Exploring the Role of AI in Predicting Chronic Disease Progression: Diabetes and Cardiovascular Diseases

Abstract

This review integrates multimodal data, which includes genetic information, wearable sensor outputs, and electronic health records (EHRs), providing an innovative analysis of artificial intelligence (AI) advancements for chronic disease prediction. AI is revolutionizing chronic disease management, particularly for diabetes and cardiovascular disease. Through the incorporation of evidence from AI-enabled models, the research projects predictive accuracies exceeding 80% in the onset and progression of illness and their role in ensuring early diagnosis, customized treatment, and operational efficiency. Inventions like neural networks improved by particle swarm optimization attain diagnostic accuracies of 99.67%, while edge computing helps real-time monitoring to reduce hospitalizations through early interventions. Although AI in health care has advanced significantly, algorithmic biases against underrepresented populations—e.g., older adults and non-White communities—fragmented data ecosystems preventing institutional interoperability, and ethical issues regarding privacy and transparency continue to impede scalability and fair implementation. To overcome these obstacles and ensure innovations are distributed equitably across global health care systems, future initiatives should prioritize multimodal data fusion, fairness audits during model development, federated learning frameworks that support safe cross-institutional collaboration, and large clinical trials that validate AI in multiple real-world settings. The advancement of AI has an opportunity to significantly enhance the quality of life for those struggling with chronic diseases and transform health care systems globally, provided that its advancement strategies are transparent, equitable, and focused on wide-ranging validation projects. By combining different multimodal data (EHRs, wearables, and genomes) with fairness audits and federated learning, this review provides a novel synthesis of AI advances for the prediction of chronic diseases. This assessment stands out from others because it addresses both technical performance and ethical deployment strategies. The aim is to support efforts to generate AI adoption equitably in the real world.

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