5. Can AI Help Medicine to be More Inclusive and Sustainable?

This chapter surveys the evolution of artificial intelligence in medicine from early expert systems (e.g., INTERNIST-1 and MYCIN) through statistical learning, deep learning, and today’s expanding ecosystem of AI-enabled clinical applications and devices. It frames this technological trajectory against pressing demographic megatrends—especially population ageing, rising multimorbidity, escalating costs, and widening workforce shortages—that threaten the sustainability and inclusiveness of healthcare systems in Europe and globally. The authors argue that, while access constraints and inequities are already emerging, AI-enabled digital health offers a plausible set of countermeasures by accelerating discovery, augmenting diagnostics, and improving decision support and population health management.At the same time, the chapter emphasises that deploying AI in techno-human clinical settings introduces intrinsic complexity and non-linear side effects, making governance and human-centred design decisive success factors. It highlights both the promise of the “AI-augmented physician” and the risks of deskilling, upskilling inhibition, bias transmission, and unequal performance across patient groups, alongside broader concerns about industry power, regulation (including the EU AI Act), and dual-use scenarios. The central conclusion is a call for pragmatic, multidisciplinary stewardship: healthcare can benefit from AI only if stakeholders—clinicians, patients, caregivers, industry, regulators, and institutions—collaborate to balance innovation with accountability, equity, and long-term system sustainability.