The integration of machine learning (ML) in healthcare has significantly transformed medical diagnosis, treatment, and patient care. This article reviews current trends and future prospects of ML applications in healthcare, focusing on disease diagnosis, risk prediction, personalized treatment, and efficient resource management. Key applications include image recognition in radiology and pathology, predictive analytics for disease prognosis, and precision medicine tailored to individual patient profiles. The review highlights challenges in integrating ML into healthcare systems, such as data privacy, model interpretability, and validation. Future prospects include predictive analytics for preemptive health issues, wearable devices for continuous monitoring, and ML integration with genomics for personalized medicine. The study presents a systematic literature review (SLR) on assessing and maintaining ML models in healthcare. It follows Kitchenham and Charters' (2007) systematic resistance approach, analyzing 27 studies. The review identifies gaps in current research, particularly in model evaluation, monitoring, and maintenance in real-world healthcare settings. Key findings indicate that most studies focus on model creation and experimental validation, with limited attention to ongoing model evaluation and maintenance. The quality criteria for the studies show low scores, indicating a lack of detailed methods for model evaluation and maintenance. The review emphasizes the need for robust model monitoring and evaluation in healthcare to ensure accuracy and reliability. It discusses challenges such as data quality, model drift, and ethical considerations. The study also highlights the importance of continuous learning and the need for standardized guidelines for ML in healthcare. Future research should focus on developing systematic and repeatable frameworks for evaluating and maintaining ML models in healthcare applications. The review concludes that ongoing research is essential to address the challenges and improve the effectiveness of ML in healthcare.The integration of machine learning (ML) in healthcare has significantly transformed medical diagnosis, treatment, and patient care. This article reviews current trends and future prospects of ML applications in healthcare, focusing on disease diagnosis, risk prediction, personalized treatment, and efficient resource management. Key applications include image recognition in radiology and pathology, predictive analytics for disease prognosis, and precision medicine tailored to individual patient profiles. The review highlights challenges in integrating ML into healthcare systems, such as data privacy, model interpretability, and validation. Future prospects include predictive analytics for preemptive health issues, wearable devices for continuous monitoring, and ML integration with genomics for personalized medicine. The study presents a systematic literature review (SLR) on assessing and maintaining ML models in healthcare. It follows Kitchenham and Charters' (2007) systematic resistance approach, analyzing 27 studies. The review identifies gaps in current research, particularly in model evaluation, monitoring, and maintenance in real-world healthcare settings. Key findings indicate that most studies focus on model creation and experimental validation, with limited attention to ongoing model evaluation and maintenance. The quality criteria for the studies show low scores, indicating a lack of detailed methods for model evaluation and maintenance. The review emphasizes the need for robust model monitoring and evaluation in healthcare to ensure accuracy and reliability. It discusses challenges such as data quality, model drift, and ethical considerations. The study also highlights the importance of continuous learning and the need for standardized guidelines for ML in healthcare. Future research should focus on developing systematic and repeatable frameworks for evaluating and maintaining ML models in healthcare applications. The review concludes that ongoing research is essential to address the challenges and improve the effectiveness of ML in healthcare.