A federal judge has halted certain aspects of a newly proposed rule from the Education Department that would modify borrowing limits for individuals pursuing careers in healthcare. This legal decision may have significant implications for those looking to finance their education in clinical fields.

Healthcare embraces AI while still addressing regulatory challenges.

A modern hospital ward bathed in warm morning sunlight: a physician in a white coat leans over a sleek AI diagnostic console displaying patient scans, while a sturdy steel guardrail labeled “Guardrails” curves protectively around the device; subtle floating medical charts hover overhead as a nurse watches thoughtfully from the side—natural light, hopeful yet cautious atmosphere.

Healthcare is all in on AI but still grappling with the guardrails - Modern Healthcare

- A federal judge halted an Education Department rule on borrowing limits for clinicians.  
- The blocked rule aimed to impact future borrowing for aspiring healthcare professionals.  
- Legal challenges continue over regulations affecting student loans for healthcare education.

A federal judge blocked part of an Education Department rule that would affect borrowing limits for aspiring clinicians.

Healthcare is all in on AI but still grappling with the guardrails

The ongoing conflict between healthcare providers and insurers has evolved into a sophisticated contest dominated by algorithms, where AI-driven tools for prior authorizations face off against those designed for appeals.

Ashis Barad has firsthand experience from both perspectives of this struggle. He began his professional journey as a physician at Sutter Health and later worked at Baylor Scott & White Health, where he was instrumental in advocating for advanced digital solutions. In 2022, Barad took on the role of chief digital and information officer at Allegheny Health Network and its parent organization, Highmark Health, gaining insights into how insurers develop and implement AI technologies. Two years later, he transitioned back to the provider side by joining the Hospital for Special Surgery (HSS) in the same executive role.

Barad asserts that the ongoing race to automate authorizations and appeals is ultimately a misguided approach. 

“We're engaged in a limited competition while ignoring the broader, ongoing challenge,” Barad pointed out. “If we continue to focus solely on the immediate concerns, we will persist on an inflationary trajectory. We must collaborate to determine how AI can lead us towards a more efficient, cost-effective model because that is what our patients deserve.”

Rather than funneling resources into AI for swifter authorizations and appeals, Barad envisions a future where providers and payers share data to create more individualized care protocols.

He highlighted HSS as a prime example, noting that the institution conducts over 40,000 orthopedic surgeries annually, surpassing any other hospital in the United States. HSS has successfully created a comprehensive database that links imaging results with surgical outcomes.

Such extensive real-world data is a significant gap in the information available to insurers. Barad believes that if payers and providers collaborated to develop these models, authorization processes could become more precise and tailored to individual patients, rather than relying on generic guidelines.

He has already tested this concept in discussions with payers. During a recent meeting with a prominent insurance provider, he inquired about the potential value of HSS's integrated imaging and outcomes data for their actuarial teams.

“They expressed a keen interest, saying they'd find it extremely valuable,” he shared.

Payers generally have access only to basic claims information, such as rates of infection or readmission following initial surgeries, but they lack data on outcomes for patients undergoing multiple procedures. This lack of detail is significant, especially as many HSS patients are complex cases that fall outside typical metrics.

Barad believes that a transformative opportunity exists in utilizing this richer dataset to fundamentally change authorization processes. He pointed to the practice of “gold carding,” which allows exceptional providers to bypass prior authorizations, as an area ripe for reimagining.

Currently, this practice relies on rudimentary criteria, such as flowcharts and cost limits, rather than actual patient outcomes. Barad envisions a redesigned approach personalized for each patient, integrating authorization seamlessly into care pathways rather than treating it as a separate, post-service request.

However, he warns that as long as providers and payers continue to invest in AI merely to outsmart one another, this competition adds unnecessary costs to an already strained system. He emphasizes that the road to genuine cost reduction lies in cooperation and shared data initiatives.

The conflict between providers and payers over bots is increasing costs for all.

Dimly lit industrial hall with two towering mechanical robots locked in a fierce tug-of-war over a giant, cracked dollar coin. One robot bears a glowing medical caduceus emblem, the other a bold insurance-shield icon. Sparks fly from frayed cables as warm orange light flickers off metal surfaces. In the foreground, silhouettes of worried adults clutching empty wallets watch helplessly under heavy shadows.

The ‘Bot Vs. Bot’ Dynamic Between Providers & Payers Is Driving Up Costs for Everyone - MedCity News

- The conflict between healthcare providers and payers revolves around competing AI tools and strategies.  
- Ashis Barad advocates for collaboration on data to create personalized care pathways.  
- Current autorization methods need to evolve from blanket policies to individualized patient approaches.

The war between healthcare providers and payers has increasingly become a battle of algorithms, with AI tools for prior authorizations on one side and AI-powered appeals on the other.

Ashis Barad has sat on both sides of that fight. He began his career as a physician at Sutter Health, then practiced at Baylor Scott & White Health, where he led the push for better digital tools. In 2022, Barad transitioned into the role of chief digital and information officer at Allegheny Health Network and its parent company Highmark Health, getting a taste of how payers build and deploy AI internally. Two years ago, he joined the Hospital for Special Surgery (HSS) to serve as chief digital and information officer, sitting firmly on the provider side once again.

In his eyes, providers and payers racing to out-automate each other is a losing strategy.

“We’re playing a finite game when there’s an infinite game to be played,” Barad declared. “If we play the finite game of today, we’re going to continue on the inflationary route. So we have to figure out how to work together to figure out what the deflationary path for AI is, because that’s what we owe the people, honestly.”

Instead of pouring resources into AI for faster authorizations and appeals, Barad wants to see providers and payers pool their data to build more personalized care pathways.

He pointed to HSS’ own data as an example of what’s possible. The hospital performs more than 40,000 orthopedic surgeries a year — more than double the volume of any other U.S. hospital — and has developed a structured repository connecting imaging data to surgical outcomes.

Having that kind of real-world data is something insurers lack. Barad thinks that if payers and providers built those models together, authorizations would become far more targeted at the individual level, rather than relying on blanket rules.

Barad said he has already tested the idea with payers directly. During a recent meeting with a large insurance company, he asked how valuable HSS’ linked imaging-and-outcomes data would be to their actuarial teams.

“They were like, we would salivate over it,” he said.

That’s because payers typically only have access to basic claims data — infection or readmission rates after a first surgery, for instance — not the outcomes data for patients on their third, fourth or fifth procedure. But those are the kind of complex cases that make up much of HSS’ patient population, Barad pointed out.

Barad thinks the real opportunity lies in using richer data to reshape how authorization works in the first place. He pointed to gold carding, the practice of exempting high-performing providers from prior authorization, as a concept ripe for reinvention.

Today’s version is a blunt instrument based on flowcharts and cost thresholds rather than actual outcomes, he explained. Built the right way, Barad thinks it could instead be personalized down to each patient. Authorization would be baked into a care pathway, not requested after the fact.

But as long as payers and providers keep investing in AI to outmaneuver each other, Barad warned this spending only adds a new layer of cost to a system already straining under it. He said the path to actually lowering costs runs through collaboration on data.

