 The article discusses the challenges and limitations of artificial intelligence, AI, in healthcare, including intrinsic difficulties in machine learning, logistical issues in implementation, and barriers to adoption and sociocultural changes. Robust peer-reviewed clinical evaluation is necessary for evidence generation, but conducting these may not always be feasible. Regulation that balances innovation with potential harm and post-market surveillance are required to ensure patient safety. Mechanisms for direct comparisons of AI systems must be developed, including independent test sets. Developers must address potential dangers such as dataset shift, confounder bias, and negative consequences on health outcomes. If these goals can be achieved, the benefits for patients could be transformational. This article was authored by Christopher J. Kelly, Alan Kafka-Kesselingdom, Mustafa Suleiman, and others. We are article.tv, links in the description below.