AI in Healthcare

13 academic & grey-literature sources bearing on this topic — catalogued metadata with a primary link; one-line findings are ✦ AI-generated summaries, labeled as such (charter §7.9). Browse the full literature index.

• Current state of Food and Drug Administration-approved artificial intelligence/machine learning medical devices: pathways, transparency, and evidence gaps ↗Peer-reviewed✦ AIDocuments that most FDA AI/ML devices clear via the 510(k) pathway with limited clinical validation and poor transparency, exposing regulatory evidence gaps.
• Unregulated large language models produce medical device-like output ↗Peer-reviewed✦ AIFinds general-purpose LLMs 'readily produced device-like decision support across a range of scenarios,' implying they should fall under medical-device regulation if clinically deployed.
• A general framework for governing marketed AI/ML medical devices ↗Peer-reviewed✦ AIProposes a post-market governance framework for AI/ML medical devices addressing performance drift and ongoing monitoring beyond initial approval.
• Global Initiative on AI for Health (GI-AI4H): strategic priorities advancing governance across the United Nations ↗Peer-reviewed✦ AISets out the WHO/ITU Global Initiative on AI for Health's strategic priorities to harmonize international regulatory and governance standards for health AI.
• A future role for health applications of large language models depends on regulators enforcing safety standards ↗Peer-reviewed✦ AIArgues medical LLMs are likely device-like clinical decision support and that 'the urgent need to enforce existing regulations' is the key safeguard against unsafe deployment.
• External validation of AI models in health should be replaced with recurring local validation ↗Peer-reviewed✦ AIContends external validation 'does not guarantee generalizability' and proposes recurring local validation as the safer regulatory paradigm for clinical AI.
• The imperative for regulatory oversight of large language models (or generative AI) in healthcare ↗Peer-reviewed✦ AICalls for a new regulatory category/oversight for medical LLMs, warning existing device frameworks were not designed for general-purpose generative models.
• How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals ↗Peer-reviewed✦ AIAudit of 130 FDA-approved medical AI devices finds evaluation gaps — mostly retrospective, scant multi-site testing — "that can mask vulnerabilities of devices when they are deployed on patients".
• Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis ↗Peer-reviewed✦ AIMaps 222 US- and 240 EU-approved AI/ML medical devices (2015–20); of 124 approved in both regions, 80 were first approved in Europe — grounding pathway-stringency debates.
• Algorithm Change Protocols in the Regulation of Adaptive Machine Learning-Based Medical Devices ↗Peer-reviewed✦ AIAnalyzes the SaMD prespecification and algorithm change protocol mechanism (FDA predetermined change control) for governing continuously-learning medical-device algorithms.
• The need for a system view to regulate artificial intelligence/machine learning-based software as medical device ↗Peer-reviewed✦ AIArgues regulators of adaptive AI/ML medical software must shift from a product-centric approach to "a system view" covering human-AI interaction and organizational context.
• Dissecting racial bias in an algorithm used to manage the health of populations ↗Peer-reviewed✦ AIA widely used US care-management algorithm is racially biased — "at a given risk score, Black patients are considerably sicker" — because it predicts costs, not illness.
• Hallucination ↗PreprintJi, Z., et al. (2023), 'Survey of Hallucination in Natural Language Generation,' ACM Computing Surveys 55(12): 1-38.