AI for Personalized Healthcare: Predicting Risk and Recommending the Right Care
DOI:
https://doi.org/10.56472/ICCSAIML25-113Keywords:
Artificial Intelligence, Personalized Healthcare, Predictive Modeling, Recommendation Engine, Chronic Disease Prevention, Digital Health, Machine Learning, Patient Engagement, Health Risk Stratification, Cost ReductionAbstract
The integration of Artificial Intelligence (AI) into healthcare is transforming the industry from reactive treatment to proactive, personalized care. This paper presents a comprehensive overview of how AI through advanced machine learning and natural language processing can predict individual health risks and recommend personalized care strategies at scale. The core components of the proposed solution include a Prediction Engine and a Recommendation Engine. The Prediction Engine forecasts the onset of chronic conditions and potential high-cost healthcare events using multimodal data such as claims, EHRs, wearable devices, and social determinants of health. Meanwhile, the Recommendation Engine delivers tailored next-best actions through digital channels, boosting patient engagement and adherence. The platform architecture is built for scalability, regulatory compliance, and real-time responsiveness. It incorporates feedback loops, robust dashboards, and evidence-based learning to refine outputs over time. Demonstrated outcomes include improved clinical decision-making, early detection of health risks, a 2–3x increase in preventive screenings, and cost reductions of up to 12% in targeted populations. The paper concludes with a discussion on real-world deployments, performance metrics, and key challenges including fairness, privacy, and model interpretability. The findings underscore the role of AI in reshaping personalized healthcare, enabling improved outcomes for patients, providers, and payers alike
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References
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