Comparative Analysis of ChatGPT and Human Expertise in Diagnosing Primary Liver Carcinoma: A Focus on Gross Morphology

Abstract

Objective: This study aims to compare the diagnostic accuracy of customized ChatGPT and human experts in identifying primary liver carcinoma using gross morphology. Materials and Methods: Gross morphology images of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) cases were assessed. These images were analyzed by two versions of customized ChatGPT (e.g., with and without a scoring system), pathology residents, and pathologist assistants. The diagnostic accuracy and consistency of each participant group were evaluated. Results: The study analyzed 128 liver carcinoma images (62 HCC, 66 CCA) were analyzed, with the participation of 13 pathology residents (median experience of 1.5 years) and three pathologist assistants (median experience of 5 years). When augmented with a scoring system, ChatGPT’s performance was found to align closely with first- and second-year pathology residents and was inferior to third-year pathology residents and pathologist assistants, with statistical significance (p-values < 0.01). In contrast, the diagnostic accuracy of ChatGPT, when operating without the scoring system, was significantly lower than that of all human participants (p-values < 0.01). Kappa statistics indicated that the diagnostic consistency was slight to fair for both customized versions of ChatGPT and the pathology residents. It was noted that the interobserver agreement among the pathologist assistants was moderate. Conclusion: The study highlights the potential of ChatGPT for augmenting diagnostic processes in pathology. However, it also emphasizes the current limitations of this AI tool compared to human expertise, particularly among experienced participants. This suggests the importance of integrating AI with human judgment in diagnosticpathology.

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