Comparing customized ChatGPT and pathology residents in histopathologic description and diagnosis of common diseases

Abstract

This study aimed to evaluate and analyze the performance of a customized Chat Generative Pre-Trained Transformer (ChatGPT), known as GPT, against pathology residents in providing microscopic descriptions and diagnosing diseases from histopathological images. A dataset of representative photomicrographs from 70 diseases across 14 organ systems was analyzed by a customized version of ChatGPT-4 (GPT-4) and pathology residents. Two pathologists independently evaluated the microscopic descriptions and diagnoses using a predefined scoring system (0–4 for microscopic descriptions and 0–2 for pathological diagnoses), with higher scores indicating greater accuracy. Microscopic descriptions that received perfect scores, which included all relevant keywords and findings, were then presented to the standard version of ChatGPT to assess its diagnostic capabilities based on these descriptions. GPT-4 showed consistency in microscopic description and diagnosis scores across five rounds, accomplishing median scores of 50 % and 48.6 %, respectively. However, its performance was still inferior to junior and senior pathology residents (73.9 % and 93.9 % description scores and 63.9 % and 87.9 % diagnosis scores, respectively). When analyzing classic ChatGPT's understanding of microscopic descriptions provided by residents, it correctly diagnosed 35 (87.5 %) of cases from junior residents and 44 (68.8 %) from senior residents, given that the initial descriptions consisted of keywords and relevant findings. While GPT-4 can accurately interpret some histopathological images, its overall performance is currently inferior to that of pathology residents. However, ChatGPT's ability to accurately interpret and diagnose diseases from the descriptions provided by residents suggests that this technology could serve as a valuable support tool in pathology diagnostics.