aceMultimodal artificial intelligence in dental diagnosis: influence of reasoning depth and domain-specific medical model on diagnostic performance
Large language models are increasingly explored as clinical decision-support tools, but the relationship between configurable reasoning effort and multimodal diagnostic performance remains unclear. This study evaluated the association between API-configured reasoning effort and multimodal oral and dental diagnostic performance in GPT-5 and compared its outputs with those of the compact open-weight medical-domain model MedGemma-1.5-4b-it. Eight anonymized multimodal case reports representing dental trauma, odonto…
In an eight-case dental benchmark, GPT-5 at high or medium reasoning effort scored highest and always included the reference diagnosis, outperforming minimal effort and a compact medical model.
The study used only eight cases, no pairwise comparison stayed significant after Holm correction, and the GPT-5 versus MedGemma contrast is not a controlled test of domain specialization; the association needs larger prospective confirmation.
Evidence
- Peer-reviewedOdontology2026-10-13
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Truvace Impact Record TRV-2026-1351, v1: “Multimodal artificial intelligence in dental diagnosis: influence of reasoning depth and domain-specific medical model on diagnostic performance.” Truvace, 2026-10-14. /record/TRV-2026-1351 (accessed at citation time). sha256 0b4d68534c2b618a…
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