Clinical Manuscript: Feasibility and Proof-of-Concept of the Rapid Dx Analyzer Structured Prompting Protocol: Achieving High Diagnostic Concordance in a Retrospective Case Series    

Abstract

Aim/Background: Diagnostic uncertainty persists as a major driver of preventable patient harm in clinical practice. This feasibility study examined whether a structured data-submission protocol — the Rapid Dx Analyzer and Clinical Decision Tool (R-DA) — could improve the reliability of a commercial large language model (LLM) in generating differential diagnoses for complex clinical presentations. Methods: A single-user, retrospective case series was conducted. Twenty non-consecutive clinical cases were selected from an institutional database using pre-defined complexity criteria (involvement of two or more organ systems, three or more differential diagnoses, ambiguous or conflicting data, or time-todiagnosis exceeding 48 hours). For each case, a structured prompt was submitted to Gemini 3.0 Pro via its web interface. The primary outcome was diagnostic concordance — defined as the model\'s top-ranked output matching the confirmed clinical diagnosis (established by biopsy, surgical findings, or definitive clinical course). The clinician providing input was not blinded to the final diagnosis. Results: Concordance between the R-DA-generated output and the confirmed diagnosis was observed in all 20 cases (100%; 95% CI [Clopper-Pearson]: 83.2%–100%). This result should be interpreted with caution given the small sample size and the lack of independent adjudication. Conclusion: These preliminary findings suggest that structured prompt engineering may meaningfully improve LLM-assisted diagnostic reasoning. The R-DA protocol warrants further investigation through prospective, multi-center trials with blinded adjudication. This study does not support claims of specialistlevel performance, but provides a hypothesis-generating foundation for future validation work