A Comprehensive Review Of Machine Learning Applications and Challenges in Healthcare

Machine learning (ML) is transforming modern healthcare, leading to enhanced diagnostic accuracy, streamlined workflows, patient-specific treatment regimens, and data-driven clinical decision-making. Here we reviewed the findings to discuss the state of the art of machine learning (ML) applications in medical imaging, electronic health record (EHR), clinical decision support systems, generative AI, explainable AI, and remote patient monitoring. Although machine learning (ML) offers an unprecedented opportunity to improve efficiency and accuracy, its application also raises concerns about fairness, transparency, accountability, environmental sustainability, and clinical acceptance. This review combines technical, ethical and sociotechnical perspectives and informs the requirement for strong governance models, fairness-by-design approaches, explainable constructs, clinician-centric education and sustainable artificial intelligence (AI) innovation. The results are intended to facilitate the responsible adoption of machine learning (ML) in contemporary healthcare systems.

کلیدواژه ها:

Machine learning ، healthcare ، explainable Al ، electronic health record (EHR)integration ، fairness ، generative Al