Subgroup analyses show significant between-model differences, with Copilot (75.25%) and ChatGPT-4 (72.97%) achieving the highest pooled accuracies¶

Claim · All claims
Evidence · 1 study · quant-synthesis r3 · q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q3 i? Subgroup analyses stratified by LLM architecture demonstrated statistically significant differences in performance (Q-between = 24.44, P = .011), with Copilot and ChatGPT-4 highest. → Dashti Mahmood 2026

Evidence¶

Dashti Mahmood 2026¶

Dashti Mahmood, Khosraviani Farshad, Meyari Atieh, Amirzade-Iranaq Mohammad Hosein, Chaurasia Akhilanand, Hefzi Delband, Ghadimi Niloofar, Tichy Antonin, Khurshid Zohaib, Schwendicke Falk. (2026). Accuracy of Large Language Models in Answering Dental Examination Questions: A Systematic Review and Meta-Analysis. International Dental Journal. https://doi.org/10.1016/j.identj.2026.109609

q3 · i? · quant-synthesis · r3

Model-stratified subgroup analysis within the meta-analysis of 39 studies. Copilot pooled accuracy was 75.25% (95% CI: 14.01%-100%, I²=24.9%) and ChatGPT-4 was 72.97% (95% CI: 67.01%-78.57%, I²=87.2%); the between-group test was significant (Q-between = 24.44, P = .011).

"Subgroup analyses stratified by architecture demonstrated statistically significant differences in performance (Q-between = 24.44, P = .011). Copilot (GPT family interface) and ChatGPT-4 (OpenAI interface) achieved the highest pooled estimates (Copilot: 75.25%; 95% CI: 14.01%-100%; ChatGPT4: 72.97%; 95% CI: 67.01%-78.57%)"

Discussion¶

Related Claims¶

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