Photo: Malorny, Getty Images

The ‘Bot Vs. Bot’ Dynamic Between Providers & Payers Is Driving Up Costs for Everyone

The global market for AI in healthcare is projected to soar to $491 billion by 2032. However, the World Economic Forum (WEF) issues a cautionary note about potentially repeating the errors of past digital transformations. 

One significant example highlighted is the implementation of electronic health records (EHRs). While EHRs were expected to enhance coordination and elevate clinical decision-making, their introduction initially led to an increase in administrative burdens. This shift often resulted in healthcare professionals spending more time on documentation than on patient care.

The WEF suggests that rather than becoming the focal point of clinician-patient interactions, AI should function discreetly in the background. The aim should be to streamline healthcare processes, thereby enabling clinicians to dedicate more time to developing trust with their patients.

The key takeaway, according to the WEF, is not that digital technologies are inherently flawed, but rather that innovation lacking a clear focus on human-centered objectives can lead to unforeseen challenges.

Building trust between patients and healthcare providers has been a longstanding concern for the WEF, which has recognized it as essential for the effective integration of AI in the healthcare sector. Shyam Bishen, who heads the Centre for Health and Healthcare at the WEF, emphasized in a 2024 article that “mistrust is the most formidable barrier to adopting generative AI in healthcare.”

WEF: AI Could Enhance the Humanity of Healthcare

A bright, sunlit WEF conference stage with a banner reading “WEF” overhead, a doctor gently resting a hand on an adult patient’s shoulder as an AI-powered tablet beside them glows with soft, warm-toned network graphics, warm natural light illuminating their hopeful expressions.

WEF: AI Has the Potential to Make Healthcare More Human - Healthcare Digital

- The global AI healthcare market is projected to reach US$491bn by 2032.  
- WEF warns against past mistakes, citing problematic electronic health records rollout consequences.  
- Trust is crucial for AI adoption; mistrust is a barrier in healthcare settings.  

With the global AI healthcare market on track to reach US$491bn by 2032, the World Economic Forum (WEF) warns against repeating mistakes made during previous waves of digital transformation.

It cites the rollout of electronic health records (EHRs), which promised better coordination and improved clinical decision-making, but initially contributed to growing administrative workloads, often forcing doctors to spend more time documenting care than delivering it.

Instead of taking centre stage in the clinician-patient relationship, WEF argues AI must operate in the background, removing friction from healthcare delivery while allowing clinicians to spend more time building trust with patients.

The lesson, WEF argues, is not that digital technologies fail, but that innovation without a clear human-centred purpose can create unintended consequences.

Creating trust between patient and clinician

WEF has long recognised trust as a critical factor in the successful adoption of AI in healthcare.

“Mistrust is the most formidable barrier to adopting generative AI in healthcare,” Shyam Bishen, Head of the Centre for Health and Healthcare at WEF, wrote in a 2024 article.

WEF: AI Has the Potential to Make Healthcare More Human

The intersection of agentic artificial intelligence (AI) and the healthcare sector has reached a pivotal juncture. With the rapid pace of technological advancement, many healthcare organizations find it challenging to keep up. This delay is not just a matter of efficiency, as it could lead to postponed care, compromised patient privacy, and a breakdown of trust between clinicians and patients.

Leading organizations in this space are typically those that grasp how agentic AI operates, not necessarily the ones that adopt it the quickest. It’s crucial to comprehend where responsible usage of AI can falter.

Exploring the Framework

While agentic AI might seem like a singular entity, it actually functions through five interconnected layers:

1. **Power Requirements**: Similar to all cloud computing frameworks, robust electrical infrastructure is essential to power the AI agents.
  
2. **Infrastructure/Hardware**: Data centers springing up globally are the repositories for the vast amounts of data that AI agents utilize.

3. **Network Layer**: It’s vital to have advanced security measures in place to protect sensitive data.

4. **Data Layer**: The intricate chain of protocols is necessary for effective digital data transmission.

5. **Application Layer**: The various forms in which users engage with AI agents can differ significantly.

Users must understand how these layers interrelate to identify and address issues effectively. Furthermore, reassessing the potential vulnerabilities at each layer is essential for responsible utilization.

Defining Responsible Use

Discussions around responsible AI often focus on the network, data, and application layers, as these are the ones users interact with most frequently. Security breaches can occur even in well-protected organizations if the data layer isn't managed correctly. For example, if an AI learns to equate a patient's pain rating of 10 with "severe pain" without proper context, it can lead to misleading documentation and consequently harm clinician trust and organizational credibility.

Responsible usage of AI demands more than just a secure infrastructure. Three foundational principles must guide any AI program:

1. **Bias Mitigation**: The medical field has historical examples that illustrate the dangers of biased treatments and protocols. AI trained on historical data can replicate these biases on a large scale. Healthcare organizations must consistently review AI systems for biases related to age, race, gender, and other variables, treating this as a clinical necessity rather than a mere compliance task.

2. **Observability**: Transparency is crucial for all personnel interacting with AI agents. They must understand the agent's designated tasks and performance metrics to ensure it functions within established boundaries.

3. **Explainability**: Articulating the functionality and rationale behind an AI agent's actions is essential. Regular third-party audits are important safeguards that enhance accountability both internally and externally.

Effective governance in AI for healthcare requires more than just compliance with a single standard or certification. 

Adopting a comprehensive strategy is vital. Industry benchmarks such as the National Institute of Standards and Technology’s (NIST) Risk Management Framework and insights from the Open Worldwide Application Security Project (OWASP) provide valuable guidance for navigating AI risk and security. Compliance with HIPAA is crucial for every healthcare AI application. Initiatives like the Coalition for Health AI, which I am part of, aim to solidify principles of agentic AI into standardized best practices across the sector.

However, having frameworks isn’t sufficient on its own. The most effective organizations are weaving these standards into their governance models, tailoring them to their specific workflows, patient demographics, and risk assessments, rather than simply fulfilling compliance requirements. The objective is to cultivate trust that elevates AI from a potential liability to a lasting asset.

Thoughtful Implementation Over Hastiness

Healthcare providers cannot ignore the importance of agentic AI, yet they must also refrain from deploying it without adequate infrastructure for responsible use. Finding the balance between overanalyzing—leading to stagnation—and underanalyzing—resulting in reckless deployment—is crucial for strategic success.

Agentic AI serves as a bridge uniting predictive, generative, and autonomous systems. Establishing this bridge sustainably requires active human involvement to set, monitor, and enforce safety parameters over time.

Challenges in healthcare often arise not from the accuracy of AI models, but from determining accountability when errors occur. The most effective adopters create an environment of clinical and organizational trust, transforming agentic AI into a valuable tool for both healthcare teams and their patients.

Steering the Responsible Integration of Agentic AI in Healthcare

A bright, sunlit hospital ward where a female physician in a white coat stands beside a sleek robotic arm offering her a digital tablet displaying a glowing 3D heart scan. A stethoscope and patient charts lie on a nearby stainless steel tray. Warm natural light streams through large windows, casting soft shadows that emphasize human–AI collaboration and responsible oversight in healthcare.

Navigating Responsible Adoption of Agentic AI in Healthcare - HCI Innovation Group

- The healthcare industry faces challenges in adopting rapidly advancing agentic AI technology responsibly.  
- Responsible AI use requires addressing bias, observability, and explainability principles thoroughly.  
- Successful organizations integrate AI frameworks into their governance tailored to specific healthcare contexts.  

