Privacy Implications of AI-Enabled Predictive Analytics in Clinical Diagnostics, and How to Mitigate Them

Bioethica Forum 17 (1) (2025)
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Abstract

AI-enabled predictive analytics is widely deployed in clinical care settings for healthcare monitoring, diagnostics and risk management. The technology may offer valuable insights into individual and population health patterns, trends and outcomes. Predictive analytics may, however, also tangibly affect individual patient privacy and the right thereto. On the one hand, predictive analytics may undermine a patient’s state of privacy by constructing or modifying their health identity independent of the patient themselves. On the other hand, the use of predictive analytics may violate the patient’s right to privacy if the patient has no control over the use or output of the technology. These repercussions ultimately erode patient autonomy and agency. This paper discusses these implications in further detail, and proposes possible measures for their mitigation. They involve the incorporation in the AI systems of accuracy-enhancing statistical models and methods, more privacy-conscious institutional policies and practices, and effective choice for patients to accept or refuse diagnostics and treatment drawing on AI-enabled predictive analytics.

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References found in this work

Principles of biomedical ethics.Tom L. Beauchamp - 2019 - New York: Oxford University Press. Edited by James F. Childress.
The informational nature of personal identity.Luciano Floridi - 2011 - Minds and Machines 21 (4):549-566.
From Individual to Group Privacy in Big Data Analytics.Brent Mittelstadt - 2017 - Philosophy and Technology 30 (4):475-494.
Predictive privacy: towards an applied ethics of data analytics.Rainer Mühlhoff - 2021 - Ethics and Information Technology 23 (4):675-690.
Two Concepts of Group Privacy.Michele Loi & Markus Christen - 2020 - Philosophy and Technology 33 (2):207-224.

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