GPT-4 for Information Retrieval and Comparison of Medical Oncology Guidelines
Abstract
Oncologists face increasingly complex clinical decision-making processes as new cancer therapies are approved and treatment guidelines are revised at an unprecedented rate. With the aim of improving oncologists' efficiency and supporting their adherence to the most recent treatment recommendations, we evaluated the use of the large language model generative pretrained transformer 4 (GPT-4) to interpret guidelines from the American Society of Clinical Oncology and the European Society for Medical Oncology. The ability of GPT-4 to answer clinically relevant questions regarding the management of patients with pancreatic cancer, metastatic colorectal cancer, and hepatocellular carcinoma was assessed. We also assessed GPT-4 outputs with and without retrieval-augmented generation (RAG), which provided additional knowledge to the model, and then manually compared the results with the original guideline documents. GPT-4 with RAG provided correct responses in 84% of cases (of 218 statements, 184 were correct, 30 were inaccurate, and 4 were wrong). GPT-4 without RAG provided correct responses in only 57% of cases (of 163 statements, 93 were correct, 29 were inaccurate, and 41 were wrong). We showed that GPT-4, when enhanced with additional clinical information through RAG, can accurately identify detailed similarities and disparities in diagnostic and treatment proposals across different authoritative sources.
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Ferber et al. (2024) found that inclusion of RAG increased the rate of correct responses from 57% to 84% with a GPT-4 based RAG implementation for cancer patient management using oncology guidelines.
“Meanwhile, Ferber et al. (2024) look at implementing a GPT-4 based RAG system for providing guidance on cancer patient management using oncology guidelines. Findings show that the inclusion of RAG increased the rate of correct responses from 57% to 84% with a GPT-4 based RAG implementation.”
From Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines (2025) · cites this paper as Ferber et al. (2024)
The results confirm the findings of smaller-scale studies showing clear benefits of implementing RAG architectures over baseline LLMs.
“The results confirm the findings of smaller-scale studies, such as those by Ferber et al. (2024) and Kresevic et al. (2024), showing clear benefits of implementing RAG architectures over baseline LLMs.”
From Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines (2025) · cites this paper as Ferber et al. (2024)
In oncology, LLMs can serve as a rapid and reliable reference tool, for example by providing medical recommendation suggestions from official medical guidelines.
“In oncology, where clinical decision-making is increasingly complex, LLMs can serve as a rapid and reliable reference tool, for instance, by providing medical recommendation suggestions from official medical guidelines4.”
From Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology (2025) · cites this paper as [4]
RAG effectively addresses a major limitation in current LLMs, which often tend to hallucinate by providing wrong but seemingly correct or plausible answers to questions beyond their knowledge.
“by using RAG, our system effectively addresses a major limitation in current LLMs, which often tend to hallucinate by providing wrong but seemingly correct or plausible answers to questions beyond their knowledge4.”
From Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology (2025) · cites this paper as [4]
Previous work already showed that LLMs can reliably identify temporal differences in medical documents.
“Previous work already showed that LLMs can reliably identify temporal differences in medical documents4.”
From Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology (2025) · cites this paper as [4]
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- Authors
- Ferber, D., Wiest, I. C., Wölflein, G., Ebert, M. P., Beutel, G., Eckardt, J.-N., Truhn, D., Springfeld, C., Jäger, D., & Kather, J. N.
- Affiliations
- Heidelberg University (DE)
- University of St Andrews (GB)
- Medizinische Hochschule Hannover (DE)
- University Hospital Carl Gustav Carus (DE)
- Universitätsklinikum Aachen (DE)
- ORCID
- Venue
- NEJM AI
- Publisher
- Massachusetts Medical Society
- Published
- 2024-05-23
- Volume
- 1
- Issue
- 6
- Pages
- AIcs2300235-AIcs2300235
- ISSN
- 2836-9386 (ISSN-L)
- OpenAlex
- W4397003633
- Cited by
- 92
- References
- 20
- Citations by year
- 2026: 26 · 2025: 47 · 2024: 19
- FWCI
- 8.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NEJM AI (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- GPT-4, large language models, retrieval-augmented generation, guideline comparison, clinical decision support, pancreatic cancer, metastatic colorectal cancer, hepatocellular carcinoma, ASCO guidelines, ESMO guidelines, guideline adherence
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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