ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2024
Comparison of Prompt Engineering and Fine-Tuning Strategies in Large Language Models in the Classification of Clinical Notes.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Large Language Models Approximate Inter-Expert Agreement in Glaucoma Suspect and Glaucoma Classification from Multimodal Data.Ophthalmology science · 2026Article
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- LLMs as emerging tools for understanding and managing bone metastasis and cancer-induced bone pain.iScience · 2026Article
- Grounding large language models in clinical diagnostics.Nature communications · 2026Article
- Enhancing quality of antimicrobial prescribing through 'Ask Eolas' (language model): a user-testing and simulation evaluation.npj antimicrobials and resistance · 2026Article
- Generative Artificial Intelligence Methodology Reporting in Otolaryngology: A Scoping Review.The Laryngoscope · 2026Article
- Standardizing Heterogeneous MRI Series Description Metadata Using Large Language Models.Journal of imaging informatics in medicine · 2026Article
- Comparison of local large language models for extraction of signs and symptoms data from electronic health records.PloS one · 2026Article
- Clinical evaluation of large language model recommendations in melanoma: comparison with multidisciplinary tumor board decisions in a real-world cohort.Frontiers in oncology · 2026Article
- Optimising large language models for clinical information extraction: a benchmarking study in the context of ulcerative colitis research.BMJ digital health & AI · 2025Article
- Evaluation of large language models in patient education for hyperthyroidism: A comparative study of chatgpt, gemini, and deepseek.Endocrine · 2025Article
- Feasibility of extracting key elements from thoracic surgical operative notes using foundational large language models.Journal of thoracic disease · 2025Article
- The impact of fine-tuning LLMs on the quality of automated therapy assessed by digital patients.Npj mental health research · 2025Article
- Article
- Leveraging large language models for automated depression screening.PLOS digital health · 2025Article
- Medical accuracy of artificial intelligence chatbots in oncology: a scoping review.The oncologist · 2025Article
- Enabling Demonstrated Consent for Biobanking with Blockchain and Generative AI.The American journal of bioethics : AJOB · 2025Article
- Advancing Pancreatic Cancer Prediction with a Next Visit Token Prediction Head on Top of Med-BERT.Cancers · 2025Article
- Evaluation of LLMs accuracy and consistency in the registered dietitian exam through prompt engineering and knowledge retrieval.Scientific reports · 2025Article
- Bridging the gap: a practical step-by-step approach to warrant safe implementation of large language models in healthcare.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
The emerging large language models (LLMs) are actively evaluated in various fields including healthcare. Most studies have focused on established benchmarks and standard parameters; however, the variation and impact of prompt engineering and fine-tuning strategies have not been fully explored. This study benchmarks GPT-3.5 Turbo, GPT-4, and Llama-7B against BERT models and medical fellows' annotations in identifying patients with metastatic cancer from discharge summaries. Results revealed that clear, concise prompts incorporating reasoning steps significantly enhanced performance. GPT-4 exhibited superior performance among all models. Notably, one-shot learning and fine-tuning provided no incremental benefit. The model's accuracy sustained even when keywords for metastatic cancer were removed or when half of the input tokens were randomly discarded. These findings underscore GPT-4's potential to substitute specialized models, such as PubMedBERT, through strategic prompt engineering, and suggest opportunities to improve open-source models, which are better suited to use in clinical settings.
Identifiers
38827053PMC11141826What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.