In this cross-sectional survey of pwMS, we found moderate overall acceptance of AI in MS care, but acceptance was highly use-case dependent. Two patterns stood out: a pronounced “responsibility gradient,” with greater comfort for supportive applications (e.g., screening, chronic management) and lower comfort for high-stakes tasks (diagnosis, treatment selection), and experiential familiarity—operationalized as frequent general AI use —emerged as the strongest and most consistent predictor of acceptance, whereas clinical indicators such as disability and disease duration were not independently associated.

Interestingly, our results suggest that patient acceptance is shaped less by disease burden than by digital inclusion and everyday exposure to AI. While a recent lager multinational study of stationary patients found that poorer health status was a significant predictor of lower AI acceptance, we observed no such association with disability in our MS cohort (5).

The strong association between frequent general AI use and both the composite score and multiple single-item outcomes is consistent with the Technology Acceptance Model. This framework proposes that perceived usefulness and perceived ease of use are the primary drivers of adoption, both of which are likely enhanced by the patients’ prior experience and familiarity with the technology.(17). Importantly, prior work suggests pwMS often show strong health information engagement and limited information avoidance (18), indicating that reluctance toward AI may not stem from disinterest or low competence, but from uncertainty about how AI works, what it can and cannot do, and how it will be governed in clinical settings. This is clinically actionable: unlike disease severity, familiarity can be increased through guided, low-risk exposure and patient education (13, 19).

Acceptance was not uniform across clinical domains. PwMS were more comfortable with AI in supportive roles but more cautious when AI was framed as influencing high-stakes medical decisions. This aligns with evidence from other patient populations showing stronger acceptance of AI as an assistive tool than as an autonomous decision-maker (5, 20, 21). Moreover, the observed’responsibility gradient’ aligns with recent global findings suggesting that patient caution correlates directly with the personal stakes of the clinical application (5). Importantly, most respondents preferred a collaborative model where clinicians retain final responsibility. This preference should be treated as a design requirement rather than a barrier: AI tools in MS care are more likely to gain trust when implemented as clinician-support systems embedded in shared decision-making and accompanied by clear accountability structures (15, 22).

Regional and age-related differences further suggest that acceptance is shaped by context. Residency in Western Germany was associated with greater acceptance across several domains, which may reflect differences in digital infrastructure, exposure to health technologies, and trust environments described in German digital health literacy research (23, 24). Age showed a negative association, particularly for AI-supported chronic management, consistent with previous work indicating that older adults’ concerns often center on operational errors, loss of personal interaction, privacy/security, and unequal access to care (25–27). Together, these findings highlight a potential equity issue: groups that may benefit from monitoring and supportive AI tools could also be those with lower baseline comfort, underscoring the need for age-sensitive and region-sensitive implementation approaches.

Taken together, our findings argue against a “one-size-fits-all” rollout. A pragmatic approach would be to start with high-acceptance use cases (monitoring, screening, administrative support), where benefits are tangible and perceived risk is lower, and then iteratively expand toward more decision-critical applications as familiarity and trust grow. Because familiarity appears to be a key lever, implementation strategies could include low-stakes onboarding (e.g., scheduling/administrative chatbots), transparent communication about data use and limitations, and patient-facing explanations that emphasize augmentation rather than replacement of clinician judgement (5, 15, 22, 28).This staged strategy may be particularly relevant for older pwMS and for settings with lower baseline acceptance (29, 30). Given the growing evidence for potential of assistive AI system in the care of MS, this is an urgent matter that requires both good clinical judgements as well as roll-out strategies that are coordinated with pwMS.

This study provides, to our knowledge, one of the first empirical baselines of AI acceptance in pwMS across multiple clinical domains, using both a reliable composite measure and domain-specific outcomes. However, several limitations should be considered. The cross-sectional design precludes causal inference. Recruitment through academic centres and digital channels likely introduced selection bias toward digitally engaged and highly educated participants. although MS serves as a valuable model for data-driven chronic care, these findings may have limited generalizability to populations with lower educational attainment, different disease phenotypes, or less access to specialized care. Additionally, MS diagnosis and disability were self-reported and not clinically verified. Collapsing Likert responses improved model stability but reduced response granularity. Finally, attitudes were assessed during a period of intense public discourse on AI (including large language models), which may have influenced responses and may not fully reflect stable long-term views.

Ultimately, our results suggest that the successful integration of AI into MS care is not merely a technological challenge, but a sociotechnical one. While the potential for algorithmic precision and newer models is clear, our findings suggest that perceived clinical utility is closely linked to clear communication about the tool’s role and to users’ prior familiarity with AI-enabled technologies, adding granularity by showing that caution is not a blanket rejection. Moving forward, the priority must shift from simply validating model performance to designing’human-in-the-loop’ workflows that respect the patient’s clear preference for augmented rather than automated care. By treating patient familiarity as a modifiable factor—cultivated through active education and continuous, low-risk engagement—clinicians and developers can bridge the current trust gap, ensuring that AI evolves from a theoretical asset into a clinically integrated partner in the long-term management of multiple sclerosis (13).