The article "Machine Learning Applications in Healthcare: Current Trends and Future Prospects" by Dr. José Gabriel Carrasco Ramírez, Md. Mafiqul Islam, and ASM Ibnul Hasan Even explores the integration of Machine Learning (ML) techniques in healthcare, highlighting their growing importance and challenges. The authors emphasize the need for strong collaboration between data scientists and healthcare professionals to ensure that ML models are effective, understandable, and reliable in clinical settings. They discuss the obstacles faced in modeling, analysis, and validation, particularly in healthcare, where patient lives are at stake. The review focuses on the assessment and maintenance of ML models in healthcare, using a Systematic Literature Review (SLR) approach to gather and analyze relevant research. The study identifies gaps in current practices, such as the lack of specific metrics and best practices for evaluating models in production, and the need for continuous monitoring and data management. The authors propose a method for assessing and analyzing the effectiveness of ML models in healthcare, emphasizing the importance of collaboration and communication between experts. The review concludes with recommendations for future research, including the development of systematic approaches for model evaluation, monitoring, and maintenance in healthcare applications.The article "Machine Learning Applications in Healthcare: Current Trends and Future Prospects" by Dr. José Gabriel Carrasco Ramírez, Md. Mafiqul Islam, and ASM Ibnul Hasan Even explores the integration of Machine Learning (ML) techniques in healthcare, highlighting their growing importance and challenges. The authors emphasize the need for strong collaboration between data scientists and healthcare professionals to ensure that ML models are effective, understandable, and reliable in clinical settings. They discuss the obstacles faced in modeling, analysis, and validation, particularly in healthcare, where patient lives are at stake. The review focuses on the assessment and maintenance of ML models in healthcare, using a Systematic Literature Review (SLR) approach to gather and analyze relevant research. The study identifies gaps in current practices, such as the lack of specific metrics and best practices for evaluating models in production, and the need for continuous monitoring and data management. The authors propose a method for assessing and analyzing the effectiveness of ML models in healthcare, emphasizing the importance of collaboration and communication between experts. The review concludes with recommendations for future research, including the development of systematic approaches for model evaluation, monitoring, and maintenance in healthcare applications.