AI-Driven Preventive Medicine: Predictive Analytics for Early Disease Detection in Precision Healthcare
Keywords:
AI Preventive Medicine, Artificial Intelligence, Predictive Analytics, Preventive Medicine, Precision Healthcare, Machine Learning, Early Disease Detection, Clinical Decision Support, Deep Learning, Personalized TreatmentAbstract
The emergence of Artificial Intelligence (AI) is fundamentally changing how health systems approach disease prevention, patient risk stratification, and clinical reasoning. This review covers AI-based technologies in preventive medicine, focusing on early disease detection, clinical decision support, personalised therapeutic planning, and precision-oriented care delivery. A corpus of 68 peer-reviewed studies published between 2018 and 2026 was synthesised following the PRISMA protocol. Coverage encompasses five principal thematic domains: (1) AI-assisted identification of oncological, cardiovascular, diabetic, and neurological pathologies; (2) machine learning and deep learning architectures in predictive modelling; (3) AI-enabled clinical decision support infrastructure; (4) genomic-informed and wearable-device-enabled personalisation of care; and (5) barriers relating to data confidentiality, algorithmic bias, interpretability, and regulatory compliance. AI-driven systems consistently exceed 90% diagnostic accuracy across many disease categories. Limitations — including model opacity, data quality inconsistencies, and limited prospective validation — continue to constrain clinical adoption. This review provides a systematic synthesis of AI-enabled preventive medicine and identifies research priorities for ethical and effective implementation.
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Copyright (c) 2026 Jeremy Mytskevych (Author)
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