AI and Big Data Convergence in Predictive Analytics for Early Disease Detection and Personalized Treatment

Abstract

Healthcare is being reshaped by the pairing of artificial intelligence with large-scale data analytics, and one of the clearest effects of that pairing is earlier, more accurate disease detection alongside treatment plans built around the individual patient rather than the average one. As these two technologies have matured together, they have made it possible to build predictive models that can make sense of the kind of messy, multi-source data hospitals that generate electronic health records, medical images, genomic sequences, and streams from wearable sensors. This paper looks at recent progress in AI-driven predictive analytics across several clinical areas, including oncology, infectious disease, neurology, cardiometabolic conditions, mental health, neurodevelopmental disorders, and precision medicine. Across these areas, a common toolkit keeps reappearing: machine learning, deep learning, transformer architectures, autoencoders, automated machine learning, explainable AI, and generative AI, each applied to some combination of diagnosis, risk prediction, treatment planning, and decision support. Taken together, the studies reviewed here suggest that combining heterogeneous health data with these newer analytical methods genuinely improves diagnostic accuracy, opens the door to earlier intervention, and supports care that is tailored to the individual. That said, the field is not without friction. Data heterogeneity, weak generalizability across populations, privacy and security concerns, algorithmic bias, and a persistent lack of model transparency all remain real obstacles. Explainable AI has become one of the more important responses to this last problem, since clinicians are understandably reluctant to act on predictions they cannot interrogate. Overall, the convergence of AI and big data gives precision medicine a solid foundation to build on, but only if that progress is paired with rigorous validation, sound ethical governance, and models clinicians can trust.