Law, Ethics and Policy of Quantum & AI in Healthcare and Life Sciences published at Harvard, Stanford and European Commission

A collaborative research initiative by scholars from Stanford, Harvard, and MIT, published by the Petrie-Flom Center at Harvard Law School, the Stanford Center for Responsible Quantum Technology, and the European Commission, delves into the complex regulatory and ethical landscape of integrating quantum technologies and artificial intelligence (AI) into the healthcare and life sciences sectors. This series of policy guides and analyses, authored by an interdisciplinary team including Mauritz Kop, Suzan Slijpen, Katie Liu, Jin-Hee Lee, Constanze Albrecht, and I. Glenn Cohen, offers a comprehensive examination of the transformative potential and inherent challenges of this technological convergence.

Regulating Quantum & AI in Healthcare and Medicine: A Brief Policy Guide

This body of research, examining the entangled legal, ethical, and policy dimensions of integrating quantum technologies and AI into healthcare, is articulated across a series of publications in leading academic and policy forums. These works collaboratively build a comprehensive framework for understanding and navigating the future of medicine. A related policy guide was also published on the European Commission's Futurium platform, further disseminating these findings to a key international policymaking audience. The specific publications include:

1. A Brief Quantum Medicine Policy Guidehttps://blog.petrieflom.law.harvard.edu/2024/12/06/a-brief-quantum-medicine-policy-guide/

2. How Quantum Technologies May Be Integrated Into Healthcare, What Regulators Should Considerhttps://law.stanford.edu/publications/how-quantum-technologies-may-be-integrated-into-healthcare-what-regulators-should-consider/

3. EU and US Regulatory Challenges Facing AI Health Care Innovator Firmshttps://blog.petrieflom.law.harvard.edu/2024/04/04/eu-and-us-regulatory-challenges-facing-ai-health-care-innovator-firms/

4. Regulating Quantum & AI in Healthcare: A Brief Policy Guidehttps://futurium.ec.europa.eu/en/european-ai-alliance/document/regulating-quantum-ai-healthcare-brief-policy-guide

by Mauritz Kop, Suzan Slijpen, Katie Liu, Jin-Hee Lee, Constanze Albrecht & I. Glenn Cohen

Forging the Future of Medicine: A Scholarly Perspective on the Law, Ethics, and Policy of Quantum and AI in Healthcare

The research posits that the fusion of AI with second-generation quantum technologies (2G QT)—which harness quantum-mechanical phenomena like superposition and entanglement—is poised to revolutionize precision medicine. This synergy of quantum computing, sensing and simulation with artificial intelligence promises hyper-personalized healthcare solutions, capable of tackling intricate medical problems that lie beyond the grasp of classical computing. The potential applications are vast, spanning from accelerated drug discovery and development workflows and enhanced diagnostic imaging to rapid genome sequencing and real-time health monitoring. For instance, quantum simulations could model molecular interactions to create more effective pharmaceuticals, while quantum dots may offer novel platforms for targeted cancer therapies and treatments for neurodegenerative conditions by overcoming the blood-brain barrier.

However, the authors caution that these groundbreaking advancements are accompanied by significant ethical, legal, socio-economic, and policy (ELSPI) implications. The emergence of Quantum Artificial Intelligence (QAI), Quantum Machine Learning (QML), and Quantum Large Language Models (QLLM) is expected to amplify these ELSPI concerns. The dual-use nature of these technologies, such as their potential application in gain-of-function research, necessitates a principled and human-centric governance approach.

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Academics, Artificial Intelligence, European Law, American Law, Stanford, Harvard, Medical Devices, Medicine, HealthcareMauritz Kop6 december 2024quantum, AI, healthcare, medicaldevices, law, ethics, policy, governance, regulation, medicine, EMA, FDA, Harvard, Stanford, Mauritz Kop

What are the main requirements for AI systems in Healthcare?

Main barriers to adoptation of Artificial Intelligence in healthcare.

Absence of a specific AI law, or clear legal framework from the perspective of both professional users (A) and patients (B).

When constructing such a framework, it is important to make a distinction between the various sub-areas of healthcare, such as research and development, professional care providers and recipients of care. Because each sub-area has different needs.

Barriers for professional users.

It is simply unclear for companies and private and academic research institutes in the medical sector what is and is not allowed in the field of AI, blockchain, computer & machine vision and robotics. Both at European level and at national level. This knowledge is important for the commodification of their inventions/creations. Two practical examples are permission from Farmatec and obtaining a CE-marking.

Requirements for sustained use of AI in healthcare.

Since traceability and transparency are key within any healthcare (and food-feed) system, blockchain could play an important role in sustained use of AI in healthcare.

A EU AI Directive or Regulation should be able to implement and/or adhere to principles of Eudralex (The body of European Union legislation in the pharmaceutical sector), Good Manufacturing Practices (GMP) and Good Distribution Practices (GDP) in particular.