Computer knows best? The need for value-flexibility in medical AI

By R. McDougall. 2018

209 citations recorded by the source corpus.

Artificial intelligence (AI) is increasingly being developed for use in medicine, including for diagnosis and in treatment decision making. The use of AI in medical treatment raises many ethical issues that are yet to be explored in depth by bioethicists. In this paper, I focus specifically on the relationship between the ethical ideal of shared decision making and AI systems that generate treatment recommendations, using the example of IBM’s Watson for Oncology. I argue that use of this type of system creates both important risks and significant opportunities for promoting shared decision making. If value judgements are fixed and covert in AI systems, then we risk a shift back to more paternalistic medical care. However, if designed and used in an ethically informed way, AI could offer a potentially powerful way of supporting shared decision making. It could be used to incorporate explicit value reflection, promoting patient autonomy. In the context of medical treatment, we need value-flexible AI that can both respond to the values and treatment goals of individual patients and support clinicians to engage in shared decision making.

Subfields: Artificial Intelligence, Health Informatics, Health Information Management

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Citations in the corpus

Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI (2021, background): “41 These worries are understandable and would indeed be worrisome if black box algorithms would automatise decision making, without humans in the loop.”
Ethics and governance of trustworthy medical artificial intelligence (2023, background): “Computers recommend treatments based on specific parameters that may not actually best reflect the values and preferences of a particular patient [60].”
What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine (2023, background): “It can also consider factors, such as patient demographics, genetics, and comorbidities to optimise treatment strategies [92].”
The ethics of AI in health care: A mapping review. (2020, background): “…healthcare is also seen as a means of promoting social justice is crucial in order to establish: what type of data services will be embedded in the system (Voigt, 2019); what data should be collected; and which values should be embedded in algorithmic decision-making services (McDougall, 2019).”
On the ethics of algorithmic decision-making in healthcare (2019, background): “24 We believe this analysis to be basically correct (for a more critical view, cf. Di Nucci 25 ).”