Machine learning is increasingly integrated into clinical practice, with applications ranging from pre-clinical data processing, bedside diagnosis assistance, patient stratification, treatment decision making, and early warning as part of primary and secondary prevention. However, a multitude of technological, medical, and ethical considerations are critical in machine-learning utilization, including the necessity for careful validation of machine-learning-based technologies in real-life contexts, unbiased evaluation of benefits and risks, and avoidance of technological over-dependence and associated loss of clinical, ethical, and social-related decision-making capacities. Other challenges include the need for careful benchmarking and external validations, dissemination of end-user knowledge from computational experts to field users, and responsible code and data sharing, enabling transparent assessment of pipelines. In this review, we highlight key promises and achievements in integration of machine-learning platforms into clinical medicine while highlighting limitations, pitfalls, and challenges toward enhanced integration of learning systems into the medical realm.
