{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T18:30:24Z","timestamp":1782930624766,"version":"3.54.5"},"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["390696704"],"award-info":[{"award-number":["390696704"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>Artificial intelligence (AI) is increasingly being integrated into healthcare, particularly in data-intensive chronic diseases that rely on longitudinal monitoring and shared decision-making. Multiple sclerosis is a prototypical example of such care, but real-world benefit will depend on whether people accept AI support in different clinical roles. We conducted a cross-sectional, web-based survey among 241 people with MS (pwMS) to assess comfort with AI across eight clinical domains and to identify predictors of acceptance. We derived an artificial-intelligence attitudes composite with high internal consistency (Cronbach alpha\u2009=\u20090.90). Overall acceptance was moderate (mean 3.39\u2009\u00b1\u20090.78). Acceptance differed across domains, demonstrating a responsibility gradient: comfort was highest for supportive applications such as chronic management (54.4%) and symptom screening (50.2%), but lower for treatment selection (38.6%) and diagnosis (35.3%; P\u2009&lt;\u20090.001). In multivariable models, frequent general AI use (at least weekly; 30.7%) was the strongest independent predictor of acceptance (P\u2009&lt;\u20090.001). Acceptance also differed by region (Eastern vs Western Germany, P\u2009=\u20090.025), whereas clinical disability was not significantly associated. Older age was associated with lower acceptance of AI-supported management. Most participants viewed AI as a logistical support tool but, assuming equal diagnostic accuracy, 78.8% preferred joint artificial-intelligence-clinician decision-making with clinician final responsibility. These findings indicate that acceptance may be context-dependent and more strongly associated with prior familiarity than with disease severity. Implementation should move beyond technical validation to transparent, clinician-led \u2018human-in-the-loop\u2019 workflows with explicit accountability and staged adoption beginning with low-risk use cases.<\/jats:p>","DOI":"10.1371\/journal.pdig.0001236","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T17:41:18Z","timestamp":1782927678000},"page":"e0001236","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring attitudes and acceptance of artificial intelligence in multiple sclerosis from the patient 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