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Bioengineering
Volume 12
Issue 8
10.3390/bioengineering12080895
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Enhancing Clinical Decision Support with Adaptive Iterative Self-Query Retrieval for Retrieval-Augmented Large Language Models

by
Srinivasagam Prabha
1,†,
Cesar A. Gomez-Cabello
1,†,
Syed Ali Haider
1,
Ariana Genovese
1,
Maissa Trabilsy
1,
Nadia G. Wood
2,
Sanjay Bagaria
3,
Cui Tao
4 and
Antonio J. Forte
1,4,5,*
1
Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA
2
Department of Radiology AI IT, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA
3
Division of Surgical Oncology, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA
4
Department of AI and Informatics, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA
5
Center for Digital Health, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work and share the first authorship.
Bioengineering 2025, 12(8), 895; https://doi.org/10.3390/bioengineering12080895 (registering DOI)
Submission received: 30 July 2025 / Revised: 19 August 2025 / Accepted: 20 August 2025 / Published: 21 August 2025
(This article belongs to the Special Issue Application of Artificial Intelligence in Complex Diseases)
Versions Notes

Abstract

Retrieval-Augmented Generation (RAG) offers a promising strategy to harness large language models (LLMs) for delivering up-to-date, accurate clinical guidance while reducing physicians’ cognitive burden, yet its effectiveness hinges on query clarity and structure. We propose an adaptive Self-Query Retrieval (SQR) framework that integrates three refinement modules—PICOT (Population, Intervention, Comparison, Outcome, Time), SPICE (Setting, Population, Intervention, Comparison, Evaluation), and Iterative Query Refinement (IQR)—to automatically restructure and iteratively enhance clinical questions until they meet predefined retrieval-quality thresholds. Implemented on Gemini-1.0 Pro, we benchmarked SQR using thirty postoperative rhinoplasty queries, evaluating responses for accuracy and relevance on a three-point Likert scale and for retrieval quality via precision, recall, and F1 score; statistical significance was assessed by one-way ANOVA with Tukey post-hoc testing. The full SQR pipeline achieved 87% accuracy (Likert 2.4 ± 0.7) and 100% relevance (Likert 3.0 ± 0.0), significantly outperforming a non-refined RAG baseline (50% accuracy, 80% relevance; p < 0.01 and p = 0.03). Precision, recall, and F1 rose from 0.17, 0.39 and 0.24 to 0.53, 1.00, and 0.70, respectively, while PICOT-only and SPICE-only variants yielded intermediate improvements. These findings demonstrate that automated structuring and iterative enhancement of queries via SQR substantially elevate LLM-based clinical decision support, and its model-agnostic architecture enables rapid adaptation across specialties, data sources, and LLM platforms.
Keywords: self-query retrieval; retrieval-augmented generation; large language models; clinical decision support; decision support systems self-query retrieval; retrieval-augmented generation; large language models; clinical decision support; decision support systems

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MDPI and ACS Style

Prabha, S.; Gomez-Cabello, C.A.; Haider, S.A.; Genovese, A.; Trabilsy, M.; Wood, N.G.; Bagaria, S.; Tao, C.; Forte, A.J. Enhancing Clinical Decision Support with Adaptive Iterative Self-Query Retrieval for Retrieval-Augmented Large Language Models. Bioengineering 2025, 12, 895. https://doi.org/10.3390/bioengineering12080895

AMA Style

Prabha S, Gomez-Cabello CA, Haider SA, Genovese A, Trabilsy M, Wood NG, Bagaria S, Tao C, Forte AJ. Enhancing Clinical Decision Support with Adaptive Iterative Self-Query Retrieval for Retrieval-Augmented Large Language Models. Bioengineering. 2025; 12(8):895. https://doi.org/10.3390/bioengineering12080895

Chicago/Turabian Style

Prabha, Srinivasagam, Cesar A. Gomez-Cabello, Syed Ali Haider, Ariana Genovese, Maissa Trabilsy, Nadia G. Wood, Sanjay Bagaria, Cui Tao, and Antonio J. Forte. 2025. "Enhancing Clinical Decision Support with Adaptive Iterative Self-Query Retrieval for Retrieval-Augmented Large Language Models" Bioengineering 12, no. 8: 895. https://doi.org/10.3390/bioengineering12080895

APA Style

Prabha, S., Gomez-Cabello, C. A., Haider, S. A., Genovese, A., Trabilsy, M., Wood, N. G., Bagaria, S., Tao, C., & Forte, A. J. (2025). Enhancing Clinical Decision Support with Adaptive Iterative Self-Query Retrieval for Retrieval-Augmented Large Language Models. Bioengineering, 12(8), 895. https://doi.org/10.3390/bioengineering12080895

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