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Opening the Medical "Black-Box"
Episode 03: Artificial intelligence (AI) promises to revolutionize the future of health care. New technologies have the potential to assist in the detection of strokes and prediction of seizures, the identification of individuals who are at higher risk of domestic violence, and even with the development of patient treatment plans. AI medical technologies may even help doctors provide greater access to care to remote individuals and communities. Parallel to this utopian vision of AI-driven health care is a dystopian future where health data collected through wearable devices are sold to third parties and used against patients by insurers, employers or even banks. As AI continues its integration into the offices and operating theatres of medical professions, what are the ethical and human rights issues that arise?
Join us for a dialogue between two experts on AI and its impact on health care. After outlining the current capabilities of AI in health care and its potential medium and long-term capabilities, we will explore the “good” and the “not-so-good” aspects of AI when applied to health care. This event will address the benefits that might arise from applying machine learning to health data particularly in the context of more accurate diagnostics. We will also talk about the issues that are coming from this “black-box” approach to medicine and what consumers and patients should know about how their data is being shared, as well as the steps physicians, hospitals, and companies should take to protect patients’ privacy. Further, we will discuss the future of legislation and regulation and about the legal responsibilities when AI-based systems are proven wrong. This discussion will be followed by a Q&A session open to audience members.
This is the final event in a three-part AAAS series that is sponsored by Hitachi and aims at exploring artificial intelligence technologies, the current capabilities of the technologies, their ethical, legal and social implications, and the responsibilities of the scientists and engineers developing the technologies.
Recordings from this series are archived on YouTube and made available on the AAAS website.
W. Nicholson Price is a law professor at University of Michigan. He has written extensively about health law and regulations but also published papers on the impact of AI on the medical system, intellectual property, and regulation. Professor Price received a JD and a PhD in biological sciences from Columbia University and an AB in biological sciences from Harvard College.
Hsiu-Khuern Tang is a Principal Research Scientist at Hitachi America, Ltd., where he is applying machine learning to solve business problems in different industries. In healthcare, he has created analytics applications that help hospitals make better decisions using their data. His current interests include improving the explainability of clinical machine learning models.
Ilana Harrus (Moderator) is the Senior Program Associate for the fledging AAAS AI/Applications-Implications initiative. An astrophysicist by training, her concerns about privacy in the era of AI and big data prompted her to change field after more than 15 years working at NASA. She has a PhD in Physics from Columbia University and a Master’s in Information Systems from University of Maryland, Baltimore County. She is also a PMI-certified Project Manager (PMP).
"Talking Points" summary of the discussion
ARTICLES FROM THE SCIENCE FAMILY OF JOURNALS
Algorithms on regulatory lockdown in medicine (Babic et al., 2019)
Artificial intelligence for global health (Hosny and Aerts, 2019)
AI in resource-poor health care systems (Alderton, 2019)
Medicine contends with how to use artificial intelligence (Couzin-Frankel, 2019)
Adversarial attacks on medical machine learning (Finlayson et al., 2019)
Regulation of predictive analytics in medicine (Parikh et al., 2019)
Artificial intelligence could diagnose rare disorders using just a photo of a face (Schembri, 2019)
Big data and black-box medical algorithms (Price, 2018)
Health and societal implications of medical and technological advances (Dzau and Balatbat, 2018)
Turning skin “check” into checkmate (Lev-Tov, 2017)