Berk B. Özmen, İbrahim Berber, Jerry T. Dang, Graham S. Schwarz, Matthew Kroh
This study aimed to develop and evaluate BariatricSurgeryGPT, a fine-tuned LLM specifically tailored for bariatric surgery to provide more accurate and clinically relevant responses to bariatric surgery-related questions.MethodsWe obtained 8764 bariatric surgery research abstracts published between January 1, 2020, and January 1, 2024, from PubMed.
BackgroundCommercially available large language models (LLMs) have demonstrated impressive capabilities in processing vast datasets and generating coherent narratives. However, their lack of domain-specific knowledge limits their reliability in clinical applications. This study aimed to develop and evaluate BariatricSurgeryGPT, a fine-tuned LLM specifically tailored for bariatric surgery to provide more accurate and clinically relevant responses to bariatric surgery-related questions.MethodsWe obtained 8764 bariatric surgery research abstracts published between January 1, 2020, and January 1, 2024, from PubMed. These abstracts were preprocessed and tokenized to fine-tune a pre-trained GPT-2 model using PyTorch and HuggingFace frameworks. The model's performance was evaluated using BLEU, METEOR, and ROUGE-1 scores on 20 clinically relevant bariatric surgery questions, each tested across nine temperature settings (0.1-0.9) for both the fine-tuned and baseline GPT-2 models, yielding 360 total evaluation instances.ResultsBariatricSurgeryGPT demonstrated consistent improvements over the baseline GPT-2 model across all metrics. The fine-tuned model achieved a BLEU score of 0.165 (vs 0.147 for baseline, 12.8% improvement), a METEOR score of 0.633 (vs 0.585, 8.2% improvement), and a ROUGE-1 score of 0.267 (vs 0.243, 9.7% improvement). These improvements indicate enhanced precision, recall, and semantic relevance in generating bariatric surgery-specific content.ConclusionBariatricSurgeryGPT represents the first domain-specific LLM for bariatric surgery and demonstrates the feasibility of developing specialty-specific AI tools with improved accuracy for clinical applications. The specialty-specific models could enhance surgical education through interactive learning tools, improve patient communication via personalized educational materials, and support clinical decision-making by providing evidence-based information synthesis.