Machine learning is increasingly applied in medicine to solve complex problems, but its impact remains limited. This review explores how machine learning can benefit medicine, highlighting both supervised and unsupervised learning approaches. Supervised learning, such as in EKG interpretation and lung nodule detection, aims to predict known outcomes, while unsupervised learning identifies patterns in data, useful for precision medicine. Challenges include feature selection, model complexity, and generalization to new data. The Framingham Risk Score is an example of supervised learning in medicine. Unsupervised learning, like in cancer research, helps identify subtypes of diseases. However, the success of machine learning in medicine depends on high-quality, diverse data and appropriate algorithms. Examples include the C-Path tool for breast cancer, which uses automated image analysis to improve survival prediction. Another example is the Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge, where unsupervised learning identified novel features for survival prediction. Despite these successes, challenges remain, including data collection, model validation, and integration into clinical practice. The review emphasizes the need for robust, generalizable models and the importance of human expertise in guiding machine learning processes. Overall, machine learning holds promise for improving clinical care but requires careful application and validation to ensure its effectiveness and safety.Machine learning is increasingly applied in medicine to solve complex problems, but its impact remains limited. This review explores how machine learning can benefit medicine, highlighting both supervised and unsupervised learning approaches. Supervised learning, such as in EKG interpretation and lung nodule detection, aims to predict known outcomes, while unsupervised learning identifies patterns in data, useful for precision medicine. Challenges include feature selection, model complexity, and generalization to new data. The Framingham Risk Score is an example of supervised learning in medicine. Unsupervised learning, like in cancer research, helps identify subtypes of diseases. However, the success of machine learning in medicine depends on high-quality, diverse data and appropriate algorithms. Examples include the C-Path tool for breast cancer, which uses automated image analysis to improve survival prediction. Another example is the Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge, where unsupervised learning identified novel features for survival prediction. Despite these successes, challenges remain, including data collection, model validation, and integration into clinical practice. The review emphasizes the need for robust, generalizable models and the importance of human expertise in guiding machine learning processes. Overall, machine learning holds promise for improving clinical care but requires careful application and validation to ensure its effectiveness and safety.