Multidisciplinary dental treatment planning by artificial intelligence: A comparative evaluation.
TL;DR
AI-generated treatment plans should be regarded as adjunctive decision-support tools requiring specialist oversight before clinical implementation, and OpenEvidence and ChatGPT-5 demonstrated greater agreement with the expert reference standard, although clinically significant diagnostic errors were observed across all evaluated systems.
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. 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). Agreement among evaluators was assessed using pairwise Cohen's kappa (κ) analysis based on initial independent scoring before consensus discussions.
Results
Substantial inter-evaluator agreement was observed (κ = 0.74; 95% CI: 0.68-0.80), indicating consistent application of the scoring rubric. Mean total scores were highest for OpenEvidence (7.7 ± 1.4) and ChatGPT-5 (7.6 ± 1.1), followed by Google Gemini (5.1 ± 1.2) and Microsoft Copilot (4.7 ± 3.1). OpenEvidence and ChatGPT-5 more frequently incorporated phased treatment sequencing, ferrule assessment, restorability analysis, and multidisciplinary treatment considerations. Microsoft Copilot failed to generate responses in three of the 10 evaluated cases because of content-filtering restrictions.
Conclusions
AI software programs generated structured and clinically relevant dental treatment proposals; however, meaningful variability existed in diagnostic interpretation, restorability assessment, multidisciplinary integration, and extraction thresholds. OpenEvidence and ChatGPT-5 demonstrated greater agreement with the expert reference standard, although clinically significant diagnostic errors were observed across all evaluated systems. AI-generated treatment plans should therefore be regarded as adjunctive decision-support tools requiring specialist oversight before clinical implementation.