Efficiency of artificial intelligence in identifying histological tissues from microscopic images

Histology is an essential, yet difficult, subject to study in medicine. As artificial intelligence (AI) is rapidly evolving with ever-growing image recognition abilities, it presents a great potential to be used in the study of histology. This research aimed to assess the ability of ChatGPT 4o and Google Gemini 2.0 Flash to recognize various histological sections of different tissues and organs from images. The two AI models were presented with high-resolution histological images and prompted with tasks to identify basic tissue types, specific organs, and specific structures indicated by arrows. Scores were given for correct identifications. The test was conducted twice without any training or feedback to assess consistency. McNemar test and Kappa coefficient were used to compare the responses. Both AI models had good image recognition abilities with varying performances. Overall, Google Gemini achieved higher correct scores across both tests (75% and 77.5%, compared to 65% and 65% for ChatGPT). McNemar showed a significant difference between the two models with only fair agreement shown by kappa. No significant difference was found between the repeated tests. Performance across different tissue types varied. Muscular tissue was the easiest to identify and epithelial tissue was the most difficult. AI may have a promising role in histology. Students and histologist can use AI, especially Gemini, to identify histological sections from images. Critical judgment, however, must be utilized as AI can make mistakes.