Governance Issues in Medical AI

Building AI for healthcare is the straightforward part. Governing it after deployment is where the real challenge begins. The Medicines and Healthcare products Regulatory Agency published a piece by Jennifer Dixon, CEO of The Health Foundation. It gets to the heart of why AI regulation in medicine is fundamentally different from any other technology domain. The MHRA is strengthening its approach to regulating adaptive AI, enhancing both pre-market evaluation and robust post-market surveillance. Furthermore, ensuring that safety, performance and equity remain central as technologies evolve in real-world settings. The key word there is adaptive. Most regulatory frameworks were designed for static products. A device that works a certain way when approved continues to work that way. AI systems learn, shift, and adapt in production. The National Commission for the Regulation of AI in Healthcare is addressing questions such as whether the AI model is safe and accurate. They're also looking at whether it continues to be safe as it adapts in a real world setting. That second question is the one nobody has fully solved yet. Jennifer Dixon raises questions beyond technical performance, including whether an AI application is usable as intended and acceptable to both clinicians and patients. She also highlights concerns around fairness, as well as the risk of unsafe or biased workarounds emerging in practice. This matters enormously for public sector AI deployment beyond healthcare. Every government AI system deployed in a regulated environment faces the same fundamental tension. The approval was granted for a system at a point in time. The system continues to evolve, learn, and behave differently from what was originally validated. The governance frameworks being built for healthcare AI will become the template for every high stakes AI deployment across government. Getting this right in the NHS will shape how AI is governed in justice, welfare, and national security for the next decade. The challenge with AI is not building it. It is governing it in production when the world it operates in keeps changing. A great piece worth reading from Dame Jennifer Dixon linked below. How is your organisation approaching the governance of AI systems that evolve after deployment?

A lesson from self-driving cars… Healthcare's AI conversation remains dangerously incomplete. While organizations obsess over provider adoption, we're neglecting the foundational element that will determine success or failure: trust. Joel Gordon, CMIO at UW Health, crystallized this at a Reuters conference, warning that a single high-profile AI error could devastate public confidence sector-wide. His point echoes decades of healthcare innovation: trust isn't given—it's earned through deliberate action. History and other industries can be instructive here. I was hoping by now we’d have fully autonomous self-driving vehicles (so my kids wouldn’t need a real driver’s license!), but early high-profile accidents and driver fatalities damaged consumer confidence. And while it’s picking up steam again, but we lost some good years as public trust needed to be regained. We cannot repeat this mistake with healthcare AI—it’s just too valuable and can do so much good for our patients, workforce, and our deeply inefficient health systems. As I've argued in my prior work, trust and humanity must anchor care delivery. AI that undermines these foundations will fail regardless of technical brilliance. Healthcare already battles trust deficits—vaccine hesitancy, treatment non-adherence—that cost lives and resources. AI without governance risks exponentially amplifying these challenges. We need systematic approaches addressing three areas:   Transparency in AI decision-making, with clear explanations of algorithmic conclusions. WHO principles emphasize AI must serve public benefit, requiring accountability mechanisms that patients and providers understand.   Equity-centered deployment that addresses rather than exacerbates disparities. There is no quality in healthcare without equity—a principle critical to AI deployment at scale.   Proactive error management treating mistakes as learning opportunities, not failures to hide. Improvement science teaches that error transparency builds trust when handled appropriately. As developers and entrepreneurs, we need to treat trust-building as seriously as technical validation. The question isn't whether healthcare AI will face its first major error—it's whether we'll have sufficient trust infrastructure to survive and learn from that inevitable moment. Organizations investing now in transparent governance will capture AI's potential. Those that don't risk the fate of other promising innovations that failed to earn public confidence. #Trust #HealthcareAI #AIAdoption #HealthTech #GenerativeAI #AIMedicine https://lnkd.in/eEnVguju

