The promise and perils of AI in medicine

• URL: http://arxiv.org/abs/2505.06971v1
• Date: Sun, 11 May 2025 13:04:42 GMT
• Title: The promise and perils of AI in medicine
• Authors: Robert Sparrow, Joshua Hatherley,
• Abstract summary: We offer a survey, and initial evaluation, of hopes and fears about the applications of artificial intelligence in medicine.<n>We will highlight important concerns about privacy, surveillance, and bias in big data, as well as the risks of over trust in machines.<n>We will suggest that two questions, in particular, are deserving of further attention from philosophers and bioethicists.
• Score: 2.7624021966289605
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: What does Artificial Intelligence (AI) have to contribute to health care? And what should we be looking out for if we are worried about its risks? In this paper we offer a survey, and initial evaluation, of hopes and fears about the applications of artificial intelligence in medicine. AI clearly has enormous potential as a research tool, in genomics and public health especially, as well as a diagnostic aid. It's also highly likely to impact on the organisational and business practices of healthcare systems in ways that are perhaps under-appreciated. Enthusiasts for AI have held out the prospect that it will free physicians up to spend more time attending to what really matters to them and their patients. We will argue that this claim depends upon implausible assumptions about the institutional and economic imperatives operating in contemporary healthcare settings. We will also highlight important concerns about privacy, surveillance, and bias in big data, as well as the risks of over trust in machines, the challenges of transparency, the deskilling of healthcare practitioners, the way AI reframes healthcare, and the implications of AI for the distribution of power in healthcare institutions. We will suggest that two questions, in particular, are deserving of further attention from philosophers and bioethicists. What does care look like when one is dealing with data as much as people? And, what weight should we give to the advice of machines in our own deliberations about medical decisions?

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