The healthcare industry’s relationship to agentic artificial intelligence has reached an inflection point. The technology is advancing faster than most organizations can adopt it — and in healthcare that gap carries consequences for administrators, clinicians, payers and patients. Unlike other industries where delayed adoption means lost efficiency, in healthcare it can mean delayed care, compromised privacy and erosion of clinical trust.

The organizations that lead aren’t necessarily the ones moving fastest, but those who possess a clear understanding of how agentic AI actually works — and where responsible use can break down.

Understanding the Layers

Agentic AI is often thought of as a single system. Functionally, however, it operates across five interdependent layers:

Power requirement: like all cloud-computing platforms, a strong grid is needed to power the computing of every AI agent

Infrastructure/hardware: the data centers that are springing up around the world are where the data every AI agent uses "lives"

Network layer: sophisticated digital safeguards must be in place to ensure private data remains private

Data layer: digital data delivery requires a complex chain of protocols

Application layer: the way a user interfaces with an AI agent can take a variety of forms

Every agentic AI user depends on all five. To troubleshoot a problem, or improve a process, users must know which layer to target. And to use these systems responsibly, we need to understand where each layer is vulnerable.

What ‘Responsible Use’ Really Means

Most conversations about responsible AI focus on the top three layers — network, data and application — because end users interact with these layers most often. Malicious actors can infiltrate an insecure network. But irresponsible use doesn’t require bad intent. A well-secured organization can still fail at the data layer through hallucinations that seem harmless until they aren’t.

Consider a concrete example: A patient rates their pain a 10 on a 10-point scale. An LLM might interpret that rating as “severe pain,” and insert that phrase into a clinical summary, but the physician never used the phrase himself. That single interpretive leap, multiplied across thousands of cases, can create documentation that misrepresents clinical reality, exposes organizations to legal risk, and erodes the trust of the clinicians the system was built to support.

Responsible use requires more than secure infrastructure. In particular, three principles must undergird any AI initiative:

Bias mitigation. The history of medicine offers hard lessons about what happens when treatments and protocols embed the biases of their era. AI systems trained on that same historical data can perpetuate those patterns at scale. Healthcare organizations deploying agentic AI must actively audit for bias across age, race, gender and other factors — not as a compliance exercise, but as a clinical imperative.

Observability. Every person whose work is touched by an AI agent needs visibility into what that agent is designed to do and how it’s performing. Without it, there’s no way to confirm the system is operating within its guardrails — and no way to catch it when it isn’t.

Explainability: The ability to articulate what an AI agent does, and why it does it, shouldn’t be optional. Third-party audits are a necessary safeguard. The capacity to explain a system’s purpose to stakeholders outside the immediate team ensures both internal and external accountability.

Responsible governance in this space — for now, at least — requires more than holding a single membership or certification.

Begin with a multi-pronged approach. Industry frameworks like the National Institute of Standards and Technology’s (NIST) Risk Management Framework and the Open Worldwide Application Security Project (OWASP) offer actionable insights for managing AI risk and security. HIPAA compliance remains non-negotiable for any healthcare AI deployment. Organizations like Coalition for Health AI (whose Agentic AI work group I belong to) are working toward codifying agentic AI principles into industry-wide best practices.

Frameworks alone aren't enough. The organizations already getting this right are integrating these standards into their own internal governance frameworks — contextualizing them to their specific workflows, patient populations and risk profiles — rather than treating compliance as a box to check. The goal is to build the kind of trust that makes AI a durable asset — not a liability.

Deliberate Adoption Beats Moving Quickly

Healthcare organizations can’t afford to ignore agentic AI, but they also can’t afford to deploy it without the infrastructure to use it responsibly. The gap between these two failure modes — overanalyzing to the point of paralysis, and underanalyzing to the point of recklessness — is where the real strategic work lives.

Agentic AI represents a bridge between predictive, generative, and autonomous systems. Building that bridge to last requires keeping a human meaningfully in the loop — the mechanism by which guardrails are set, monitored, and enforced over time.

Health systems struggle less with model accuracy than deciding who is responsible when the model is wrong. The most successful adopters build the kind of clinical and organizational trust that makes agentic AI transformational, both for their teams and for their patients.

Navigating Responsible Adoption of Agentic AI in Healthcare

Just four months prior to Senator Lindsey Graham's passing, I found myself presenting in front of the United States Senate, tasked with addressing a seemingly straightforward query: In what ways can artificial intelligence significantly improve healthcare?

To exemplify my point, I focused on aortic dissection.

“The gravest mistake isn’t a malfunction of the machinery; it’s the oversight or postponement of a diagnosis.”

This past weekend, early reports emerged indicating that Senator Graham succumbed to this very condition.

While the timing is rather unfortunate, I have no insights into Senator Graham's medical treatment, and nothing stated here should imply that alternative technologies would have altered the outcome. Nonetheless, this incident serves to underscore why disorders like aortic dissection are often central to discussions about AI in healthcare.

I could have discussed various conditions such as diabetes or heart failure. However, I chose aortic dissection because it epitomizes one of the toughest challenges within the healthcare landscape. This condition is rare, with symptoms that closely mirror those of more prevalent issues like heart attacks, strokes, severe back, or abdominal discomfort. Accurate recognition requires significant clinical acumen, and once it’s suspected, swift imaging is essential, alongside the immediate mobilization of specialists and often, urgent patient transfers for surgery. Every second is crucial.

Upon reflection, I realized that this example extended beyond just aortic dissection; it was fundamentally about time.

In discussions on healthcare AI, the primary focus is often whether machines can identify diseases better than human clinicians. This is indeed a vital area of progress, but after over two decades of working with healthcare systems, I am convinced that recognition is merely the initial phase of the process.

Throughout my career, I’ve encountered healthcare professionals at various stages of their journey, many grappling with burnout. Never have I met one who intended to overlook a diagnosis. Consequently, I believe we sometimes search for failures in the wrong areas. The healthcare framework is profoundly human-centric. Radiologists conclude their shifts, emergency doctors proceed to the next case, and specialists concentrate on the specific issue at hand. Meanwhile, patients often leave feeling overwhelmed as they juggle personal responsibilities like arranging childcare, caring for aging relatives, or figuring out their work schedules. Information doesn’t typically get lost due to intentional negligence; rather, it becomes obscured amidst the complexities of daily life.

Before my testimony, Dr. Andrew Ibrahim, Chief Clinical Officer at Viz.ai, presented before the House Energy and Commerce Committee to discuss the role of AI in healthcare. His insights resonated with what I hoped lawmakers would grasp. “What truly counts,” he emphasized, “isn’t just the algorithm itself, but how these technologies are embedded within actual clinical workflows to address significant, time-critical issues.”

One of the most impact-full moments from Ibrahim’s testimony was when he shared a personal experience involving his father.

When Ibrahim’s father exhibited stroke symptoms, his medical knowledge enabled him to recognize the situation promptly. He informed the emergency department prior to his father’s arrival and coordinated care that ultimately resulted in a favorable outcome.

In a recent conversation, Ibrahim shared that this experience brought to light an aspect he hadn’t fully grasped even during his medical training: “I didn’t realize how challenging it is for information to reach the right individual,” he remarked. “There’s someone at the end of that communication loop who needs to make a critical decision quickly.”

