From digital bench to bedside: exaggerated risks, realistic expectations, and genuine challenges of medical AI

Artificial intelligence (AI) has become an increasingly prominent force in medicine, driven by rapid technical advances and a growing number of clinical applications. As the field matures, it now increasingly moves from experimental development toward a phase of broader implementation. However, debates surrounding medical AI are often shaped by exaggerated risks and overly optimistic expectations. This paper seeks to contribute to a more balanced and realistic discussion. Rather than framing AI as either an existential threat or a universal solution, we advocate for an open-minded, evidence-based understanding of AI as a tool to support healthcare and discuss current and emerging challenges related to clinical validation, human-AI interaction, bias and discrimination, education, agentic AI, and the development and maintenance of trust. KEY POINTS: Question Discussions about AI in medicine continue to be dominated by exaggerated risks and overly optimistic expectations. Findings We provide a more realistic evaluation of the current opportunities of AI in medicine and want to highlight some genuine challenges that lie ahead. Clinical relevance AI is here to stay in medicine. Focusing on real and present challenges, rather than being distracted by exaggerated risks, as well as responsible expectation management, will be key to its success.