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Machine Learning Applications in Healthcare Background Recent advances in machine learning (ML) techniques have opened new possibilities for improving clinical decision-making and patient outcomes through enhanced diagnosis, risk prognosis, and treatment optimization. Core ML approaches applied in healthcare include neural networks, natural language processing, regression models, Bayesian classifiers, and clustering algorithms (Thiagarajan et al., 2020). When trained on multi-modal patient data, these methods have shown effectiveness for a range of predictive modeling and pattern recognition tasks... Key Clinical Applications Medical Imaging ML has shown immense promise in the analysis of complex medical imaging data for computer-aided screening and diagnosis. Applications include tumor detection in mammography, classification of lung nodules on chest X-rays, and segmentation of brain anatomy in MRI scans (Shen et al., 2017). Deep learning models can rapidly process imaging data and identify abnormalities missed by human interpretation. However, model reliability and generalizability remain active areas of research (Liu et al., 2019)... Challenges and Opportunities While adoption of ML in healthcare holds enormous potential, there are critical technical, regulatory, and ethical challenges to address regarding real-world deployment. Key issues include model interpretability and bias, data privacy, and medicolegal liability (Wiens et al., 2019). Close collaboration between computer scientists, clinicians, and policy makers is needed to establish appropriate governance frameworks and practice standards... This excerpt demonstrates the well-synthesized overview and relevant citations generated by the prompt to give buyers a preview of the literature review content.