He further outlined that AI's potential lies not only in spotting potentially life-threatening conditions but also in efficiently transmitting the necessary information and images to the specialists positioned to act.

He made another important observation that extends well beyond stroke cases:

“Not everyone has a medically knowledgeable relative ready to coordinate their emergency care. We must develop systems that guarantee patients receive appropriate treatment without depending on luck or personal connections.”

This is the thought that has lingered with me. We’ve dedicated considerable time questioning whether AI can outperform doctors in disease diagnosis. I’ve come to believe that the true strength of AI lies in its ability to minimize the degree to which exceptional outcomes hinge on mere chance.

Lindsey Graham's Passing Highlights the Importance of Healthcare AI

A dimly lit hospital ICU at night, the single bed empty except for a rumpled gown and a flatlined heart-monitor screen casting ominous red glows. Beside it, a sleek robotic arm in standby, digital patient charts flickering “CRITICAL” alerts. A small U.S. flag hangs quietly on the wall, shadows swallowing the corners—evoking loss and the unheeded warnings of healthcare AI.

What Lindsey Graham’s Death Reminds Us About Healthcare AI - Forbes

- Senator Graham reportedly died from aortic dissection, underscoring AI's potential impact in healthcare.  
- Aortic dissection exemplifies challenges in timely diagnosis and treatment within healthcare systems.  
- Effective healthcare AI should streamline communication and information flow for timely decision-making.  

Close-up of CT scan results reflected in glasses of physician. Universal Images Group via Getty Images

Four months before Senator Lindsey Graham’s death, I stood before the United States Senate trying to answer a deceptively simple question: Where can artificial intelligence make the biggest difference in healthcare?

To illustrate my answer, I chose aortic dissection.

“The most dangerous failure is not a machine failure. It’s a missed or delayed diagnosis.”

This weekend, preliminary reports indicated that Senator Graham died from that very condition.

The timing is an unfortunate coincidence. I have no knowledge of Senator Graham’s medical care, and nothing written here should be interpreted as evidence that different technology would have changed the outcome. But this moment does remind us why conditions like aortic dissection have become defining examples in conversations about healthcare AI.

I could have spoken about diabetes, heart failure or countless other diseases. Instead, I chose aortic dissection because it captures one of healthcare’s hardest challenges. The condition is uncommon. Its symptoms often resemble far more common illnesses, including heart attack, stroke, severe back pain or abdominal pain. Recognition requires clinical expertise, but diagnosis is only the beginning. Once suspected, imaging must be interpreted quickly, specialists mobilized, and patients frequently transferred for emergency surgery. Every minute matters.

MORE FOR YOU

Looking back, this example was never really about aortic dissection; it was about time. At least, that’s what I eventually realized.

Healthcare AI is usually framed as a question of whether computers can recognize disease better than physicians. That is certainly an important area of innovation, but after more than twenty years helping health systems deploy clinical technology, I’ve become convinced that recognition is only the first step.

In more than twenty years working with health systems, I’ve met clinicians at every stage of their careers and every stage of burnout. I’ve never met one who wanted to miss a diagnosis. That’s why I think we sometimes look for failure in the wrong place. Healthcare is an extraordinarily human system. Radiologists finish shifts. Emergency physicians move on to the next trauma. Specialists focus on the problem they were asked to solve. Patients go home overwhelmed, trying to make sense of everything that happened while simultaneously figuring out how they’ll get their children to school the next morning, who will take an aging parent to an appointment, or whether they’ll be able to return to work. Information isn’t usually lost because someone intended for it to disappear. It gets buried beneath the realities of everyday life.

Months before I testified, Dr. Andrew Ibrahim, Chief Clinical Officer at Viz.ai, appeared before the House Energy and Commerce Committee to discuss healthcare AI. His testimony echoed what I hoped lawmakers would understand. “What matters,” he explained, “is not the algorithm in isolation, but how these tools are integrated into real clinical workflows to solve real, high-stakes problems.”

For me, the most memorable part of Ibrahim’s testimony was an experience he shared about his father.

When his father developed stroke symptoms, Ibrahim recognized what was happening because of his clinical knowledge. He alerted the emergency department before his father arrived and coordinated the care that ultimately helped produce a good outcome.

When we spoke recently, Ibrahim told me that the experience revealed something he had not fully appreciated even during his medical training. “I didn’t appreciate how hard it is for information to get to the right person,” he said. “There’s a person at the end of that telephone game who has to make a time-sensitive decision.”

The opportunity for AI, he explained, is not only to recognize a potentially life-threatening condition but to move the relevant images and information through that chain to the specialist who must act.

His testimony made a related point that extends far beyond stroke:

“Not everyone has a medically trained son ready to coordinate their emergency care. We need systems that ensure patients get the right treatment without relying on luck or personal connections.”

That’s the sentence I haven’t been able to move beyond. We’ve spent years asking whether AI can diagnose disease better than physicians. I’ve come to think AI is at its best when it makes extraordinary outcomes less dependent on extraordinary luck.

What Lindsey Graham’s Death Reminds Us About Healthcare AI

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

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

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

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

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

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

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

The Need for Enhanced Data Oversight in AI Healthcare Applications

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

Why AI in Healthcare Demands Stronger Data Oversight - BankInfoSecurity

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

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

Why AI in Healthcare Demands Stronger Data Oversight

Attorney Jordan Cohen of Akerman on AI Challenges Ahead

Jordan Cohen, partner, Akerman LLP

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

See Also: Know Thy Enemy: Threats to Cyber Resilience

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

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

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

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

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

In this video interview with ISMG, Cohen also discussed:

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

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

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

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

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

Why AI in Healthcare Demands Stronger Data Oversight

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Strategist claims healthcare may emerge as an early AI 'winner.'

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Healthcare could become an early AI 'winner,' strategist says - Fox Business

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Healthcare could become an early AI 'winner,' strategist says

In Rochester, Minnesota, Mayo Clinic's internal medicine physician, Dr. Alexander Ryu, faces the daunting task of reviewing extensive medical records when preparing to consult with patients. Many individuals seeking treatment at the clinic bring an array of disorganized documents from various health systems, often looking for second or third opinions. To streamline this process, Mayo Clinic has introduced an innovative AI tool named Record Time, which assists clinicians in rapidly analyzing patient records. This tool not only organizes documents chronologically but also generates relevant summaries, making vital information easier to locate.

Dr. Ryu notes that Record Time can reduce his preparatory time for each patient visit by five to thirty minutes, a valuable efficiency that allows him to spend more time directly with patients. The tool is designed to support doctors by highlighting crucial details that could affect treatment decisions and diagnostic recommendations—a significant advantage considering the tens of millions of pages of medical records the clinic processes annually. Ryu, who is also the vice chair of innovation for the Mayo Clinic's Department of Medicine, emphasizes the need for effective solutions to manage the vast volume of patient information.

The potential of AI in healthcare is widely recognized, with major companies like Google, OpenAI, and Anthropic launching health assistant chatbots in response to a growing public interest in AI for medical inquiries. While some tech leaders tout ambitious claims about AI's ability to revolutionize disease treatment, the Mayo Clinic approaches AI more pragmatically, focusing on its capacity to enhance patient care and save lives. In collaboration with organizations such as Microsoft and Scale AI, Mayo Clinic is leveraging its large database of patient records and clinical research to refine AI applications. Currently, around 150 AI models are operational within the institution.

