Healthcare-Focused Turkish Medical LLM: Training on Real Patient-Doctor Question-Answer Data for Enhanced Medical Insight
ACM Transactions on Asian and Low-Resource Language Information Processing, cilt.24, sa.11, 2025 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 24 Sayı: 11
- Basım Tarihi: 2025
- Doi Numarası: 10.1145/3772000
- Dergi Adı: ACM Transactions on Asian and Low-Resource Language Information Processing
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Anahtar Kelimeler: catastrophic forgetting, healthcare AI, low-rank adaptation, model fine-tuning, patient-doctor interactions, Turkish medical LLM
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
The development of a Turkish-specific Large Language Model (LLM) for healthcare presents a unique opportunity to enhance AI’s accessibility and relevance for Turkish-speaking medical practitioners and patients. This study introduces a specialized Turkish Medical LLM fine-tuned on over 167,732 real patient-doctor question-answer pairs sourced from a trusted medical platform and capturing authentic linguistics in Turkish medical language. Utilizing models like LLAMA 3, the fine-tuning process was supported by Low-Rank Adaptation (LoRA) and involved innovative methods to mitigate catastrophic forgetting, including spherical linear interpolation (Slerp) merging. Evaluation of the model’s performance through similarity scores, GPT-3.5 assessments, and expert reviews indicates significant improvement in the model’s ability to generate medically accurate responses. This Turkish Medical LLM demonstrates potential to support medical decision-making and patient interaction in Turkish healthcare settings, offering an essential resource for enhancing AI inclusivity across languages.