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Functional requirements to mitigate the Risk of Harm to Patients from Artificial Intelligence in Healthcare

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arxiv 2309.10424 v1 pith:YOK5X4PS submitted 2023-09-19 cs.AI

classification cs.AI
keywords checkcontinuousartificialbiaseuropeanfunctionalharmhealthcare
verification ladder T0 review T1 audit T2 compute T3 formal
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The Directorate General for Parliamentary Research Services of the European Parliament has prepared a report to the Members of the European Parliament where they enumerate seven main risks of Artificial Intelligence (AI) in medicine and healthcare: patient harm due to AI errors, misuse of medical AI tools, bias in AI and the perpetuation of existing inequities, lack of transparency, privacy and security issues, gaps in accountability, and obstacles in implementation. In this study, we propose fourteen functional requirements that AI systems may implement to reduce the risks associated with their medical purpose: AI passport, User management, Regulation check, Academic use only disclaimer, data quality assessment, Clinicians double check, Continuous performance evaluation, Audit trail, Continuous usability test, Review of retrospective/simulated cases, Bias check, eXplainable AI, Encryption and use of field-tested libraries, and Semantic interoperability. Our intention here is to provide specific high-level specifications of technical solutions to ensure continuous good performance and use of AI systems to benefit patients in compliance with the future EU regulatory framework.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibility

    cs.CL 2025-05 reject novelty 4.0 of 10

    MedOrch is a modular framework in which LLMs call medical tools to answer clinical questions; its headline results on Alzheimer's, chest X-ray, and VQA benchmarks are weakened by best-of-five scoring.

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