However, the integration of AI in healthcare raises critical concerns about accuracy and patient privacy. Recently, Mayo Clinic's former Director of Research Operations, Traci Tamiko Eto, filed a lawsuit against the hospital, alleging retaliation for voicing concerns regarding privacy and oversight related to some of the AI systems.

A spokesperson for Mayo Clinic, Andrea Kalmanovitz, stated that while the hospital does not comment on ongoing legal matters, it is dedicated to the responsible development of AI, emphasizing the importance of privacy, security, and compliance in all initiatives. She affirmed the institution’s commitment to upholding patient trust and adhering to legal guidelines.

The utility of AI in medicine hinges on its capacity to detect trends within large datasets. Jason Droege, CEO of Scale AI, highlighted how AI can handle the tedious tasks typically performed by healthcare professionals, thereby accelerating the diagnostic process and supporting more effective treatment.

Dr. Matthew Callstrom, a radiologist and leader of Mayo Clinic’s generative AI program, believes strongly in AI’s potential. He cites a clinical trial aimed at assessing whether AI can recognize patients at risk for early-stage pancreatic cancer, a detection method that could lead to interventions years earlier than current practices, which usually diagnose the disease at an advanced stage with a meager five-year survival rate.

Additionally, Mayo Clinic has utilized AI for monitoring patients' cardiac rhythms to predict conditions like atrial fibrillation, which can lead to serious complications such as blood clots or strokes. According to Callstrom, identifying such conditions early can have a transformative impact on patient care.

To develop AI solutions, Mayo Clinic collaborates closely with both technical experts and clinicians to identify priority health challenges. Callstrom emphasizes the rigorous validation process for AI tools, which includes small-scale testing under physician supervision, with expansions based on performance outcomes. Continuous monitoring follows after tools are widely implemented.

Addressing concerns amongst staff regarding the implications of AI for employment, Callstrom reassured that while roles may evolve, they are not disappearing. For instance, nurses have been involved in creating an AI tool that documents patient interactions, significantly reducing the time spent on administrative tasks and enhancing direct patient engagement.

Droege believes the healthcare sector is still in the early stages of unlocking AI’s comprehensive potential. However, he argues that the emphasis should be placed on the quality of care rather than the speed of deployment. As he puts it, achieving optimal outcomes should be the primary goal, and speed should come only after ensuring accuracy and reliability in healthcare applications.

A leading global hospital is experimenting with AI to transform healthcare.

A modern hospital ward flooded with warm natural light as a surgeon and nurse study dynamic 3D patient scans on a sleek touchscreen console, a robotic medical arm poised to hand over surgical tools, the hospital’s grand facade visible through large windows, conveying a new era of AI-driven healthcare.

One of the world’s most prominent hospitals is testing how AI can revolutionize health care - CNN

- Mayo Clinic's AI tool, Record Time, helps organize patient records efficiently for doctors.  
- Over 150 AI models are deployed at Mayo Clinic to enhance patient care and diagnostics.  
- Concerns about AI accuracy and privacy persist, highlighted by a recent lawsuit against the hospital.  

Rochester, Minn. —

Preparing to meet with a patient can require Mayo Clinic internal medicine physician Dr. Alexander Ryu to sort through dozens, or even hundreds, of pages of medical records.

Many patients visit the renowned clinic looking for a third or fourth opinion, carting with them unsorted documents from external health systems. A new artificial intelligence tool is helping clinicians parse through records faster — generating relevant patient summaries, organizing documents in chronological order and making them easier to search.

Ryu said the tool, called Record Time, can save him between five and 30 minutes of preparation per visit, depending on the complexity of the case. That’s time he can instead spend face-to-face with the patient. And Record Time helps ensure that he doesn’t miss important details that might be buried in the file that could drive treatment and testing recommendations.

“We receive a huge volume of these records, tens of millions of pages every year, and we needed a way to find important information in that,” said Ryu, who also serves as vice chair of innovation for the Mayo Clinic Department of Medicine.

Mayo Clinic physician Dr. Alexander Ryu helped to develop an AI system to better prepare doctors for patient visits. CNN

Health-related applications are considered among the most promising areas of innovation for AI. Google, OpenAI, Anthropic and others have rolled out health assistant chatbot features — and tens of millions of people are turning to AI for medical-related questions. Leaders in Silicon Valley also frequently make bold promises about AI curing cancer and other diseases, although those statements often sound more like marketing pitches given the big industry players are largely focused on other consumer and business applications.

But Record Time is just one way that Mayo Clinic, one of the world’s most well-known hospital systems, is using AI in hopes of improving patient care and, ultimately, saving lives. The hospital is partnering with firms like Microsoft and Scale AI to use its huge volume of patient records and research to develop AI tools.

There are now around 150 AI models deployed within the hospital, according to Dr. Matthew Callstrom, a radiologist and medical director of Mayo Clinic’s generative AI program.

The use of AI in healthcare settings is not without controversy, raising big questions about accuracy and patient privacy.

Mayo Clinic’s former Director of Research Operations Traci Tamiko Eto sued the hospital earlier this month, alleging she was retaliated against for raising privacy and oversight concerns around some Mayo AI systems.

Mayo Clinic spokesperson Andrea Kalmanovitz said the hospital doesn’t comment on ongoing litigation but noted it is “committed to the responsible development and deployment of AI, with privacy, security, transparency and compliance embedded throughout our processes.”

“Our research and clinical innovation are conducted in accordance with applicable laws and regulations and we remain steadfast in upholding the trust patients place in us and respecting their privacy,” Kalmanovitz said in a statement.

‘Potentially life changing’

Part of what makes AI so useful in health and medicine is that the technology excels at identifying trends in large swaths of data, said Jason Droege, CEO of Scale AI, which worked with Mayo Clinic to develop Record Time.

“AI can step in and do a lot of the tedious work that very specialized doctors or medical professionals do to speed up that process — get to more accurate diagnoses, faster so you can treat more people,” he said. “This is an industry where a lot of what doctors are doing, and nurses and others, is pattern recognition.”

Callstrom told CNN he was convinced of AI’s potential back in 2016, when he saw how AI could help radiologists identify subtle, early cancer warning signs in imaging.

Mayo Clinic is now running a clinical trial to test whether AI can help identify patients at risk of or with early-stage pancreatic cancer — an application the hospital has said could detect the disease years earlier than the typical diagnosis. Currently, patients often aren’t diagnosed with pancreatic cancer until it is regionally spread or metastatic, when the five-year survival rate hovers around 9%, Callstrom said.

Scale AI CEO Jason Droege discusses the company's partnership with Mayo Clinic to develop AI tools for health care. CNN

The hospital has also successfully used AI to analyze patients’ heart rhythms to tell whether someone could develop atrial fibrillation, a condition that can cause blood clots and strokes.

“For those patients where you actually find it and identify it, it’s potentially life changing,” Callstrom said.

Balancing speed and trust

To build AI tools, Mayo Clinic pairs tech experts with doctors or clinicians to decide which medical problems need tackling. A big part of Callstrom’s job is ensuring that Mayo Clinic’s AI tools are accurate and trusted by both patients and doctors.

