Claims

GPT-4 outperforms GPT-3.5 and Bard on dental licensing examinations, achieving an integrated accuracy of 72%

Claim · All claims
Evidence · 1 study · quant-synthesis r3 · q3
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person · recheck by 2028: tool and model accuracy changes quickly

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q3 i? GPT-4 achieved an integrated accuracy of 72% (95% CI: 70%-74%) on dental licensing examinations, outperforming GPT-3.5 (54%) and Bard (56%). → Liu Mingxin 2025

Evidence

Liu Mingxin 2025

Liu Mingxin, Okuhara Tsuyoshi, Huang Wenbo, Ogihara Atsushi, Nagao Hikari Sophia, Okada Hiroko, Kiuchi Takahiro. (2025). Large Language Models in Dental Licensing Examinations: Systematic Review and Meta-Analysis. International Dental Journal. https://doi.org/10.1016/j.identj.2024.10.014

q3 · i? · quant-synthesis · r3

Meta-analysis of 7 studies testing GPT-4 on dental licensing examinations using a common effects model. The review reports "The integrate accuracy rate for GPT-4 was 72% (95% CI: 70%-74%)".

"In the results of GPT-4, since the I² was 24% (I² < 50%), a common effects model was used. The integrate accuracy rate for GPT-4 was 72% (95% CI: 70%-74%)"

Discussion

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