Artificial Intelligence in Healthcare: A Systematic Review of Diagnostic, Predictive, and Decision-Support Applications

Abstract

Artificial intelligence (AI) is increasingly transforming healthcare by enabling advanced approaches to disease diagnosis, clinical prediction, and decision support. This systematic review examines the applications, benefits, challenges, and emerging directions of AI in healthcare, with particular emphasis on diagnostic systems, predictive analytics, and clinical decision-support applications. Relevant peer-reviewed studies were systematically identified, screened, and synthesized to examine how machine learning, deep learning, natural language processing, and related AI techniques are being integrated into clinical practice. The findings indicate that AI-based diagnostic applications have demonstrated substantial potential in medical imaging, pathology, disease detection, and clinical classification by improving pattern recognition, diagnostic accuracy, and the efficiency of healthcare workflows. Predictive applications have increasingly supported risk stratification, early identification of disease deterioration, hospital readmission prediction, mortality estimation, and personalized treatment planning. AI-enabled decision-support systems have also assisted clinicians by integrating large and heterogeneous datasets to generate risk estimates, treatment recommendations, and clinically relevant alerts. However, the review identifies persistent challenges related to data quality, algorithmic bias, interpretability, patient privacy, cybersecurity, interoperability, regulatory oversight, and the generalizability of AI models across healthcare settings and populations. Overall, the evidence suggests that AI can complement rather than replace clinical expertise when appropriately validated, transparently implemented, and integrated into existing healthcare systems. Future research should prioritize prospective clinical validation, explainable and trustworthy AI, equitable datasets, human–AI collaboration, and robust governance frameworks to support safe, effective, and patient-centred adoption of AI in healthcare.