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September 2, 2026Medical Journal of Western Black SeaOpen Access

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

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Authors

CSCansu SarıkayaHMHazal Ceren ManazoğluRKRana Karabudak

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Overview

Cross-sectional analysis reveals moderate reliability and substantial AI involvement in online multiple sclerosis treatment resources, suggesting the need for digital health content oversight.

Key Points

  • To evaluate the quality, reliability, and estimated artificial intelligence contribution in online information concerning multiple sclerosis treatment.
  • Screened the first 100 Google search results for "multiple sclerosis treatment," identifying 84 eligible websites after excluding video content, PDFs, and inaccessible links.
  • Categorized websites by source type (hospitals, individual physicians, blogs, physiotherapy centers, and associations) and evaluated quality using DISCERN and JAMA benchmarks.
  • Quantified estimated artificial intelligence involvement across pages using the Turnitin AI writing detection tool.
  • Websites showed a mean DISCERN score of 3.38 ± 0.72 and a mean JAMA score of 2.03 ± 0.85, with no significant differences across source categories (p = 0.724 and p = 0.336, respectively).
  • Estimated artificial intelligence involvement was significantly higher on individual physician websites (61.8%) than on hospital websites (49.9%, p = 0.039).
  • Hospital websites represented 52.4% (n=44) of analyzed content, followed by individual physician sites (22.6%, n=19), blogs (14.3%, n=12), physiotherapy centers (6.0%, n=5), and associations (4.8%, n=4).

Cite This Study

Sarıkaya et al. (2026) studied this question.

synapsesocial.com/papers/6a97e249c562ede874ec6565https://doi.org/10.29058/mjwbs.1969303
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