Large language models for patient-facing pathology report interpretation: A scoping review

Pathology reports are increasingly released directly to patients, but their diagnostic language is primarily designed for clinicians. This scoping review examined how large language models (LLMs) have been used for patient-facing pathology report interpretation, how generated outputs have been evaluated, and gaps related to health literacy, language, patient subgroups, and implementation.

This scoping review followed JBI methodology and PRISMA-ScR guidance. Six databases were searched for studies published from 1 January 2018 to 12 August 2026. Eligible studies reported empirical use or evaluation of LLMs for patient-facing interpretation, explanation, rewriting, question answering, or summarization based on pathology reports or report-like pathology texts. We extracted and descriptively synthesized study characteristics, LLM applications, patient-facing output tasks, evaluation methods, and reporting related to health literacy, language, patient subgroups, and implementation.

Nineteen studies were included. GPT-family models were evaluated in 17 studies, and report-level transformation was the most common patient-facing task (12/19, 63.2%). Fidelity was assessed in all 19 studies, safety in ten (52.6%), readability in nine (47.4%), and comprehension and usability in six studies each (31.6%). Five studies included patients or other non-clinician participants in the evaluation. No study evaluated outcomes by health-literacy level or cross-language performance, and prospective evaluation within clinical communication workflows was not reported.

LLM-based patient-facing pathology report interpretation shows potential to bridge specialist pathology language and patient communication. Evaluation should extend beyond readability to include fidelity to the original pathology report, patient understanding, safety, and usability. Validation with patients and in clinical communication settings is needed before routine use.