AI tools go through the same process as a clinical trial, he said. First, it’s tested with a small group of patients with doctor oversight. The performance is measured, and then testing expands to a wider population.

Once a tool is rolled out broadly, Mayo Clinic continues to monitor how well it works.

“On the physician side … we’re skeptical a lot,” Callstrom said. “We give them the option to try (a new AI tool) and if they like it, they use it. If they don’t want to use it, they don’t have to. And the best measure of how well we’re doing is the adoption rate.”

Dr. Matthew Callstrom says AI shows huge potential for early disease detection, potentially saving patient lives. CNN

Callstrom also gets frequent questions from staff about what AI tools will mean for jobs. So far, he said, jobs aren’t going away. But they are changing.

The hospital’s nursing team, for example, helped to develop an AI system that listens and takes notes during patient visits. That potentially cuts in half the more than an hour per day they spend typing up those visits.

“What it’s doing is letting them spend more time talking to patients,” Callstrom said.

Scale’s Droege believes the health care industry is only at the beginning of realizing AI’s full potential — but that speed of adoption shouldn’t necessarily be the top priority.

“These predictions where everything is going to be fixed in a year or two, I think that’s wildly ambitious,” he said. “Quality of care is the bar, and then speed … in healthcare, you want to get it right, as fast as possible.”

One of the world’s most prominent hospitals is testing how AI can revolutionize health care

Every significant advancement in science starts with a fundamental inquiry: can this treatment enhance longevity, restore equilibrium, or significantly transform patient care? At Ipsen, we are adopting a similar methodology in the realm of artificial intelligence, questioning whether it can aid our teams in analyzing data in innovative ways, minimize repetitive tasks, and facilitate quicker, more informed decision-making focused on purposeful scientific outcomes.

To explore this potential, we have initiated a pilot program in collaboration with the Claude team specializing in healthcare and life sciences. This initiative aims to test theories within actual research and business processes, assess impacts, and gain insights swiftly while embedding a sense of responsibility from the ground up.

Launched in May 2026, this pilot involves more than 40 team members from Ipsen's Research & Development and Medical divisions, who are engaged in a specific set of use cases. These encompass external innovation evaluations, synthesizing scientific literature, generating knowledge, and enhancing clinical development workflows. The focus is on high-value tasks that require teams to sift through significant volumes of intricate data while consistently delivering high-quality results. Participants are utilizing resources such as Claude Science and Claude Code to explore the platform's capabilities.

When it comes to healthcare, the application of AI is just as critical as its functionalities. Accordingly, this pilot emphasizes strong governance, appropriate access controls, and responsible usage of these tools to ensure that teams engage with them ethically.

This initiative is not about adopting AI for its own sake. Ipsen's goal is to expedite the process of delivering medications to patients. The pilot serves as a deliberate examination of how AI can sustainably support this objective, grounded in scientific rigor. While we recognize that not every aspect of medication delivery can be hastened, optimizing decision-making processes and alleviating challenges in complex analytical tasks can lead to significant improvements.

Evaluating the theory: a targeted pilot for artificial intelligence in healthcare

A bright, sunlit hospital ward with a senior doctor and a nurse standing beside an adult patient’s bed, studying a tablet screen that shows colorful AI‐driven vitals and graphs. Behind them, a whiteboard reads “Pilot Study” surrounded by hand‐drawn charts and flow diagrams. The scene feels hopeful and focused, bathed in warm natural light.

Testing the hypothesis: a focused pilot for AI in healthcare - Ipsen

- Ipsen tests AI with Claude to enhance data analysis and decision-making in healthcare.  
- Over 40 Ipsen colleagues are involved in pilot focusing on high-value research activities.  
- The initiative ensures responsible AI use with governance and control measures in place.  

Every scientific breakthrough begins with a question: can this medicine extend life, restore balance, or meaningfully change what is possible for patients? At Ipsen, we are bringing that same discipline to artificial intelligence, asking whether it can help our teams analyse data in new ways, reduce rework and make better, faster decisions in pursuit of science with purpose.

This is the aim of our pilot with the Claude for healthcare and life science team: to test hypotheses in real research and business workflows, measure impact and learn quickly with responsibility embedded from the outset.

Launched in May 2026, the pilot brings together over 40 colleagues across Ipsen’s Research & Development and Medical organizations to work on a defined set of use cases spanning external innovation assessments, scientific literature synthesis, knowledge generation and clinical development workflows. The objective is to focus on high-value activities where teams routinely navigate large amounts of complex data and must produce consistent, high-quality outputs. Participants are exploring the platform using tools including Claude Science and Claude Code.



In healthcare, how AI is used matters as much as what it can do. This pilot is therefore being run with a strong focus on governance, appropriate access, ensuring the right controls are in place and that teams use these tools responsibly.



This is not AI for AI’s sake. Ipsen’s ambition is to bring medicines to patients sooner. This pilot is a focused test of where AI can support that ambition, responsibly and with scientific rigour. Not every step in the delivery of medicines to patients can be accelerated but improving how and when decisions are made and reducing friction in complex analytical work, can make a meaningful difference.

Testing the hypothesis: a focused pilot for AI in healthcare

For over a year, six schools operated by the UN Relief and Works Agency (UNRWA) have been cut off from access, while another six have shut down entirely, reported UN Spokesperson Stéphane Dujarric to reporters in New York.

Dujarric noted that ten schools in Area C of the West Bank have been abandoned due to ongoing settler violence and access limitations.

On Wednesday, UN Deputy Humanitarian Coordinator for the Occupied Palestinian Territory, Suzanna Tkalec, spearheaded a joint humanitarian effort visiting a former UN school in Gaza, which is currently sheltering 18 families displaced by conflict.

Dujarric emphasized that the living conditions for these families have been severely compromised due to limited humanitarian aid access. He stated, "Our humanitarian partners are dedicated to mobilizing resources to meet the community's most pressing needs."

While the Israeli military continues its actions in Gaza and the West Bank, humanitarian workers are steadfast in their commitment to provide essential assistance. In the last month, UN partner organizations have distributed over 378,000 essential items—including tarps, cleaning supplies, and water containers—to civilians in Gaza.

In June alone, the UN World Food Programme (WFP) extended food vouchers and cash assistance to around 286,000 individuals in the West Bank. Nonetheless, the UN raised concerns about potential shortages of necessary supplies as winter approaches without increased financial support. Dujarric warned, "Without additional funding, our dwindling stockpiles can't be replenished, putting vulnerable families at heightened risk, especially with winter approaching."

The World Health Organization (WHO) highlighted the necessity of establishing health-focused regulations for artificial intelligence (AI) during a press release on Wednesday. It urged that AI governance must work to alleviate health disparities rather than exacerbate them. This alert coincided with a high-level conference in Lisbon, co-hosted by the WHO and the Government of Portugal, where representatives from 37 countries gathered to discuss responsible AI implementation in healthcare.

According to an April report by WHO, while 74% of EU Member States utilize AI for diagnostic assistance, only 11% have developed a health-specific AI strategy and a mere 8% possess liability standards for AI mishaps. Dr. Hans Kluge, the WHO's regional director for Europe, commented on the global struggle for responsible AI governance, emphasizing the need for healthcare systems to prioritize patient needs over those who can afford the technology.

