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Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
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Mouse Models of Cancer Study02:43

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
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Evaluation of Large Language Models for Radiologists' Support in Multidisciplinary Breast Cancer Teams: Comparative

Hong Jiang1,2, Chun Yang3, Wenbin Zhou4

  • 1Faculty of Medicine, Macau University of Science and Technology, Macao, China.

JMIR Medical Informatics
|February 2, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) show promise in breast cancer diagnostics, with some models outperforming physicians in specific areas. However, LLMs cannot fully replace the clinical experience of physicians in complex breast cancer cases.

Keywords:
ACR BI-RADSAmerican College of Radiology Breast Imaging-Reporting and Data SystemLLMsNCCN guidelinesNational Comprehensive Cancer Networkbreast cancerclinical decision-makinglarge language modelsradiologistradiology assistance

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Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Artificial intelligence (AI) tools, specifically large language models (LLMs), demonstrate significant potential across diverse fields.
  • The efficacy of LLMs in breast cancer diagnosis and treatment remains largely unexplored.
  • This study investigates the role of LLMs in supporting radiologists within breast cancer multidisciplinary teams.

Purpose of the Study:

  • To evaluate the performance of various LLMs in assisting radiologists with breast cancer diagnosis and treatment.
  • To assess the capability of LLMs in facilitating informed clinical decisions and improving patient care in breast cancer management.
  • To compare LLM performance against radiologists with different experience levels.

Main Methods:

  • Developed a set of 50 questions based on breast cancer radiological and clinical guidelines.
  • Administered these questions to 9 popular LLMs (including ChatGPT-4.0, Claude 3 Opus, Gemini 1.5 Pro) and clinical physicians (residents, fellows, attending physicians).
  • Evaluated responses based on accuracy, confidence, and consistency against established guidelines (NCCN Breast Cancer Guidelines, ACR Breast Imaging-Reporting and Data System).

Main Results:

  • Claude 3 Opus and ChatGPT-4 demonstrated high confidence scores; ChatGPT-4o led in accuracy.
  • Claude 3 Opus, Claude 3.5 Sonnet, ChatGPT-4o, Gemini 1.5 Pro, and ChatGPT-4o mini showed superior response consistency.
  • ChatGPT-4o mini and Claude models outperformed physician groups in overall performance, though statistical significance varied; some LLMs showed comparable performance to physicians.

Conclusions:

  • LLMs like ChatGPT-4o and Claude 3 Opus show potential for supporting breast cancer diagnostics and therapy within multidisciplinary teams.
  • LLMs currently cannot fully replicate the nuanced decision-making abilities derived from extensive clinical experience, especially in complex cases.
  • Continuous AI development is crucial to enhance clinical applicability and ensure reliable integration into breast cancer care pathways.