choices," Crawford said.

Nearly half of people surveyed said they are not sure if AI is used in hospitals or doctors' offices at all.

"People should have more confidence about when and where their healthcare providers are deploying AI tools," Crawford said. Then, patients can build trust by asking what role AI plays, how it works, what role the doctor retains and what alternatives exist if they choose not to involve AI.

An ongoing class action lawsuit against Sutter Health and Memorial Care highlighted patients' discomfort with AI tools recording and transcribing conversations containing sensitive personal information without clear notice or consent.

Plaintiffs alleged that medical staff at the defendants' facilities used AI to record confidential physician-patient communications without asking for permission, in violation of state and federal law (see: Lawsuit: AI Illegally Recorded Doctor-Patient Encounters).

Abridge AI, the real-time clinical documentation platform at the center of the lawsuits, has been adopted by numerous health systems nationwide. Kaiser Permanente deployed it across 40 hospitals and more than 600 medical offices in 2024 and Mayo Clinic rolled it out to about 2,000 clinicians serving over 1 million patients in early 2025.

Kaiser Permanente said the tool requires patient consent, and AI-typed notes will be reviewed by a human before being entered into medical records.

Patients may be concerned about AI's accuracy, Crawford said. Having an AI scribe can help doctors focus more on the present conversation but if the tool transcribes incorrectly, it could cause potential harm through an incorrect diagnosis or prescription.

"Doctors deploying these tools, even the administrative tools, have to make sure that the tools are fit for purpose, are accurate, and that they are testing them and evaluating them not only before they deploy them, but once they're deployed as well, to make sure that they're functioning as the doctor wants them to," Crawford said.

Hims & Hers CEO discusses AI's role in healthcare, GLP-1 medications, and addressing healthcare costs.

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