The Artificial Intelligence-Assisted Diagnosis of Skeletal Dysplasias in Pediatric Patients: A Comparative Benchmark Study of Large Language Models and a Clinical Expert Group
LLMs offer a complementary diagnostic value in skeletal dysplasias, especially in under-resourced medical settings. Their integration as a supportive tool in multidisciplinary diagnostic workflows may enhance early recognition and reduce diagnostic delays in rare disease care.
Authors
- Sladjana Todorovic (ORCID: https://orcid.org/0000-0002-9981-8923)
- Adrijan Sarajlija (ORCID: https://orcid.org/0000-0001-8024-6737)
- Aleksandar M. Vlahovic (ORCID: https://orcid.org/0000-0003-2168-5400)
- Sanja Panić Zarić
- Dimitrije Cvetković
- Gordana Bukara-Radujković
- M. Bogosavljević (ORCID: https://orcid.org/0000-0002-4710-4814)
- Ninoslav Begović (ORCID: https://orcid.org/0000-0002-5440-5404)
- Nina Marić (ORCID: https://orcid.org/0000-0003-3143-3098)
- Nikola Ilić (ORCID: https://orcid.org/0000-0003-1186-208X)
- Zoran Paunović (ORCID: https://orcid.org/0000-0003-0880-5666)
- Katarina Mitrović (ORCID: https://orcid.org/0000-0002-3328-8739)
- Jovana Krstić
Institutions
- University of Banja Luka (BA)
- University of East Sarajevo (BA)
- University of Belgrade (RS)
- Institute of Public Health of Serbia (RS)
- University clinical center of Republika Srpska (BA)
Publication Details
- Journal
- Genes
- Published
- 2025-06-28
- DOI
- https://doi.org/10.3390/genes16070762
- Citations
- 3
- Primary Topic
- Genomics and Rare Diseases
- Type
- article
- Field-Weighted Citation Impact
- 5.45