AI in preventive healthcare: Opportunities and challenges

Chronic diseases—including cardiovascular disease, cancer, chronic obstructive pulmonary disease, diabetes, and depression—continue to rise globally, largely driven by modifiable behavioural risk factors. An estimated 40% of premature deaths are attributable to preventable behaviours such as smoking, unhealthy diets, and physical inactivity.¹ Preventive healthcare aims to identify risk early and intervene before disease onset or progression, thereby reducing morbidity, mortality, and costs. Artificial intelligence (AI) is increasingly positioned as a scalable enabler of preventive care via predictive analytics, automated risk stratification, conversational agents, and digital therapeutics. This narrative review synthesises the opportunities and challenges of AI in preventive healthcare, with illustrative examples of real-world tools (Ada Health, Lark Health Coach AI, GECA, Dejal@bot, and CoachAI). We highlight AI’s promise for earlier detection, faster and more consistent decision support, and enhanced outreach and behaviour change interventions, while critically examining barriers including non-representative data, bias, calibration and generalisability limitations, privacy and security risks, and the “black-box” problem that undermines clinical trust. Responsible integration of AI into preventive care requires robust governance, transparent evaluation, equity-oriented data strategies, and clinician oversight to ensure safety, effectiveness, and public confidence.