aceLarge language model responses to structured textual descriptions of salivary gland imaging findings: impact of contextual enrichment on self-reported diagnostic confidence and reporting behavior
Purpose: Large language models (LLMs) are increasingly applied in clinical decision support, yet their behavior when presented with structured textual descriptions of imaging findings, particularly in salivary gland disease, remains poorly defined. The present study was set out to evaluate how the responses of different LLMs change as the textual input is progressively enriched. The models did not receive imaging data; all inputs were text. Methods: This exploratory, simulation-based observational study assessed…
Self-reported diagnostic confidence in three LLMs rose as prompts were enriched, which may reflect expressed certainty rather than better diagnostic reasoning, and no outputs were checked against a reference diagnosis.
The design was an exploratory pilot of 10 cases with text-only input and no reference diagnosis, so it cannot show diagnostic accuracy or image interpretation ability and does not support standalone diagnostic use.
Evidence
- Peer-reviewedEuropean Archives of Oto-Rhino-Laryngology2026-10-20
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Truvace Impact Record TRV-2026-2044, v1: “Large language model responses to structured textual descriptions of salivary gland imaging findings: impact of contextual enrichment on self-reported diagnostic confidence and reporting behavior.” Truvace, 2026-10-20. /record/TRV-2026-2044 (accessed at citation time). sha256 ddef7518e1ec0921…
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