Work

Performance of Large Language Models Across Diagnostic Tasks in Oral and Maxillofacial Pathology: A Systematic Review

Year: 2026

Type: review

Abstract: INTRODUCTION: Large language models (LLMs) are increasingly evaluated for diagnostic applications; however, studies in oral and maxillofacial pathology (OMFP) assess materially different diagnostic tasks and outcomes. This systematic review synthesized evidence according to the specific task evaluated. METHODS: PubMed, Web of Science, and Cochrane Central were searched without language or date restrictions, supplemented by gray-literature screening and citation tracking. Studies evaluating defined OMFP diagnostic tasks using task-appropriate outcomes were included. Screening, data extraction, and quality assessment were independently performed by 2 reviewers. Owing to clinical and methodological heterogeneity, evidence was synthesized narratively by diagnostic task. RESULTS: Sixteen studies published between 2024 and 2025 were included. Seven evaluated single-diagnosis identification, 6 differential-diagnosis generation or ranking, 5 binary or multiclass classification/detection, and 1 structured diagnostic report generation; counts were non-mutually exclusive. Exact-diagnosis performance varied considerably and did not demonstrate consistent specialist equivalence. Differential-diagnosis studies generally reported higher Top-k inclusion than Top-1 accuracy, but these outcomes did not establish exact diagnostic correctness. Classification studies frequently reported high specificity but variable sensitivity. The single report-generation study reported item-level rather than whole-case diagnostic outcomes. CONCLUSION: Large language models may support selected OMFP tasks, particularly differential-diagnosis generation. However, exact-diagnosis performance remains inconsistent, classification sensitivity is task-dependent, and evidence for structured report generation is limited. Current evidence does not support a single overall estimate of diagnostic performance or autonomous diagnostic use.

Source: Pathobiology

DOI: 10.1159/pat/aehag008

Authors: Sohaib Shujaat, Marryam Riaz, Hisham Albrikeet, Hongyang Ma