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

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.