Evaluating the quality, reliability and artificial intelligence involvement of online information on multiple sclerosis treatment

Vollständiger Abstract

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Aim: This study aimed to evaluate the quality, reliability and estimated artificial intelligence contribution of online information related to multiple sclerosis treatment.Material and Methods: The first 100 Google search results for "multiple sclerosis treatment" were screened. Video content, PDF documents, and inaccessible websites were excluded from the analysis. A total of 84 eligible web pages were assessed. Each page was categorized by source type (hospital, individual physician, blog, physiotherapy center, or association). Quality was evaluated using the DISCERN and JAMA benchmarks. Estimated artificial intelligence involvement was assessed using Turnitin's AI writing detection tool.Results: Of the analyzed content, 52.4% (n=44) originated from hospital websites, followed by individual physician websites (22.6%, n=19), blogs (14.3%, n=12), physiotherapy centers (6.0%, n=5), and associations (4.8%, n=4). The mean DISCERN score was 3.38 ± 0.72, indicating moderate-to-high reliability, while the mean JAMA score was 2.03 ± 0.85, highlighting limitations in authorship and source transparency. Varying levels of estimated artificial intelligence involvement were identified across the analyzed websites. Notably, individual physician websites demonstrated higher estimated artificial intelligence involvement (61.8%) than hospital websites (49.9%), with a statistically significant difference (p = 0.039). However, no significant differences were observed among website types regarding DISCERN and JAMA scores (p=0.724, p=0.336).Conclusion: Online information on multiple sclerosis treatment exhibits moderate reliability, with varying levels of estimated artificial intelligence involvement, particularly among individual healthcare providers. These findings underscore the need for standardization and oversight in the production of digital health content.