🚨 The biggest threat in AI + healthcare isn’t bad algorithms. It’s unintegrated deployment. Healthcare doesn’t fail because models are inaccurate. It fails when intelligence outruns trust. Right now, I’m seeing the same pattern repeat across hospitals and startups: • Brilliant models • Weak governance • Rushed adoption • Clinicians sidelined • Patients unaware That’s not innovation. That’s risk acceleration. AI in healthcare isn’t a software problem it’s a human systems problem. Here’s the hard truth most teams miss: 🧠 You cannot deploy healthcare AI with only logic and speed. You must deploy it with ethics, safety, and presence at the same time. I use a Whole-Brain framework to evaluate every AI implementation: 🧩 Architect — Does it work reliably in real clinical workflows? 🛡️ Guardian — Is harm, bias, and accountability explicitly governed? ⚡ Catalyst — Does it solve a real clinical problem fast enough to matter? 👁️ Witness — Does it preserve trust, dignity, and human judgment? If any one of these is missing, the system will fail not technically, but socially. And in healthcare, loss of trust is more dangerous than model error. 🔴 The real threat is not “AI replacing clinicians.” 🔴 The real threat is AI eroding safety, equity, and accountability quietly. My rule is simple: No healthcare AI goes live unless all four domains are satisfied. Because: • If clinicians can’t override it, it’s unsafe • If patients don’t know it’s there, it’s unethical • If equity isn’t tested, harm is guaranteed • If accountability is unclear, trust will collapse 🚀 The future of healthcare AI won’t be built by faster models alone. It will be built by whole systems designed for humans. Healthcare AI must be accurate. But more importantly — it must be trusted. And trust is not a feature. It’s an outcome of how we choose to build. Harvey Castro, MD, MBA. #DrGPT Follow for AI + healthcare systems thinking #AIinHealthcare #HealthTech #DigitalHealth #AIethics #ClinicalAI #Leadership #DrGPT Inspired by Whole Brain Living Jill Bolte Taylor.

Imagine going to see your oncologist for a cancer risk consultation. The kind of appointment where you expect decades of medical training, clinical reasoning, careful judgment to converge. Instead, you watch your oncologist open ChatGPT. Not as a reference tool. Not as a second opinion. As the primary decision-making system. That happened to my friend a few days ago. The doctor used ChatGPT to look up cancer risk calculation methods, typed in her patient information to estimate risk levels, asked for medication options, checked side effects, and confidently prescribed medication based on the chatbot's output. My friend sat there thinking: "Is my cancer prevention plan being generated by the same tool people use to write vacation itineraries?" She works in #AI. She builds AI! She loves the #technology. But this crossed a line... Because this was NOT: 🥼 Evidence-based medicine 🏥 An FDA-approved clinical decision support system 🧮 A validated risk calculator ⚕️ A regulated medical device This was ChatGPT, an unregulated chatbot, being used to calculate disease risk and prescribe medication. AI #governance can seem abstract. Risk here, hypothetical scenario there. But when something like this happens between two humans, between two professionals? That's no longer abstract. That's personal. Algorithms are personal. AI Governance is not abstract, it is concrete, it is real. I spent a year as AI Policy Advisor to Senator Bill Cassidy, Chairman of the Senate HELP Committee. I sat in meetings about #healthcare AI regulation. I attended conferences on clinical AI deployment. And then I hear about this oncologist, and I realize: the governance gap isn't closing fast enough. Because if clinicians are already offloading risk calculations to general-purpose LLMs, relying on unvalidated outputs for medication choices, using consumer AI tools instead of FDA-cleared systems, then we're not "integrating AI into healthcare." We're outsourcing clinical judgment to systems never designed for medicine and hoping no one gets hurt. What should have happened: Before any AI tool touches patient care, health systems need: Risk classification (Is this high-stakes clinical decision-making? Vendor evaluation (FDA clearance, clinical validation, audit trails) Usage policies (Clear boundaries on what AI can/cannot be used for) Oversight mechanisms (Who's monitoring? What happens when things go wrong?) The oncologist probably thought she was being efficient. Innovative, even. But efficiency without safety isn't innovation. It's risk. This is exactly why I created my first LinkedIn Learning course. Because these mistakes are happening right now, in exam rooms, in boardrooms, in decisions that affect real people's lives. AI will absolutely transform medicine. But it has to do so safely, intentionally, and responsibly. Not accidentally. Let's make sure your AI adoption doesn't become the next viral cautionary tale. #aigovernance #risk #healthcare

