Artificial intelligence and machine learning in healthcare: a comprehensive review

Rosepreet Kaur Bhogal, Ajmer Singh

Abstract


Artificial intelligence (AI) and machine learning (ML) are reshaping healthcare by supporting faster diagnosis, predictive modeling, and efficient clinical workflows. This review examines 52 recent studies to assess how these technologies are applied across diagnostics, predictive analytics, patient monitoring, operations, treatment, and ethical considerations. Results show substantial progress in imaging, genomics, drug discovery, and hospital management, where systems often match or surpass human performance. At the same time, challenges such as limited generalizability, data bias, privacy concerns, and lack of interpretability remain significant barriers to adoption. This review identifies common strengths and gaps by grouping existing work into six themes, offering a structured view of current developments. The findings suggest that the future of AI in medical care lies in transparent, fair, and clinically validated systems that can scale across diverse populations and settings.

Keywords


Artificial intelligence; Clinical decision support; Healthcare applications; Machine learning; Predictive analytics; Robotics

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DOI: https://doi.org/10.11591/eei.v15i1.9815

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Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).