Generative AI for Personalized Healthcare: Data-Driven Diagnosis Models

Abstract

A Revolution that Artificial Intelligence has brought about in personalized diagnosis, integrated with treatment recommendations in the field of healthcare is because of growth of healthcare technologies unlike never before. In this paper we explore how AI-centric generative models could improve diagnostic procedures, forecast disease evolution, and recommend personalized treatment strategies. The presented method tries to put the classic learning from examples together with machine-based procedures for extracting knowledge, so as to make clinical application and interpretation of results more understandable. Case studies show that generative AI models offer superior accuracy and a lower false positive rate versus traditional machine learning models. The analysis also considers the ethics, confidentiality and scale of these models in clinical settings. The potential of generative AI to transform personalized healthcare, paving the way for patient-specific treatment and diagnostic breakthroughs. These results not only contribute to the current literature on AI deployment in healthcare, but also provide beneficial insights enabling future work dedicated towards personalized medicine (adaptive medical treatment) solutions.

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