🔥 THE HIDDEN RISK IN YOUR AI STRATEGY: What D&O Carriers, Bond Counsel, and Regulators Already See Every health system in the country is deploying AI. But most are doing it without the governance infrastructure needed to protect the institution when something goes wrong. And that gap is no longer theoretical: 📉 D&O carriers now ask for evidence of AI governance at renewal 🏦 Moody’s has named weak AI governance a credit risk factor 📑 Bond counsel is raising traceability and covenant‑compliance questions ⚖️ Class‑action lawsuits over ungoverned clinical AI have already begun The exposure is real, financial, and personal — for every board member and senior executive. 🧭 What This Session Is Actually About This is not a talk about AI technology. It’s about the governance architecture that determines whether your institution can prove, on demand, that it exercised proper oversight when something inevitably goes wrong. It is the difference between a board that can defend its duty of oversight under #Caremark and a board that cannot. 🏛️ The Two Layers Every Health System Needs Most organizations lack both essential layers: ⚙️ The Technical Layer 🏛️ The Institutional (Board‑Level) Layer • Technical compliance without real board accountability will not satisfy D&O carriers. • Board accountability without technical scaffolding will not satisfy the FDA. 👉 You need both. The TTIC | Trustworthy Technology and Innovation Consortium (TTIC) has built the operational artifacts that translate these requirements into something a health system can implement before the next renewal cycle, regulatory inquiry, or material event. 📌 What We’ll Cover Drawing on field intelligence from CIOs in #TTIC’s standards‑operationalization program, this session explores: 💹 The three forces pricing the AI governance gap right now 🏥 Why AI governance is not IT’s job, and how to structure a governance triad across clinical leadership, technology, and finance/legal that creates shared accountability (not shared blame) 🗂️ The two‑standard architecture that produces an auditable governance record as a byproduct of normal operations 🔒 What Level 3 means, and why it matters Nas Panwar Agenda: https://lnkd.in/gRmpURMz

New Guidance Alert: Joint Commission + Coalition for Health AI (CHAI) just released their framework on Responsible Use of AI in Healthcare. Why it matters: This document lays out a playbook for responsible AI adoption as hospitals assess the cambrian explosion of AI tooling. 5 Highlights from the Guidance: ✔️AI Governance Structures – Formal boards and cross-functional teams must oversee AI use, with accountability up to the C-suite and board. ✔️Patient Privacy & Transparency – Clear disclosures to patients about when/how AI is used in their care. ✔️Data Security & Use Protections – Encryption, minimization, and strict vendor agreements are non-negotiable. ✔️Ongoing Quality Monitoring – Post-deployment validation and bias checks to catch drift and ensure safety. ✔️Voluntary AI Safety Reporting – Confidential, blinded incident reporting to foster shared learning without stifling innovation. 👉 Ramifications: ✔️ Hospitals: Expect AI oversight to mirror clinical governance—this isn’t IT-only. Prepare for board-level accountability, training programs, and continuous monitoring. ✔️AI SaaS Vendors/Builders: Hospitals will demand transparency, model cards, monitoring dashboards, and contractual guardrails. Compliance is no longer optional. Read more: https://lnkd.in/eeJMEPxH

