ArticleAnnals of medicine2026
Assessing diagnostic performance of multimodal AI and human experts in oral and maxillofacial radiography: a comparative analysis of ChatGPT, Grok, and MANUS.
Article in Annals of medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundArtificial intelligence (AI), particularly large language models (LLMs), is increasingly applied to radiographic interpretation in healthcare. In dentistry, radiographic imaging is essential for diagnosis and treatment planning, yet remains subject to variability and human error. AI may enhance diagnostic accuracy and consistency.
aimTo evaluate and compare the diagnostic accuracy, consistency, and interpretive performance of multimodal AI models-ChatGPT, Grok, and MANUS-with expert radiologists in dental radiograph interpretation.
methodsA total of 120 anonymised radiographs (40 OPGs, 40 periapical, 40 CT slices) were selected from validated academic sources. Two board-certified oral and maxillofacial radiologists established gold standard diagnoses. Each image was independently assessed by the three AI models under standardised prompting. Diagnostic accuracy and intra-model consistency were analysed using descriptive statistics, Cohen's kappa, McNemar's test, and logistic regression.
resultsIn the first assessment, MANUS and ChatGPT achieved 92.5% accuracy (111/120), while Grok reached 88.3% (106/120). Performance improved in the second round: MANUS 95.0%, ChatGPT 93.3%, and Grok 90.8%, compared with 96.7% for radiologists. ChatGPT showed the highest reproducibility (κ = 0.937), whereas MANUS demonstrated the highest overall accuracy. Strong agreement was observed between ChatGPT and MANUS, with greater variability in Grok. No significant systematic bias was detected between AI outputs and radiologist benchmarks.
conclusionThe evaluated LLMs demonstrated diagnostic performance comparable to expert radiologists. MANUS excelled in accuracy and ChatGPT in reproducibility, supporting their potential as adjunct tools in dental radiology, while maintaining the need for expert oversight.Clinical trial number: Not applicable.
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