AI-assisted sentinel lymph node examination and metastatic detection in breast cancer: the potential of ChatGPT for digital pathology research

Abstract

Objective: Traditional pathological examination of lymph nodes is labor-intensive and has shown variability in diagnostic accuracy. Recent advancements in artificial intelligence (AI) provide promising opportunities to enhance and standardize pathological workflows. AI-based image analysis models, particularly those utilizing deep learning algorithms, have demonstrated potential in automating and improving diagnostic accuracy in histopathology. This study aimed to evaluate the performance of a novel AI model known as ChatGPT-4 in detecting metastatic involvement in sentinel lymph nodes (SLNs) from breast cancer cases. Methods: We utilized digital slides from frozen sections, which are commonly employed intraoperatively, to assess the model's diagnostic accuracy. A total of 90 SLNs were retrospectively collected and analyzed using ChatGPT-4. The generated diagnoses were evaluated by two senior pathologists. Results: The AI model achieved an overall accuracy of 92.2%, with a sensitivity of 100% and specificity of 80.6%. The study highlights the practical applicability of AI in diagnosing SLN metastasis, emphasizing the importance of frozen sections in real-world scenarios. Conclusions: These findings suggest that integrating AI models like ChatGPT-4 into pathological workflows could enhance diagnostic accuracy and efficiency in breast cancer treatment.

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