AI and Healthcare Transformation: Economic, Legal, and Equity Perspectives
DOI:
https://doi.org/10.5281/zenodo.23064531Keywords:
machine learning, predictive healthcare, personalized medicine, health equity, algorithmic fairnessAbstract
Machine learning (ML) is increasingly presented as the enabling technology of predictive, personalized medicine. This review examines its promise and its limits from a perspective informed by economic history: health improvements in Europe have historically depended less on single inventions than on the institutions, complementary investments and distributional choices that surround them. Drawing on peer-reviewed clinical, computational and policy literature, the article maps the present scenario across four dimensions: clinical applications, data infrastructures, evidence and validation, and equity and regulation. It shows that ML now performs at or near expert level in narrow diagnostic and prognostic tasks, yet that real-world evidence remains uneven, that training data under-represent many populations, and that widely used algorithms have encoded structural inequality. The article then sets out a forward-looking agenda covering privacy-preserving collaboration, multimodal and foundation models, interpretability, economic evaluation and the productivity J-curve, and equity-by-design governance within the European regulatory environment. It concludes that predictive ML will contribute to personalized and equitable health systems only if data, validation, incentives and accountability are treated as public infrastructure rather than as by-products of algorithm development
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Copyright (c) 2026 Amit Kumar , Rituraj Soni (Author)
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