Artificial intelligence has quickly become part of physicians’ everyday work. For a profession built on trust, evidence and accountability, deciding how much confidence to place in the technology is a more complicated question. Eighty-one percent of physicians now use AI professionally, more than double the share reported when the American Medical Association first surveyed doctors on the technology in 2023.
More than three-quarters believe AI improves their ability to care for patients, according to the AMA’s 2026 survey of nearly 1,700 physicians. But greater use has not erased clinicians’ concerns about what happens when AI gets something wrong. Eighty-eight percent of physicians said robust safety and efficacy validation is important for broader adoption, while 86 percent said the same of data privacy.
Physicians also ranked clear liability frameworks among the most important regulatory steps for increasing their trust in AI. The more AI becomes part of clinical practice, the more important it becomes for clinicians to understand when, and why, they can rely on its recommendations. Chris DeRienzo, chief physician executive at the American Hospital Association, framed trust and transparency as closely linked while moderating a panel at Newsweek's AI Health Summit on September 23 in New York City.
"It seems to me that you've got to find the right point at the intersection of trust and transparency in order to enable [clinical] judgment," DeRienzo posed. The health care leaders who joined him on-stage differed on what ultimately earns clinicians’ confidence. Matt Sakumoto, chief clinical product officer at the ambient AI provider Nabla, told Newsweek's gathered audience during the panel.
Sakumoto said clinicians need evidence behind the tools they use, including information about the data used to train models and how they have performed previously. The same questions matter for patients as AI becomes more deeply embedded in clinical care, he said. Yet transparency does not necessarily mean that clinicians need to understand everything happening inside an algorithm.
Theo Zanos, head of the Division of Health AI at Northwell Health, argued that sustained performance may ultimately matter more for gaining physicians' trust. A tool can offer extensive information about how it was built, he said, but clinicians will quickly stop using it if it does not reliably work. A systematic review of studies examining explainable AI found mixed evidence that explanations themselves increase clinicians’ trust.
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