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Health·P Space·Evidence-backed problem·Published 2026-08-23

Multidisciplinary dental treatment planning by artificial intelligence: A comparative evaluation

Abstract: Purpose The purpose of this study was to evaluate and compare four AI software programs-ChatGPT-5, Microsoft Copilot, Google Gemini (V 2.5), and OpenEvidence-in generating comprehensive dental treatment plans for minimally destructed and severely mutilated teeth using identical clinical inputs. Material and methods Ten anonymized clinical cases, each consisting of 1 intraoral photograph and 1 corresponding periapical radiograph, were independently submitted to each AI software program using a standardized prompt…

TRV-2026-0852Peer-reviewedPermanent record — cite & verify
Multidisciplinary dental treatment planning by artificial intelligence: A comparative evaluation

"22-0008-010 Camp Lejeune" by NavyMedicine is marked with Public Domain Mark 1.0. To view the terms, visit https://creativecommons.org/publicdomain/mark/1.0/.

The quick read

AI-generated responses were evaluated using a structured scoring rubric across five domains: diagnostic accuracy, restorability assessment, multidisciplinary integration, treatment sequencing, and extraction appropriateness (score range: 0-10 per case). Results Substantial inter-evaluator agreement was observed (κ = 0.74; 95% CI: 0.68-0.80), indicating consistent application of the scoring rubric.

Main points
  • Purpose The purpose of this study was to evaluate and compare four AI software programs-ChatGPT-5, Microsoft Copilot, Google Gemini (V 2.5), and OpenEvidence-in generating comprehensive dental treatment plans for minimally destructed and severely mutilated teeth using identical clinical inputs.
  • Material and methods Ten anonymized clinical cases, each consisting of 1 intraoral photograph and 1 corresponding periapical radiograph, were independently submitted to each AI software program using a standardized prompt.
  • A reference standard was established through consensus among four calibrated specialists (one surgically trained prosthodontist, one restorative dentist, one prosthodontist, and one endodontist).
Problem

OpenEvidence and ChatGPT-5 demonstrated greater agreement with the expert reference standard, although clinically significant diagnostic errors were observed across all evaluated systems.

The rundown

A reference standard was established through consensus among four calibrated specialists (one surgically trained prosthodontist, one restorative dentist, one prosthodontist, and one endodontist). AI-generated responses were evaluated using a structured scoring rubric across five domains: diagnostic accuracy, restorability assessment, multidisciplinary integration, treatment sequencing, and extraction appropriateness (score range: 0-10 per case).

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