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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 An aircraft\u2019s autopilot can guide every aspect of a flight, from takeoff to landing. But it is expected, and reassuring to know, that a pilot has the skills to intervene and assume manual control at any time, especially during an emergency. There is a similar expectation for healthcare professionals, who increasingly encounter AI\u2019s expansion in practice to aid clinical diagnoses, documentation and decision-making. The concern? That overreliance on AI could eventually erode clinical skills and competencies, a phenomenon dubbed \u201cdeskilling.\u201d
Image: Ron Keren, MD, MPH. Reprinted with permission.
\u201cResearch is showing that there is a fine line between augmentation and deskilling, and there\u2019s a slippery slope between the two,\u201d Ron Keren, MD, MPH, senior vice president and chief medical officer at Children\u2019s Hospital of Philadelphia, told Healio. Impact on care Public-facing AI became mainstream with the launch of ChatGPT in 2022, but the technology\u2019s impact is already felt across medical specialties. A 2025 observational study published in The Lancet Gastroenterology & Hepatology found that adenoma detection rates during colonoscopies performed without AI fell from 28.4% in the 3 months before AI was introduced into practice to 22.4% in the 3 months afterward. Maxim Topaz \u201cBecause adenoma detection correlates with interval colorectal cancer risk, that could translate to approximately 18% more missed cancers,\u201d Maxim Topaz, PhD, RN, MA, Elizabeth Standish Gill Associate Professor of Nursing at Columbia University Medical Center, told Healio. He emphasized an urgent need to monitor AI\u2019s impact on care quality, as well as to develop oversight strategies to address deskilling concerns. \u201cWe still have hundreds of trials asking whether AI improves care and almost none measuring what happens when it is switched off,\u201d Topaz said. \u201cThere\u2019s no doubt AI is here to stay in some form,\u201d Amy Oxentenko, MD, MACP, MACG, AGAF, professor of medicine at Mayo Clinic College of Medicine and Science and vice dean of practice and chief patient experience officer for all Mayo Clinic sites, told Healio. She also noted it is easy to imagine \u201ca day in the life\u201d in which these AI systems may fail. As healthcare becomes more reliant on AI, clinicians may be less prepared to function when those systems are unavailable: Algorithms can go offline, vendors can fail and hospitals can get hit by cyberattacks, Topaz cautioned. Disruptions are not the only concern. Anxieties about deskilling are shared by healthcare professionals and patients alike. In a Wolters Kluwer survey, 77% of doctors, 70% of nurses and 68% of patients expressed concern about AI-driven deskilling. \u201cAI helps augment clinicians\u2019 capabilities, but the core judgment about a clinical scenario should always be made by the clinicians themselves,\u201d Deepti Pandita, MD, FACP, FAMIA, chief AI officer and vice president of clinical informatics and chief medical informatics at University of California Irvine Healthsystem, told Healio. \u201cWhat AI helps with is pattern recognition, documentation, triage support, decision support and suggestions \u2014 or what I call AI nudges \u2014 but ultimately, clinicians themselves are always responsible for the final diagnosis, the final output and final accountability,\u201d she added. \u2018Active role\u2019 in AI integration, oversight Human oversight is critical to ensure AI tools support quality patient care and preserve clinical skills. Topaz advised healthcare systems to identify frontline practitioners, including nurses and other clinicians, to serve on governance boards. \u201cDeskilling is a workforce and patient safety issue, not an IT issue,\u201d he said. Keren also encouraged clinicians to become involved in AI governance at their practices and institutions, regardless of whether they personally embrace AI or are skeptical of it. \u201c[Clinicians] should play an active role in helping us design how AI tools are integrated into clinical care,\u201d Keren said. \u201cThey will know best what\u2019s going to help them, and what\u2019s going to potentially negatively impact their performance.\u201d The clerical areas of practice \u2014 where AI tools are being applied at the fastest rate \u2014 raise fewer deskilling-related concerns, according to Keren. \u201cWisely, both the developers of AI technologies, as well as the health systems that are implementing them, have started with these clerical tasks, like documentation, note-taking, coding and billing, facilitating communications with patients through portals, and scheduling,\u201d Keren said. \u201cIn that setting, errors are consequential, but less likely to cause serious patient harm.\u201d Oxentenko concurred, noting AI tools that handle clerical and administrative tasks can save time and give clinicians more bandwidth to connect with their patients. However, she did highlight an important caveat. Amy Oxentenko \u201cIt\u2019s still incumbent on [the clinician] to review notes that AI has generated,\u201d Oxentenko, who also serves on the Healio AI advisory board, said. \u201cYou can\u2019t get complacent and assume it\u2019s going to be accurate. An incorrect medication or dosage could have grave consequences.\u201d On the other hand, AI tools that perform clinical care tasks \u2014 summarizing patient records, interpreting medical images, generating differential diagnoses and providing clinical decision support \u2014 require the greatest oversight from clinicians and governance boards, Keren emphasized. In these areas, \u201cerrors have the potential to really harm patients,\u201d he said. When it comes to tackling governance of AI tools, Topaz advised healthcare systems and practices to \u201cstart with measurement, because you cannot govern what you do not measure.\u201d Topaz proposed several metrics that health systems and practices could employ including, \u201cperiodic unassisted performance assessments in quality programs, manual-mode protocols where a defined share of cases \u2014 for example, one in five \u2014 is completed without AI, and monitoring of automation bias signals such as override and agreement rates.\u201d Developing a governance structure does not necessarily require new hires. Deepti Pandita \u201cIn a resource-constrained environment where you<\/p>\n","protected":false},"author":1,"featured_media":3926,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3965","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=\/wp\/v2\/posts\/3965","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3965"}],"version-history":[{"count":0,"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=\/wp\/v2\/posts\/3965\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=\/wp\/v2\/media\/3926"}],"wp:attachment":[{"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3965"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3965"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3965"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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