Dr. Kluge pointed out the urgency of AI regulation, noting that patients increasingly rely on chatbots for medical queries. He stated, "Regulating AI in health is challenging, but failing to do so carries risks for patients, trust, and equality."

In 2021, WHO released guidance on the ethical use of AI in healthcare, stressing the importance of employing the technology safely and ethically to benefit patients and communities. The conference aims to develop strategies that ensure equitable access to healthcare technology for underserved populations as AI becomes more prevalent.

At the recent 19th session of the UN Expert Mechanism on the Rights of Indigenous Peoples, UN human rights chief Volker Türk remarked that the transition to clean energy should not infringe upon the rights of Indigenous Peoples. He pointed out that Indigenous rights defenders frequently encounter threats, violence, and legal challenges, especially when opposing development initiatives affecting their lands.

Between 2023 and 2025, it is estimated that 15% of human rights defenders who are killed or forcibly disappeared will be Indigenous individuals, Türk reported. He called attention to cases such as Brooklyn Rivera, an Indigenous leader from Nicaragua who passed away in state detention, urging Member States to uphold Indigenous rights.

Türk also celebrated recent victories, such as an Indigenous group in Australia that achieved unprecedented compensation for mining activities carried out without their consent. He affirmed, "Rights are not distant goals; they are immediate obligations. Where these rights are protected, society becomes fairer and more resilient."

He highlighted that Indigenous Peoples are often at the forefront of efforts concerning biodiversity, climate resilience, and resource management, noting, "Sustainable peace relies on justice, participation, and the recognition of rights." He called for acknowledgment of Indigenous Peoples' rights to self-determination and land control, emphasizing the historical injustices they have faced.

Türk urged countries to support the UN Voluntary Fund for Indigenous Peoples and the Global Alliance for Human Rights, which promote Indigenous involvement in international discussions. "By working together, we can place human rights at the core of decision-making everywhere," he said.

Global News Summary: West Bank schools shut down, AI applications in healthcare, Indigenous rights issues

A triptych-style thumbnail:  
Left panel (dark theme): a deserted school entrance in the West Bank at dusk, its metal gate barred and weathered, a Palestinian flag draped over the fence, heavy clouds overhead.  
Center panel (natural light): a bright hospital ward where a doctor in scrubs reviews a glowing 3D brain scan on a sleek monitor, warm sunlight streaming through large windows.  
Right panel (natural light): an Indigenous elder in traditional dress standing atop a grassy hill at sunrise, holding a hand-woven rights banner as a small group of supporters listens.

World News in Brief: Schools closed in West Bank, AI in healthcare, Indigenous rights - UN News

- Six UNRWA schools in the West Bank remain closed due to settler attacks and access issues.  
- Humanitarian aid continues in Gaza despite military strikes, reaching thousands with essential supplies.  
- WHO urges global AI health regulations to ensure equitable access and safe patient care.  

For more than a year, six schools established by the UN Relief and Works Agency (UNRWA) have been inaccessible, and another six have been closed, UN Spokesperson Stéphane Dujarric told journalists in New York.

Ten schools in Area C of the West Bank were recently abandoned due to settler attacks and access restrictions, he added.

On Wednesday, Suzanna Tkalec, the UN’s deputy humanitarian coordinator for the Occupied Palestinian Territory, led a joint humanitarian mission to a former UN school in Gaza currently hosting 18 displaced families.

Mr. Dujarric said that according to the UN aid coordination office, OCHA, those families’ living conditions have been undermined by a lack of access to humanitarian aid.

“Our humanitarian partners are committed to mobilizing assistance to address the community’s most urgent needs,” he said.

Aid reaches thousands in the Occupied Palestinian Territory

Even as the Israeli military continues to strike Gaza and launch raids into the West Bank, humanitarian workers continue to provide lifesaving assistance.

The UN’s partner organizations have distributed more than 378,000 items, including tarpaulins, cleaning kits and jerrycans to civilians in Gaza in the past month.

In the West Bank, the UN World Food Programme (WFP) provided food vouchers and cash assistance to about 286,000 people in June.

The UN is concerned, however, that the required supplies may not be available by the winter season without increased support.

“Without additional funding, depleted stockpiles cannot be replenished, putting vulnerable families at even greater risk, particularly as humanitarians prepare for winter,” Mr. Dujarric said.

© WHO/Uka Borregaard

WHO urges countries to develop health-specific AI regulations

Artificial intelligence (AI) governance strategies must ensure the technology remedies health inequities in access and treatment, rather than amplifying them, the World Health Organization (WHO) said on Wednesday in a press release.

The warning comes as the UN continues its push to bring Member States together around global AI governance. The WHO and the Government of Portugal kicked off a high-level conference in Lisbon on Wednesday, hosting government representatives from 37 countries across all six WHO regions to share strategies for responsible AI use in healthcare.

In an April report, the WHO found that 74 per cent of European Union (EU) Member States already used AI to assist with diagnostics, but only 11 per cent had a health-specific AI strategy and just eight per cent had liability standards for AI in case something goes wrong.

“Every country in the world is wrestling with the same questions right now: how do we govern AI responsibly, how do we build the health workforce to use it safely, and how do we make sure it serves patients rather than just those who can afford the technology,” said Dr. Hans Kluge, WHO regional director for Europe.

Regulation push prioritizes patients

In the press release, Dr. Kluge said part of the reason AI regulation is so urgent is that patients are already using chatbots to ask about their symptoms.

“Regulating AI in health is hard,” he said. “But not regulating it is harder – measured in patients harmed, trust lost and inequalities widened.

The WHO released guidance on the ethical use of AI in healthcare in 2021, focusing on how it can be used safely and ethically to advance the interests of patients and communities

A key focus of the conference is to develop strategies to ensure that patients with less access to the healthcare system and to technology are not left behind as countries and providers adopt AI tools

“No country should have to navigate the AI transformation alone,” the WHO wrote.

© Unsplash/Gustavo Leighton

Indigenous rights activists face disproportionate dangers

The world’s transition to clean energy must not come at the cost of Indigenous Peoples’ rights, UN human rights chief Volker Türk said on Wednesday at the 19th session of the UN-appointed Expert Mechanism on the Rights of Indigenous Peoples.

Indigenous rights defenders often face intimidation, harassment, violence, criminalisation and prosecution, especially as they defend their land from development projects such as mining and large-scale agriculture.

Between 2023 and 2025, at least 15 per cent of the human rights defenders killed or forcibly disappeared around the world were Indigenous Persons, according to the human rights chief.

Mr. Türk highlighted the case of Brooklyn Rivera, an Indigenous leader in Nicaragua who died in state custody in May, and called for all Member States to respect Indigenous rights.

He also highlighted recent successes such as an Indigenous group in Australia that in May secured record compensation for mining conducted without their consent.

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“Indigenous Peoples’ rights are not theoretical aspirations for the future; they are obligations for the present, now,” Mr. Türk said. “And wherever those rights are respected, all of society becomes more just, more peaceful and more resilient.”

Highlighting Indigenous contributions

Indigenous Peoples around the world lead efforts on biodiversity conservation, climate resilience, resource management, peacebuilding and human rights.

