Comparative analysis of ChatGPT and Bard in answering pathology examination questions requiring image interpretation

Abstract

OBJECTIVES: To evaluate the accuracy of ChatGPT and Bard in answering pathology examination questions requiring image interpretation. METHODS: The study evaluated ChatGPT-4 and Bard's performance using 86 multiple-choice questions, with 17 (19.8%) focusing on general pathology and 69 (80.2%) on systemic pathology. Of these, 62 (72.1%) included microscopic images, and 57 (66.3%) were first-order questions focusing on diagnosing the disease. The authors presented these artificial intelligence (AI) tools with questions, both with and without clinical contexts, and assessed their answers against a reference standard set by pathologists. RESULTS: ChatGPT-4 achieved a 100% (n = 86) accuracy rate in questions with clinical context, surpassing Bard's 87.2% (n = 75). Without context, the accuracy of both AI tools declined significantly, with ChatGPT-4 at 52.3% (n = 45) and Bard at 38.4% (n = 33). ChatGPT-4 consistently outperformed Bard across various categories, particularly in systemic pathology and first-order questions. A notable issue identified was Bard's tendency to "hallucinate" or provide plausible but incorrect answers, especially without clinical context. CONCLUSIONS: This study demonstrated the potential of ChatGPT and Bard in pathology education, stressing the importance of clinical context for accurate AI interpretations of pathology images. It underlined the need for careful AI integration in medical education.

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