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TruaceTracing the truth around AIThursday, September 3, 2026
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Health·P Space·Evidence-backed problem·Published 2026-09-03

Ethical Considerations on Artificial Intelligence, Health and Health Care. All That Glisters Is Not Gold

Abstract: Background Artificial intelligence (AI) is transforming global health care through innovations in deep learning, generative models and agentic AI systems. Traditional reductionist approaches to complex pathophysiological pathways fail to capture the true complexity of disease, motivating the adoption of network medicine (NM), which models biological systems as dynamic, interconnected networks. When combined with AI, NM enables integration of multiomic data and better characterizes disease mechanisms to guide pre…

TRV-2026-0972Peer-reviewedPermanent record — cite & verify
Ethical Considerations on Artificial Intelligence, Health and Health Care. All That Glisters Is Not Gold

The ethics of Aristotle by D. P. Chase. Public domain

The quick read

Background Artificial intelligence (AI) is transforming global health care through innovations in deep learning, generative models and agentic AI systems. Traditional reductionist approaches to complex pathophysiological pathways fail to capture the true complexity of disease, motivating the adoption of network medicine (NM), which models biological systems as dynamic, interconnected networks.

When combined with AI, NM enables integration of multiomic data and better characterizes disease mechanisms to guide precision therapies. Nevertheless, the rapid diffusion of AI in health care also raises profound ethical, regulatory and social challenges, since only a small fraction of AI tools have achieved routine clinical use, often due to limited generalizability, opaque algorithms and workflow incompatibility.

Main points
  • Background Artificial intelligence (AI) is transforming global health care through innovations in deep learning, generative models and agentic AI systems.
  • Traditional reductionist approaches to complex pathophysiological pathways fail to capture the true complexity of disease, motivating the adoption of network medicine (NM), which models biological systems as dynamic, interconnected networks.
  • When combined with AI, NM enables integration of multiomic data and better characterizes disease mechanisms to guide precision therapies.
Problem

Results Yet, challenges persist, including algorithmic bias, data inequity and variable regulatory standards across regions.

Sources

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The debate