{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T06:07:20Z","timestamp":1792130840253,"version":"4.4.0"},"reference-count":52,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Building on the assertion that nearly every clinician will eventually use artificial intelligence (AI), this study provides a triangulated qualitative analysis of the requirements, challenges, and prospects for integrating AI into routine health care practice. This skills gap contributes to cautious and uneven adoption across clinical settings. Despite advancements, many health care professionals report a self-perceived lack of proficiency in comprehending, critically evaluating, and ethically deploying AI tools, which contributes to cautious adoption in clinical settings.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n While addressing key research questions, the study investigates the necessary prerequisites, barriers, and opportunities for AI adoption and specific training priorities that medical staff require. The study is uniquely focused on the health care workforce, moving beyond the predominant emphasis in the literature on medical students.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Situated in Flanders, Belgium, a recognized innovation leader but with moderate lifelong learning participation, this research combines 15 semistructured expert interviews, a regional survey of 134 health care professionals, and 3 co-interpretive focus groups with 39 stakeholders, all conducted in 2024.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The results expose small generational and mainly occupational divides. For instance, 85.07% (114\/134) of survey respondents expressed interest in introductory AI courses tailored to health care, while 80% (134) of them sought practical, job-relevant AI skills. However, only 13.8% (134) of clinicians felt that their training adequately prepared them for AI integration. Notably, younger professionals (<30 years of age) were most eager to engage with AI but also expressed greater concern about job displacement, while older professionals (>50 years of age) prioritized reducing administrative burden. Physicians and dentists reported higher self-assessed AI knowledge, whereas nurses and physiotherapists showed the lowest familiarity. The survey also revealed differences in preferred learning formats, with doctors favoring flexible, asynchronous learning and nurses emphasizing the need for accredited, employer-supported training during work hours. Ethics, though emphasized in academic literature, ranked low in training interest among most practitioners, except for younger and palliative care professionals. Focus group participants confirmed the need for clear regulatory guidance and access to accredited, practically oriented training. A significant insight was that nurses often lacked institutional support and funding for training, despite their pivotal role in AI-enabled workflows.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Taken together, these findings indicate that a one-size-fits-all approach to AI education in health care is unlikely to be effective. By triangulating insights across research stages, this study highlights the need for occupation-specific, accessible, and accredited AI training programs that bridge gaps in digital literacy and align with practical clinical priorities. The qualitative insights obtained can inform policy and training priorities in light of the European Union (EU) AI literacy mandates, while highlighting persistent gaps in workforce preparation.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/76709","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T07:10:09Z","timestamp":1759993809000},"page":"e76709-e76709","source":"Crossref","is-referenced-by-count":30,"title":["Bridging the AI-Literacy Gap in Health Care: Qualitative Analysis of the Flanders Case 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