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Health·G Space·Evidence-backed gain·Published 2026-07-13

Exploring attitudes and acceptance of artificial intelligence in multiple sclerosis from the patient perspective

Abstract: 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…

TRV-2026-0107Peer-reviewedPermanent record — cite & verify
Exploring attitudes and acceptance of artificial intelligence in multiple sclerosis from the patient perspective

Teriflunomide 3D spacefill by Jynto ( talk ). CC0 · http://creativecommons.org/publicdomain/zero/1.0/deed.en

The quick read

By July 1 2026, researchers had surveyed 241 people with MS via a web-based questionnaire to measure comfort with AI across eight clinical domains. They found moderate overall acceptance (mean 3.39 ± 0.78) that varied by task, with 54.4% comfortable with chronic management and 50.2% with symptom screening, compared to 38.6% for treatment selection and 35.3% for diagnosis. Frequent general AI use was the strongest predictor of acceptance.

Acceptance appears context-dependent and tied more to prior familiarity than disease severity, which matters for implementation planning. The observed preference for clinician-led human-in-the-loop workflows, with 78.8% favoring joint decision-making assuming equal accuracy, suggests staged adoption starting with low-risk use cases may be more acceptable, but the study does not demonstrate actual health benefits or harms from deployed AI.

Main points
  • Cross-sectional web-based survey of 241 people with MS (pwMS) assessed comfort across eight clinical domains using an AI attitudes composite (Cronbach alpha = 0.90).
  • Overall acceptance was moderate with mean 3.39 ± 0.78 and showed a responsibility gradient across domains (P < 0.001).
  • Frequent general AI use at least weekly (30.7%) was the strongest independent predictor of acceptance (P < 0.001), while clinical disability was not significantly associated.
  • Acceptance differed by region (Eastern vs Western Germany, P = 0.025) and older age was associated with lower acceptance of AI-supported management.
Gain

In a survey of 241 people with MS, respondents reported higher comfort with AI when used for low-risk supportive roles like chronic management and symptom screening, with most preferring a joint model where clinicians retain final responsibility.

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