“Durable peace requires inclusion, justice, meaningful participation at every stage and recognition of rights,” he said. “And this must involve recognition of Indigenous Peoples’ rights to self-determination and to control over their lands.”

Majority populations have historically dispossessed Indigenous communities of land and resources, denied them self-determination, degraded their environments, and threatened their governance systems and cultures, human rights chief noted.

Mr. Türk urged Member States to support the UN Voluntary Fund for Indigenous Peoples and the Global Alliance for Human Rights, both of which strengthen Indigenous participation in international fora.

“Together we can work to place all human rights at the centre of decision-making everywhere,” he said.

World News in Brief: Schools closed in West Bank, AI in healthcare, Indigenous rights

The healthcare sector stands as one of the most vital industries within our society and presents a significant opportunity for the integration of artificial intelligence (AI). This integration aims to enhance support for clinicians, improve patient outcomes, and streamline operations within health systems. Despite the rapid advancements of AI technology, implementing it effectively within real clinical environments continues to prove challenging. A recent study indicates that 77% of health systems identify underdeveloped AI tools as a primary obstacle to their integration. In response, Scale is collaborating with Mayo Clinic to create and implement AI solutions that are specifically tailored to enhance clinical care.

Transforming Healthcare Through AI

This partnership is setting the stage for a comprehensive shift toward AI-driven healthcare solutions, emphasizing the importance of reliability, precision, and practicality. Utilizing the Scale Generative AI Platform, our joint efforts are concentrating on three critical areas:

1. **Enhancing Clinical Processes**: By minimizing the time clinicians spend reviewing patient health records before initial consultations, healthcare professionals can allocate more focus to patient interaction rather than paperwork. Since the initiative began, healthcare providers have increased their one-on-one time with patients by an average of eleven minutes, all while sustaining a high standard of care.

2. **Increasing Patient Safety and Quality of Care**: The initiative aims to automate the identification of vital safety incidents, such as wrong-site surgeries or serious patient falls, which often get lost amid routine reporting. This automation reduces the need for extensive manual oversight by administrative teams.

3. **Maximizing Healthcare Workers' Licenses**: By alleviating administrative burdens, the collaboration enhances continuous patient care while laying the groundwork for future AI innovations.

Matthew R. Callstrom, M.D., Ph.D., who serves as the medical director of the AI program at Mayo Clinic, stated, “This initiative is fundamentally about leveraging innovation to provide patients with more timely, coordinated, and compassionate care. Our collaboration merges the strengths of both organizations to enhance care delivery and improve patient experiences.”

Jason Droege, CEO of Scale AI, praised Mayo Clinic’s commitment to patient-centered innovation, remarking, “Mayo Clinic exemplifies the future of patient-focused healthcare, and we are excited to partner with them in developing reliable AI systems that not only assist clinicians but also lead to improved patient outcomes.”

Paving the Way for AI in Healthcare

These targeted initiatives represent crucial early milestones in the application of AI to address real clinical challenges. We aim to deliver tangible benefits for healthcare providers and patients alike while establishing a robust foundation for future advancements in the field. Committed to maintaining privacy and compliance, all data will remain secure within Mayo Clinic's HIPAA-compliant framework. Furthermore, our systems are built with the highest standards for data security and regulatory compliance in mind. As our collaboration progresses, we are dedicated to advancing meticulously evaluated AI systems that enhance care delivery and transform the operations of the healthcare sector.

To discover more about how AI is reshaping healthcare, visit scale.com/enterprise/healthcare.

Scale AI and Mayo Clinic Revolutionizing Clinical Care

A sunlit hospital examination room with warm, natural light flooding through tall windows. A physician in scrubs gently taps a sleek glass touchscreen on a stainless steel desk, revealing a richly detailed 3D organ model with color-coded diagnostic overlays. The glass display bears a prominent Scale AI logo etched into its frame, while the Mayo Clinic shield hangs on the wall behind, softly illuminated by sunlight. The scene conveys a harmonious blend of cutting-edge AI and clinical expertise transforming patient care.

How Scale AI and Mayo Clinic Are Transforming Clinical Care - Scale AI

- AI is being deployed to improve clinical care and support healthcare systems effectively.  
- Collaboration with Mayo Clinic focuses on reliable AI solutions to enhance patient outcomes.  
- Initial efforts streamline clinical processes and enhance patient safety while ensuring data security.  

Healthcare is one of the most important industries in our society, and one of the most consequential places to apply AI. In practice, this means supporting clinicians, improving patient outcomes, and helping health systems operate more effectively. While AI has advanced quickly, deploying it in real clinical settings remains a challenge. In fact, 77% of health systems cite immature AI tools as a significant barrier to adoption based on a 2025 study. That’s why Scale is working with Mayo Clinic to develop and deploy AI applications designed to meaningfully improve clinical care.

Driving AI Transformation in Healthcare

The collaboration establishes a framework for AI transformation, focusing on solutions that are reliable, accurate, and helpful. Together, and powered by the Scale Generative AI Platform, we are prioritizing three key areas:

Streamlining Clinical Processes : Reducing clinician review time of patients' health records before the initial consultation, allowing doctors and nurses to focus on direct care rather than manual documentation. Since launch, doctors are spending an average of eleven more minutes with each patient while reliably maintaining an expert standard of care for patients.

: Reducing clinician review time of patients' health records before the initial consultation, allowing doctors and nurses to focus on direct care rather than manual documentation. Since launch, doctors are spending an average of eleven more minutes with each patient while reliably maintaining an expert standard of care for patients. Enhancing Patient Safety and Quality of Care : Automating the detection of critical safety events, such as wrong-site surgeries or serious falls, hidden within routine reporting noise, reducing administrative manual review.

: Automating the detection of critical safety events, such as wrong-site surgeries or serious falls, hidden within routine reporting noise, reducing administrative manual review. Helping Mayo Healthcare Workers Operate at Top of License: Reducing administrative tasks, improving 24/7 patient care, and establishing a scalable foundation for future AI applications.

“Ultimately, this work is about helping patients by using innovation to deliver more timely, coordinated and compassionate care,” said Matthew R. Callstrom, M.D., Ph.D., medical director, AI program at Mayo Clinic. “This collaboration combines each organization’s expertise to advance care delivery and patient experience.”

“Mayo Clinic has been a model for what patient-centered innovation should look like, and it’s why they’re at the forefront of modern healthcare. We’re proud to work alongside their teams to build reliable AI systems that support clinicians and deliver better outcomes for patients.” – Jason Droege, CEO of Scale AI.

Charting a New Path for AI in Healthcare

These initial areas of focus mark important early steps in applying AI to real clinical needs. This work is designed to deliver practical value for clinicians and patients today, while creating a foundation for broader innovation over time. All of this is grounded in privacy and compliance: data remains within Mayo Clinic's secure, HIPAA-compliant environment, and our systems are built from the ground up to meet the highest standards of data security and regulatory oversight. As our efforts evolve, we will continue advancing rigorously evaluated AI systems that support care delivery and improve how the healthcare industry operates.

Visit us at scale.com/enterprise/healthcare to learn more about how AI is transforming healthcare.

Healthcare embraces AI while still addressing regulatory challenges.

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