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 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.
Methods: Key strategies in this scenario are explainable AI (XAI) and counterfactual reasoning, which enhance transparency, accountability and fairness. These methods allow clinicians and regulators to interpret model decisions, identify biases and ensure human oversight in high-stakes contexts. Ethical frameworks such as the European Commission's Assessment List for Trustworthy AI (ALTAI) operationalize these goals through auditable requirements spanning transparency, fairness, privacy and societal well-being.
Results: Yet, challenges persist, including algorithmic bias, data inequity and variable regulatory standards across regions. Machine learning and generative AI…
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