MedReadr: Development and Evaluation of an In-Browser, Rule-Based Natural Language Processing Algorithm to Estimate the Reliability of Consumer Health Articles
MedReadr demonstrates that structural reliability scoring of online health articles can be automated using a transparent, rule-based NLP approach. Applied to English-language articles from mainstream search results on common medical conditions, the tool showed strong agreement with validated manual scoring systems. However, it has only been validated on a narrow scope of content and is not designed to analyze search results for specific questions or detect misinformation. Future research should assess its performance across a broader range of web content and evaluate whether its integration improves patient comprehension, digital health literacy, and clinician-patient communication.
Authors
- Joshua Winograd (ORCID: https://orcid.org/0000-0002-7386-4323)
- Bilal Chughtai (ORCID: https://orcid.org/0000-0002-0515-2578)
- Andrew Julian Vickers (ORCID: https://orcid.org/0000-0003-1525-6503)
- Adithya Balasubramanian (ORCID: https://orcid.org/0000-0002-7912-8896)
- Naeem Bhojani (ORCID: https://orcid.org/0000-0003-2679-2635)
- Alia Codelia‐Anjum (ORCID: https://orcid.org/0000-0003-4301-1001)
- Dean Elterman (ORCID: https://orcid.org/0000-0003-1507-7783)
- Nikit Venishetty (ORCID: https://orcid.org/0000-0002-2289-3079)
- Kevin C. Zorn
- Autumn Kim
Institutions
- Northwell Health (US)
- Memorial Sloan Kettering Cancer Center (US)
- University of Toronto (CA)
- Washington University in St. Louis (US)
- Cornell University (US)
- Royal Victoria Hospital (CA)
- College Medical Center (US)
- Weill Cornell Medicine (US)
- Université de Montréal (CA)
- Texas Tech University Health Sciences Center (US)
Publication Details
- Journal
- Biomedicine Hub
- Published
- 2025-08-25
- DOI
- https://doi.org/10.1159/000548163
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00