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Updated: Sep 26, 2026
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Artificial Intelligence-Supported Evidence Synthesis: A Case Study of Smart Infusion Pump Interoperability
Carlos Sanchez-Piedra1,2, Ivo Heyerdahl-Viau3, Esther-Elena Garcia-Carpintero1,2
1Agency for Health Technology Assessment, Instituto de Salud Carlos III, 28029 Madrid, Spain.
Background/Objectives:
Artificial intelligence tools have emerged as promising methodological support for systematic reviews and health technology assessment (HTA). Smart infusion pump interoperability represents a relevant case study due to its implications for medication safety, nursing workflow, and hospital quality improvement. The aim was to evaluate the performance of artificial intelligence as a methodological support tool across a systematic review, using the evidence synthesis on smart infusion pump-electronic health record interoperability as a case study.
Methods:
A systematic review following PRISMA 2020 guidelines was conducted. Searches were performed in MEDLINE, Embase, and Cochrane Library databases. AI-assisted tools (ChatGPT GPT-4 and Open Science Reviewer) were incorporated into data extraction, reporting appraisal based on STROBE criteria, and exploratory identification of methodological limitations under strict human supervision. Concordance between AI-assisted and manual extraction was evaluated descriptively.
Results:
Overall concordance between AI-assisted and manual data extraction was 82.5% (99/120) across assessed variables. Agreement was highest for structured variables, including study design (10/10; 100.0%), study identification variables (19/20; 95.0%), and participant characteristics (36/40; 90.0%). Agreement was lower for study content variables (18/30; 60.0%) and methodological appraisal (16/20; 80.0%). Among the 21 discrepancies, misclassification errors were most common (13/21; 61.9%), followed by omissions (3/21; 14.3%), incomplete data (3/21; 14.3%), and hallucinations (2/21; 9.5%). AI-assisted identification of methodological limitations showed substantial descriptive agreement with human assessments but demonstrated limited capacity for judgmental interpretations.
Conclusions:
Artificial intelligence demonstrated utility for structured review tasks such as data extraction and reporting appraisal, but showed limitations in tasks requiring interpretative and methodological judgement. Human oversight therefore remains essential throughout the review process. These findings derive from a single case study and should not be generalised beyond the evaluated context and AI tools.
