Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"November 13, 2026 3 min read Add topic to email alerts Receive an email when new articles are posted on Please provide your email address to receive an email when new articles are posted on . ” data-action=subscribe> Subscribe We were unable to process your request. Please try again later. If you continue to have this issue please contact customerservice@slackinc.com. Back to Healio Key takeaways: Liability remains a concern with AI use in healthcare, but decisions will likely be determined by a court of law. Understanding where AI output data came from is important to protect patients and users. ORLANDO \u2014 Developers, hospital systems and clinicians may be liable when AI makes a mistake, although the ultimate decision about legal accountability will be determined in court, according to experts at AIMed26. In a panel discussion, Leo Anthony Celi, MD, MSc, MPH, clinical research director and principal research scientist for the laboratory of computational physiology at Massachusetts Institute of Technology, asked participants about how clinicians can identify an AI mistake and who is liable when a mistake occurs. Experts weighed in on the differences between liability and accountability when AI makes a mistake. Image: Adobe Stock \u201cToday, liability lands mostly on the clinician who acted on the output and the hospital that deployed the tool,\u201d Celi told Healio. \u201cDevelopers are rarely held to account, and I think that is backward. The clinician at the bedside usually had no say in buying the system and could not see how it was trained. Responsibility should be shared with the developer and the health system, in proportion to what each party could have known and controlled.\u201d During the panel discussion, participants responded to a question about identifying mistakes made by an AI tool. \u201cOne of the problems we have right now with AI and healthcare is that we tend to sort of hold it to this extremely high standard of performance because it\u2019s a computational intervention,\u201d Philip Payne, PhD, chief health AI officer at for BJC Healthcare and Washington University Medicine, said during the discussion. \u201cWe need to be careful not to assume that anything short of perfection is a failure of AI.\u201d Ruhy Patel, MBBS, surgeon and global lead, medical affairs, at Olympus Corp., said during the discussion that holding AI to perfection makes identifying errors more difficult. \u201cWhat we should be looking at is where should we improve?\u201d Patel said. \u201cWhere can AI deliver upon standardization and consistency? Where does AI augment us?\u201d Clear guidelines around liability in AI use are also needed, but establishing them in real-world scenarios will be difficult, according to Payne. \u201cThe likely best-case scenario from an ethical and moral standpoint … [is] we share responsibility around these tools and capabilities,\u201d Payne said. \u201cThen there\u2019s the reality … that it will almost exclusively be dictated by case law, and we\u2019ll sort of emerge from cases that we\u2019ve not yet seen.\u201d \u201cWhat happens is going to happen at the court of law,\u201d Nina Kottler, MD, MS, chief AI officer at Mosaic Clinical Technologies, said during the panel discussion. \u201cThe decision’s being made there. And frankly, probably everyone gets sued.\u201d Kottler proposed a different question, looking at who should be accountable when AI makes a mistake. \u201cThe accountability question is one that we can answer and we should answer,\u201d she said. \u201cThat is our responsibility as clinicians, and that is not a single person, because AI has a life cycle.\u201d For clinicians using AI tools, understanding where the data come from is important, according to Celi. \u201cAsk what data the tool learned from, whether patients like yours were in that data and how it has performed at your own hospital,\u201d Celi told Healio. \u201cThe patients most likely to be harmed by an AI error are the ones who were poorly served before AI arrived, because the models learn from records of that care.\u201d Celi said hospitals should make reporting potential errors \u201ceasy and free of blame\u201d to \u201cprotect clinical judgment.\u201d \u201cWhen an error happens, the patient should be told and the event should go through the same safety review as a medication error,\u201d Celi said. \u201cThat review should ask whether the error was a one-off or part of a pattern affecting a particular group of patients. The findings should go back to the developer and to other hospitals using the same tool. Many clinical AI tools fall outside any shared adverse event reporting system; and we need one.\u201d Patients and community members should also be involved in evaluating AI tools before clinical use, Celi said. \u201cThey are the ones who live with its mistakes,\u201d he said. For more information: Leo Anthony Celi, MD, MSc, MPH, can be reached at nephrology@healio.com. Published by: Sources\/Disclosures Source: Celi L, et al. Who is liable when AI gets it wrong? Presented at: AIMed26; Nov. 11-13, 2026; Orlando. Disclosures: Healio could not confirm relevant financial disclosures at the time of publication. Healio is an official media partner of the AIMed26 conference. Ask a clinical question and tap into Healio AI’s knowledge base. PubMed, enrolling\/recruiting trials, guidelines Clinical Guidance, Healio CME, FDA news Healio’s exclusive daily news coverage of clinical data Learn more Add topic to email alerts Receive an email when new articles are posted on Please provide your email address to receive an email when new articles are posted on . ” data-action=subscribe> Subscribe We were unable to process your request. Please try again later. If you continue to have this issue please contact customerservice@slackinc.com. 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