AI Regulations for Healthcare Professionals

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 Regulatory Challenges: Existing regulatory frameworks, like the FDA's 510(k) device clearance process and HIPAA, are outdated and were not designed for modern AI technologies.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

Who's liable when AI gets it wrong in healthcare? It's the question I hear from physicians constantly: "If I follow the AI recommendation and it's wrong, am I liable? If I ignore it and it was right, am I also liable?" Europe might be changing the answer. Three new EU regulations are shifting AI liability from a "prove harm after the fact" model to a "prove safety upfront" approach: 🔹 EU AI Act: High-risk healthcare AI must meet strict safety requirements by 2026 🔹 EU Product Liability Directive: Software developers now treated like device manufacturers—if your AI doesn't meet safety standards and causes harm, you're presumed liable 🔹 European Health Data Space: Mandates data sharing for AI development with €20M+ fines for non-compliance Similarly, the UK just classified AI ambient scribes as medical devices requiring full regulatory approval. The shift: Instead of physicians wondering "did I make the right clinical decision with this AI?" the question becomes "did the AI company prove their system was safe before I used it?" Even if you're not in Europe, these standards often become global norms (think GDPR). Read more here: https://lnkd.in/gWSYVQn8

https://lnkd.in/g5ir6w57 The European Union has adopted the AI Act as its first comprehensive legal framework specifically for AI, effective from July 12, 2024. The Act is designed to ensure the safe and trustworthy deployment of AI across various sectors, including healthcare, by setting harmonized rules for AI systems in the EU market. 1️⃣ Scope and Application: The AI Act applies to all AI system providers and deployers within the EU, including those based outside the EU if their AI outputs are used in the Union. It covers a wide range of AI systems, including general-purpose models and high-risk applications, with specific regulations for each category. 2️⃣ Risk-Based Classification: The Act classifies AI systems based on their risk levels. High-risk AI systems, especially in healthcare, face stringent requirements and oversight, while general-purpose AI models have additional transparency obligations. Prohibited AI practices include manipulative or deceptive uses, though certain medical applications are exempt. 3️⃣ Innovation and Compliance: To support innovation, the AI Act includes provisions like regulatory sandboxes for testing AI systems and exemptions for open-source AI models unless they pose systemic risks. High-risk AI systems must comply with both the AI Act and relevant sector-specific regulations, like the Medical Device Regulation (MDR) and the In Vitro Diagnostic Medical Device Regulation (IVDR). 4️⃣ Global Impact and Challenges: The AI Act may influence global AI regulation by setting high standards, and its implementation within existing sector-specific regulations could create complexities. The evolving nature of AI technology necessitates ongoing updates to the regulatory framework to balance innovation with safety and fairness.

 #AI is moving into many aspects of healthcare. Policy and regulation has been lagging behind-- understandable looking at the rate in which AI is being developed with possible application in healthcare. Some important legislation is in the works. The Health Tech Investment Act 2025 (https://lnkd.in/giC4H3KF) could be a pivotal policy milestone in how the U.S. healthcare system leverages AI-driven clinical services — especially under the Medicare program. This legislation supports reimbursement for “algorithm-based healthcare services” — defined as services delivered through FDA-cleared or approved devices using AI or machine learning — via the hospital outpatient prospective payment system (OPPS). Key Provisions Include: 🟢 Establishes a new technology payment classification (APC) for AI-enabled tools starting in January 2026. 🟢 Payment will be based on actual technology costs, including software subscriptions, staff time, and overhead — as reported by the manufacturer. 🟢 Guarantees at least 5 years of payment protection for each approved AI tool before reassignment to a standard APC, allowing time for adoption and claims data to accumulate. 🟢 Expands eligibility to include adjunctive AI services used alongside existing care modalities (e.g., AI overlays in radiology or cardiology). Codifies SaaS reimbursement rules for cloud-based diagnostics dating back to January 2023. Payment for some AI-enabled medical devices already exists: -The American Medical Association has created 16 Category III CPT codes to report AI-enabled services, with 7 new codes coming on-line for 2025, covering: AI-assistance in ECG and echocardiogram interpretation, chest imaging and prostate pathology. -Centers for Medicare & Medicaid Services has also granted New Technology Add-On Payments (NTAPs) for select AI platforms such as: Viz.ai for stroke detection, Caption Health for AI-guided cardiac ultrasound. Together, these coding and payment pathways represent a growing foundation — but today’s environment remains fragmented, inconsistent, and limited in scope. What is missing from this legislation? Despite financial progress, the bill does not address clinical oversight, bias mitigation, or transparency standards for AI in practice. Missing: 📍No requirement for clinical validation across diverse populations 📍No mandated post-market monitoring 📍No framework for managing rapid algorithm updates or drift (maintenance) The bill accelerates reimbursement — a key enabler of adoption — but we need complementary policies from CMS and FDA. We risk paying for tools before ensuring they are safe, equitable, and clinically effective. Should CMS prioritize payment before we have clear, shared standards for safety and implementation? How could we strike a fair and just balance? #UsingWhatWeHaveBetter