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TRUVACE RECORD VERSION
record: TRV-2026-0107
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-13T05:22:05.611355Z
status: published
lens: g_space
sector: health
headline: Exploring attitudes and acceptance of artificial intelligence in multiple sclerosis from the patient perspective
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: comfort was highest for supportive applications such as chronic management (54.4%) and symptom screening (50.2%) | 78.8% preferred joint artificial-intelligence-clinician decision-making with clinician final responsibility
problem_reading: (none)
problem_evidence: (none)
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.
limitation: Findings reflect self-reported comfort in a cross-sectional survey, not measured clinical outcomes from deployed AI systems.
tag: Evidence-backed gain
key_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.
rundown: 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.
sources:
- peer_reviewed | PLOS Digital Health | https://doi.org/10.1371/journal.pdig.0001236 | 2026-07-01
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