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Related Experiment Video

Updated: Sep 12, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Impact of an Evidence-Based Large Language Model (LLM) Diagnostic Decision Support System: A Randomised Controlled

Sangah Ahn¹, Joongheum Park^2,3, Sujeong Hur^1,3

¹SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.

Studies in Health Technology and Informatics

|August 8, 2025

Summary

Generative artificial intelligence (AI) can aid healthcare decisions but lacks evidence. This study examines AI

Keywords:

Clinical Decision Support System (CDSS)Emergency Medicine Large Language Model (LLM)

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Area of Science:

Clinical Informatics
Artificial Intelligence in Healthcare
Emergency Medicine

Background:

Generative artificial intelligence (AI) offers personalized treatment insights by analyzing patient data.
Current generative AI limitations include a lack of accurate evidence for clinical recommendations.
The integration of AI into clinical workflows requires careful evaluation.

Purpose of the Study:

To investigate the impact of AI-generated diagnostic suggestions on emergency healthcare providers' diagnostic patterns.
To evaluate how AI influences clinical decision-making in emergency settings.
To assess the relationship between clinician adoption of AI tools and diagnostic accuracy.

Main Methods:

Observational study design.
Analysis of diagnostic patterns before and after AI implementation.
Correlation analysis between clinician AI adoption rates and diagnostic accuracy metrics.

Main Results:

AI-generated suggestions altered diagnostic patterns among emergency healthcare providers.
Clinician adoption of AI diagnostic tools showed a correlation with changes in decision-making.
Further analysis is needed to quantify the precise impact on overall diagnosis accuracy.

Conclusions:

Generative AI influences clinical decision-making in emergency care.
The adoption of AI tools by clinicians warrants further investigation regarding diagnostic accuracy.
Future research should focus on validating AI evidence and optimizing its integration into clinical practice.