ArticleFrontiers in artificial intelligence2024
Fine-tuning a local LLaMA-3 large language model for automated privacy-preserving physician letter generation in radiation oncology.
Article in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Schema Enforcement and Structured-Output Stability in Locally Deployed LLMs for Clinical Admission-Note Editing: A Proxy-Based Pre-Deployment Evaluation.Healthcare (Basel, Switzerland) · 2026Article
- Leveraging large language models to populate structured clinical case report forms from unstructured medical notes in radiation oncology.Clinical and translational radiation oncology · 2026Article
- Generative Artificial Intelligence Methodology Reporting in Otolaryngology: A Scoping Review.The Laryngoscope · 2026Article
- Curation and Extraction of Drug-Related Entities from Reddit Platform.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Large Language Model-Enhanced Drug Repositioning Knowledge Extraction via Long Chain-of-Thought: Development and Evaluation Study.JMIR medical informatics · 2025Article
- Article
- Cancer Vaccine Adjuvant Name Recognition from Biomedical Literature using Large Language Models.ArXiv · 2025Article
- Benchmarking GPT-5 in radiation oncology: measurable gains, but persistent need for expert oversight.Frontiers in oncology · 2025Article
- Tracking priming-induced language recovery in aphasia with pre-trained language models.Frontiers in artificial intelligence · 2025Article
- Structuring privacy policy: an AI approach.Frontiers in artificial intelligence · 2025Article
- Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare.ArXiv · 2024Article
- Leveraging Large Language Models for Cancer Vaccine Adjuvant Name Extraction from Biomedical Literature.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
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15 authors.
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Abstract
Introduction: Generating physician letters is a time-consuming task in daily clinical practice. Methods: This study investigates local fine-tuning of large language models (LLMs), specifically LLaMA models, for physician letter generation in a privacy-preserving manner within the field of radiation oncology. Results: Our findings demonstrate that base LLaMA models, without fine-tuning, are inadequate for effectively generating physician letters. The QLoRA algorithm provides an efficient method for local intra-institutional fine-tuning of LLMs with limited computational resources (i.e., a single 48 GB GPU workstation within the hospital). The fine-tuned LLM successfully learns radiation oncology-specific information and generates physician letters in an institution-specific style. ROUGE scores of the generated summary reports highlight the superiority of the 8B LLaMA-3 model over the 13B LLaMA-2 model. Further multidimensional physician evaluations of 10 cases reveal that, although the fine-tuned LLaMA-3 model has limited capacity to generate content beyond the provided input data, it successfully generates salutations, diagnoses and treatment histories, recommendations for further treatment, and planned schedules. Overall, clinical benefit was rated highly by the clinical experts (average score of 3.4 on a 4-point scale). Discussion: With careful physician review and correction, automated LLM-based physician letter generation has significant practical value.
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