Semantic Modeling of Medical Specialty Relationships Using Large Language Models

This work proposes a computational framework for modeling semantic relationships between medical specialties using large language models. Forty-four medical specialties officially recognized in France were analyzed using Claude 4 Sonnet, GPT-4.1, and LLaMA 3.2 3B. Each model evaluated the relevance of 307 ICD-11 disease families, 260 educational teaching items, and 276 technical skills. From these ratings, criterion-specific similarity matrices were constructed and aggregated into composite matrices. The framework includes hierarchical clustering, substitution-coverage analysis, Mantel correlation tests, adjusted Rand index evaluation, and heatmap-based visualization of inter-model differences. Claude 4 Sonnet and GPT-4.1 produced highly consistent similarity structures, with a mean off-diagonal similarity of 0.867, a standard deviation of 0.045, and strong matrix correlation. LLaMA 3.2 3B generated more homogeneous patterns, indicating reduced differentiation while preserving global structure. Hierarchical clustering revealed five stable groups of specialties aligned with functional medical domains. At similarity thresholds above 0.90, most specialties had two to five semantically close candidates, suggesting a basis for exploratory analysis of short-term cross-specialty coverage under appropriate expert and institutional constraints. These results suggest that large language models can produce stable and interpretable representations of relationships between medical specialties. The proposed framework provides a data-driven approach for analyzing specialty proximity and can support exploratory applications in medical education structuring, cross-specialty coordination, and health-system planning.

@article{Bouajaja2026,
  title     = {Semantic Modeling of Medical Specialty Relationships Using Large Language Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {5},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Ismail Bouajaja and Omar Elfahim and Omar Bouattane and Oussama Barakat and Abdelaziz Daaif},
  doi       = {10.14569/IJACSA.2026.0170555},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170555}
}