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. 2024 Mar 19;150(3):140.
doi: 10.1007/s00432-024-05678-6.

Utilizing large language models in breast cancer management: systematic review

Vera Sorin  1   2 , Benjamin S Glicksberg  3 , Yaara Artsi  4 , Yiftach Barash  5   6 , Eli Konen  5 , Girish N Nadkarni  3   7 , Eyal Klang  3   7
Affiliations

Utilizing large language models in breast cancer management: systematic review

Vera Sorin et al. J Cancer Res Clin Oncol. .

Abstract

Purpose: Despite advanced technologies in breast cancer management, challenges remain in efficiently interpreting vast clinical data for patient-specific insights. We reviewed the literature on how large language models (LLMs) such as ChatGPT might offer solutions in this field.

Methods: We searched MEDLINE for relevant studies published before December 22, 2023. Keywords included: "large language models", "LLM", "GPT", "ChatGPT", "OpenAI", and "breast". The risk bias was evaluated using the QUADAS-2 tool.

Results: Six studies evaluating either ChatGPT-3.5 or GPT-4, met our inclusion criteria. They explored clinical notes analysis, guideline-based question-answering, and patient management recommendations. Accuracy varied between studies, ranging from 50 to 98%. Higher accuracy was seen in structured tasks like information retrieval. Half of the studies used real patient data, adding practical clinical value. Challenges included inconsistent accuracy, dependency on the way questions are posed (prompt-dependency), and in some cases, missing critical clinical information.

Conclusion: LLMs hold potential in breast cancer care, especially in textual information extraction and guideline-driven clinical question-answering. Yet, their inconsistent accuracy underscores the need for careful validation of these models, and the importance of ongoing supervision.

Keywords: Artificial intelligence; Breast cancer; GPT; Large language models.

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Conflict of interest statement

The authors declare no competing interests.

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Flow Diagram of the Inclusion Process. Flow diagram of the search and inclusion process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines
Fig. 2
Fig. 2
Applications of large language models in breast cancer care and the corresponding accuracies achieved in various tasks in the different studies
See this image and copyright information in PMC

References

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