The lack of certainty surrounding whether a causal relationship exists when large data sets provide information can create ethical dilemmas. Black-box medicine pulls together huge datasets to predict what medicines might work, primarily based on patterns rather than an understanding of disease mechanisms. (Price) The patterns can be based on genetic and biological data, can be beneficial to health, and can lead a hypothesis later tested. That is, research eventually may answer the why and confirm causality. There are vast benefits to black-box medicine but it is unclear how to evaluate the duty of the doctor to a patient for whom the recommendation fails or what to do when the recommendation the algorithm produces violates the known standard of care. Does black-box data equate to the best medical judgment of the doctor or replace it? The algorithm choice does not corroborate a care choice (it does not supplement expertise), it supplants expertise. The level of risk (the side effect profile and risk of foregoing the standard of care based on the condition) is an important consideration. Risk 1: Following the computer-generated advice. Risk 2: Forgoing the standard treatment. Risk 3: Who is responsible if it turns out bad (negligence, malpractice, no consequences)? Risk 4: Can the developers be held responsible? Risk 5: The regulations do not keep up with the technology.
See W. Nicholson Price, III, “Medical Malpractice and Black-Box Medicine,” Big Data, Health Law, and Bioethics, Chapter 20. edited by I. Glenn Cohen, Holly Fernandez Lynch, Effy Vayena, Urs Gasser, Cambridge University Press, 2018
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