Regulating Algorithms in Healthcare, IP and Liability - Cambridge

Description

The workshop considered liability from multiple perspectives including: the liability of clinicians, what scheme of liability should be imposed upon AI, the FDA’s approach to liability, and the view of liability from a machine learning researcher's perspective. As one perceptive delegate reminded us, potential patient benefit is the ultimate goal of using AI for health. This theme underscored the discussion on liability for AI.

Glenn Cohen painted a cogent picture of what predictive analytics promises to do for healthcare. In particular, Glenn outlined scenarios of how AI might be implemented into the healthcare system including what scheme of liability might apply to AI for health. He set out a range of possible systems for dealing with liability, drawing parallels with vaccine compensation schemes.

Nicholson Price gave a considered description of the current state of clinical decision support software and medical malpractice law. He developed a visionary outline of how AI might fit into medical malpractice, arguing that medical malpractice law typically does a poor job of adapting to new technology.

The liability panel applied their considerable expertise to a wide range of liability issues, including:


Differentiating between various kinds of machine learning
Outlining how the FDA might treat AI for healthcare
Exploring whose responsibility it should be to ensure machine learning is ethical, safe, and directed toward patient-centred care

Period	6 Sept 2018
Event type	Workshop
Location	Cambridge, United KingdomShow on map
Degree of Recognition	International