Should we really trust AI to manage our most sensitive healthcare data? It might sound cautious, but here’s why this question is critical: As AI becomes more involved in patient care, the potential risks—especially around privacy and bias—are growing. The stakes are incredibly high when it comes to safeguarding patient data and ensuring fair treatment. The reality? • Patient Privacy Risks – AI systems handle massive amounts of sensitive information. Without rigorous privacy measures, there’s a real risk of compromising patient trust. • Algorithmic Bias – With 80% of healthcare datasets lacking diversity, AI systems may unintentionally reinforce health disparities, leading to skewed outcomes for certain groups. • Diversity in Development – Engaging a range of perspectives ensures AI solutions reflect the needs of all populations, not just a select few. So, what’s the way forward? → Governance & Oversight – Regulatory frameworks must enforce ethical standards in healthcare AI. → Transparent Consent – Patients deserve to know how their data is used and stored. → Inclusive Data Practices – AI needs diverse, representative data to minimize bias and maximize fairness. The takeaway? AI in healthcare offers massive potential, but only if we draw ethical lines that protect privacy and promote inclusivity. Where do you think the line should be drawn? Let’s talk. 👇

The biggest barrier to AI transforming healthcare? It’s not the tech. Before we can fully benefit from AI in healthcare, we have to confront these issues. Because AI in HC is not without challenges. And understanding the challenges is critical to unlocking AI’s full impact in HC. Here are some of the challenges that need to be addressed before we can fully benefit. (And yes, there are more than these). 𝗘𝘁𝗵𝗶𝗰𝗮𝗹: - Bias AI may perpetuate or amplify biases in HC data, leading to unequal care across demographics. - Impact on Patient-Provider Relationship The use of AI may reduce human interaction, empathy, and personalized care, potentially dehumanizing HC. - Environmental and Social Implications AI consume significant resources and energy, raising ethical questions about environmental sustainability and the social consequences of workforce displacement. 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹: - Integration with Legacy Systems Difficulty in connecting AI tools with outdated IT infrastructure. - Handling Unstructured Data Large volumes of data are unstructured and hard for AI to analyze. - AI Hallucinations and Reliability Issues AI models sometimes generate incorrect or fabricated outputs that can mislead clinical decisions. 𝗠𝗲𝗱𝗶𝗰𝗮𝗹: - Managing Increased Demand from AI-Driven Diagnostics AI-enhanced disease detection may increase demand for follow-up tests and interventions, potentially overwhelming HC capacity. - Clinical Scope and Generalizability AI models may have limited applicability outside the specific clinical contexts or patient populations they were trained on. - Alignment with Local Care Practices AI systems need to be adapted to the unique workflows, protocols, and standards of care specific to each HC setting or region. 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆: - Need for Adaptive and Forward-Looking Regulation Current regulations lag behind AI innovation, creating gaps in oversight and compliance. - Governance of AI Use by Clinicians and Patients GenAI tools like ChatGPT are increasingly used by clinicians and patients without clear policies or training. - Liability and Accountability in AI-Driven Care Who is legally responsible when AI systems cause patient harm remains unclear and complex. 𝗧𝗿𝘂𝘀𝘁:  - Workforce Resistance Distrust in AI due to fears of job displacement or lack of transparency. - Transparency and Explainability Many AI tools make it difficult for clinicians and patients to understand how decisions are made. - Reliability and Performance Over Time AI models may become less accurate over time. 𝗗𝗮𝘁𝗮: - Limited Digitalization of HC Data A lot of HC data is not digitized, limiting AI’s access to comprehensive information. - Data Quality and Accuracy HC data often contains errors, inconsistencies, missing values, and outdated information. - Data Privacy and Security HC data is highly sensitive, raising concerns about unauthorized access and breaches. What challenges would you add to the list? 

