GlobalDentBench: A Multinational Benchmark for Evaluating LLM Clinical Reasoning in Dentistry with Expert Calibration

GlobalDentBench introduces the first multinational dental benchmark with 8,978 expert-validated questions across 14 specialties, revealing that current LLMs face severe limitations in clinical reasoning with a 31.01% unsafe recommendation rate. The study demonstrates performance degrades sharply as reasoning complexity increases, with accuracy dropping from 81.34% on multiple-choice to just 22.34% on case-based questions, highlighting critical safety gaps before LLMs can be deployed in healthcare.

GlobalDentBench represents a critical inflection point in AI healthcare validation. Researchers have constructed a rigorous multinational evaluation framework that exposes fundamental weaknesses in how leading LLMs approach clinical reasoning, moving beyond surface-level accuracy metrics to assess real-world safety implications. The benchmark's calibration by senior dentists achieved near-perfect agreement rates, establishing credibility that extends beyond academic exercise into practical validation methodology.

The performance degradation pattern is particularly significant: while LLMs achieve reasonable results on knowledge recall tasks, their ability to handle complex case-based scenarios—precisely where clinical judgment matters most—collapses to 22.34% accuracy. This stepwise decline suggests reasoning complexity triggers fundamental architectural limitations rather than training data insufficiency. The 31.01% unsafe recommendation rate, with 4.51% carrying risks of irreversible harm, quantifies what practitioners already suspect: current models lack the contextual reasoning necessary for clinical decision support.

For AI developers and healthcare institutions, this work establishes a scalable evaluation template that will likely become industry standard. The emphasis on specialist-specific performance variations reveals that generic LLM benchmarks mask dangerous failure modes in specialized domains. Orthodontics and other high-risk specialties demonstrate particular vulnerability.

The immediate consequence is clear pushback against premature clinical AI deployment. Regulatory bodies will likely cite GlobalDentBench as evidence requiring extensive validation frameworks before integration into clinical workflows. Organizations investing in healthcare AI face pressure to demonstrate performance across comparable specialized benchmarks. The research suggests progress requires architectural innovations rather than incremental model scaling, fundamentally reshaping development priorities in medical AI.

• →LLM accuracy plummets from 81.34% on multiple-choice to 22.34% on complex case-based dental questions, exposing reasoning limitations
• →31.01% of LLM-generated clinical recommendations are unsafe, with 4.51% risking irreversible patient harm across dental specialties
• →Performance degrades predictably with reasoning complexity levels, dropping from 74.01% at basic knowledge recall to 35.71% at individualized reasoning
• →Expert calibration by six senior dentists achieved 99.98% agreement, establishing GlobalDentBench as methodologically rigorous standard for healthcare AI evaluation
• →The benchmark spans 14 dental specialties across 88 countries, providing multinational validation framework that will likely influence healthcare AI regulation