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Utilizing large language models in breast cancer management: systematic review
Vera Sorin1,2, Benjamin S Glicksberg3, Yaara Artsi4
1Department of Diagnostic Imaging, Chaim Sheba Medical Center, Affiliated to the Sackler School of Medicine, Tel-Aviv University, Emek Haela St. 1, 52621, Ramat Gan, Israel. verasrn@gmail.com.
Large language models (LLMs) like ChatGPT show promise for breast cancer care, aiding in data interpretation and question-answering. However, their accuracy varies, requiring careful validation and human oversight for clinical use.
Area of Science:
- Artificial Intelligence in Oncology
- Clinical Informatics
- Natural Language Processing
Background:
- Interpreting extensive clinical data for personalized breast cancer insights remains challenging despite technological advancements.
- Large language models (LLMs) offer potential solutions for processing and analyzing complex medical information.
Conclusions:
- LLMs demonstrate potential in breast cancer care, particularly for extracting textual information and answering guideline-based questions.
- Inconsistent accuracy necessitates rigorous validation and continuous human supervision for safe and effective clinical implementation.
- Further research is needed to optimize LLM performance and integration into breast cancer workflows.
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