The integration of machine learning into clinical medicine offers significant potential to improve healthcare delivery. However, ethical challenges must be addressed to realize these benefits. Machine learning algorithms may inadvertently reflect human biases, such as racial discrimination, if trained on biased data. For example, algorithms used in criminal justice have shown racial bias, and similar issues could arise in healthcare. Health disparities based on race may lead to biased outcomes in algorithms, as seen in studies using the Framingham Heart Study for non-white populations. Subtle biases in healthcare delivery may be harder to detect and prevent, potentially leading to self-fulfilling prophecies. Machine learning could also be misused to prioritize quality metrics over patient care, or to generate profits for developers. Ethical guidelines are needed to ensure machine learning systems align with patient care goals. The shift toward data-driven healthcare may change the nature of the physician-patient relationship, raising questions about fiduciary obligations and confidentiality. As machine learning becomes more integrated into healthcare, ethical principles such as beneficence and respect for patients must guide its use. The introduction of machine learning systems requires a reimagining of confidentiality and other core ethical principles. Ethical challenges, including potential bias and fiduciary relationships with machine learning systems, must be addressed promptly. Machine learning systems should be built to reflect ethical standards and held to those standards. A key step is determining how to ensure this, through policy, programming, or task-force efforts. The article emphasizes the need for ongoing ethical consideration as machine learning becomes more prevalent in healthcare.The integration of machine learning into clinical medicine offers significant potential to improve healthcare delivery. However, ethical challenges must be addressed to realize these benefits. Machine learning algorithms may inadvertently reflect human biases, such as racial discrimination, if trained on biased data. For example, algorithms used in criminal justice have shown racial bias, and similar issues could arise in healthcare. Health disparities based on race may lead to biased outcomes in algorithms, as seen in studies using the Framingham Heart Study for non-white populations. Subtle biases in healthcare delivery may be harder to detect and prevent, potentially leading to self-fulfilling prophecies. Machine learning could also be misused to prioritize quality metrics over patient care, or to generate profits for developers. Ethical guidelines are needed to ensure machine learning systems align with patient care goals. The shift toward data-driven healthcare may change the nature of the physician-patient relationship, raising questions about fiduciary obligations and confidentiality. As machine learning becomes more integrated into healthcare, ethical principles such as beneficence and respect for patients must guide its use. The introduction of machine learning systems requires a reimagining of confidentiality and other core ethical principles. Ethical challenges, including potential bias and fiduciary relationships with machine learning systems, must be addressed promptly. Machine learning systems should be built to reflect ethical standards and held to those standards. A key step is determining how to ensure this, through policy, programming, or task-force efforts. The article emphasizes the need for ongoing ethical consideration as machine learning becomes more prevalent in healthcare.