Exploring Attitudes and Acceptance of Artificial Intelligence in Multiple Sclerosis from the Patient Perspective

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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 acceptance. We derived an artificial-intelligence attitudes composite with high internal consistency (Cronbach alpha = 0.90). Overall acceptance was moderate (mean 3.39 ± 0.78). 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 < 0.001). In multivariable models, frequent general AI use (at least weekly; 30.7%) was the strongest independent predictor of acceptance (P < 0.001). Acceptance also differed by region (Eastern vs Western Germany, P = 0.025), 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 is context-dependent and aligns more closely with prior familiarity than with disease severity. Implementation should move beyond technical validation to transparent, clinician-led’human-in-the-loop’ workflows with explicit accountability and staged adoption beginning with low-risk use cases. Author Summary We use artificial intelligence more and more in everyday life, and similar tools are now being introduced into medical care. For long-term conditions such as multiple sclerosis, digital systems could help manage large amounts of clinical information and support monitoring between visits. At the same time, these tools will only be useful if the people receiving care are willing to use them and understand what role they play. In this study, we asked 241 people living with multiple sclerosis in Germany how comfortable they would feel with artificial intelligence in different parts of care. We found that comfort depended strongly on the task. Participants were most open to artificial intelligence when it supported practical, lower-risk functions such as ongoing monitoring or symptom screening, and they were more cautious when it was described as influencing diagnosis or treatment choices. Most participants wanted clinicians to remain responsible for final decisions. Acceptance was higher among people who already used artificial intelligence frequently in everyday life, and it differed by age and by region. Our findings suggest that successful implementation will require more than technical performance: it should be introduced transparently, with clinician oversight, and in a stepwise way that builds familiarity without shifting responsibility away from the clinical team.

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