AI-Driven Predictive Analytics for Healthcare: Challenges and Opportunities

Abstract

AI-powered predictive analytics is transforming healthcare by offering data-informed perceptions into prognosis, diagnosis, resource distribution, and treatment. Because they utilize Machine Learning (ML), Natural Language Processing (NLP), together with Deep Learning (DL) on multimodal data, like Electronic Health Records (EHR), medical imaging, also genomics, predictive systems offer the potential for earlier disease detection and more tailored interventions. This review compiles healthcare's AI-driven predictive analytics advancements as it highlights current methods' pros and cons and notes difficulties like data diversity, interpretability, equity, and regulatory obstacles. Opportunities within preventive care, population health, decision support, and precision medicine exist simultaneously. We represent how ethical predictive models get used inside patient care. We do this via the doing of a comparison regarding key studies. This study finds that predictive analytics is something that promises much. However, its effectiveness relies on an execution that can be transparent, or even ethical, and quite scalable.