This article from July, 15 reports on a closed-door workshop organized by the Stanford Institute for Human-Centered Artificial Intelligence (HAI) in May 2024, where 55 leading policymakers, academics, healthcare providers, AI developers, and patient advocates gathered to discuss the future of healthcare AI policy. The main focus of the workshop was on identifying gaps in current regulatory frameworks and fostering support for necessary changes to govern AI in healthcare effectively. Key Points Discussed: 1.) AI Potential and Investment: AI has the potential to revolutionize healthcare by improving diagnostic accuracy, streamlining administrative processes, and increasing patient engagement. From 2017-2021, the healthcare sector saw significant private AI investment, totaling $28.9 billion. 2.) Regulatory Challenges: Existing regulatory frameworks, like the FDA's 510(k) device clearance process and HIPAA, are outdated and were not designed for modern AI technologies. These regulations struggle to keep up with the rapid advancements in AI and the unique challenges posed by AI applications. 3.) The workshop focused on 3 main areas: - AI software for clinical decision support. - Healthcare enterprise AI tools. - Patient-facing AI applications. 4.) Need for New Frameworks: There was consensus among participants that new or substantially revised regulatory frameworks are essential to effectively govern AI in healthcare. Current regulations are like driving a 1976 Chevy Impala on modern roads, and are inadequate for today's technological landscape. The article emphasizes the urgent need for updated governance structures to ensure the safe, fair, and effective use of AI in healthcare. The article describes the 3 use cases discussed: Use Case 1: AI in Software as a Medical Device - AI-powered medical devices face challenges with the FDA's clearance, hindering innovation. - Workshop participants suggested public-private partnerships for managing evidence and more detailed risk categories for different AI devices. Use Case 2: AI in Enterprise Clinical Operations and Administration - Balancing human oversight with autonomous AI efficiency in clinical settings is challenging. - There is need for transparent AI tool information for providers, and a hybrid oversight model. Use Case 3: Patient-Facing AI Applications - Patient-facing AI applications lack clear regulations, risking the dissemination of misleading medical information. - Involving patients in AI development and regulation is needed to ensure trust and address health disparities. Link to the article: https://lnkd.in/gDng9Edy by Caroline Meinhardt, Alaa Youssef, Rory Thompson, Daniel Zhang, Rohini Kosoglu, Kavita Patel, Curtis Langlotz

The EU dropped a 241-page deep dive on AI in healthcare… …and it basically says: “AI has potential, but deployment across EU Member States remains limited.” The Directorate General for Health and Food Safety (DG SANTE) has released a 241-page study assessing the current state of AI integration into EU healthcare systems, including opportunities, challenges, and recommendations for future action. Key observations from the report: AI-based solutions are already available that can optimise resource allocation, improve diagnostic accuracy, streamline administrative processes, and support treatment planning. Despite this potential, deployment across EU Member States remains limited. The primary obstacles identified are: • Lack of standardisation and interoperability of health data • Fragmented and complex regulatory requirements (MDR, IVDR, AI Act, EHDS) • Limited funding and sustainable financing mechanisms • Low levels of trust and digital health literacy among patients and healthcare professionals • Insufficient local performance validation and post-deployment monitoring The study notes that countries such as the USA, Israel, and Japan have advanced further in real-world AI deployment, implementing large-scale pilots that have already reduced diagnosis times, improved patient flow, and generated measurable cost savings. To enable safe, effective, and ethical deployment of AI in healthcare, the report proposes: - Establishing EU-wide standards for data governance and interoperability - Creating centres of excellence for AI in healthcare - Introducing consolidated funding mechanisms - Requiring local real-world performance assessment before large-scale deployment - Developing a catalogue of certified AI solutions for healthcare Potential impact on our industry For AI-enabled medical devices, the report sends a clear market signal: - Higher evidence expectations: Manufacturers will need robust local performance validation, comprehensive post-market monitoring, and clear demonstration of real-world clinical value in EU healthcare settings. - Integrated compliance: Strategies must cover both horizontal frameworks (AI Act, GDPR) and sector-specific regulations (MDR, IVDR), embedding AI transparency, data governance, and clinical performance requirements into technical documentation from the start. - Early clinical partnerships: Working with healthcare providers early will be key to demonstrating clinical and operational benefits, building trust, and supporting adoption. - Competitive advantage through proof: Companies that combine regulatory compliance with measurable workflow efficiency gains will be well-positioned in a market that remains underpenetrated. 👇 The full report is available just below. Sharing it with your colleagues. This will help them anticipate upcoming expectations for AI-enabled medical devices and prepare accordingly. ✌️ Peace, Hatem Your Clinical Evaluation Expert & Partner