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Updated: May 30, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
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Accelerating systematic reviews: a novel 1-wk screening protocol using rule-based automation with AI-assisted Python

Emely Robleto1,2,3, Lina A Shehadeh1,2,3

  • 1Interdisciplinary Stem Cell Institute, University of Miami Leonard M. Miller School of Medicine, Miami, Florida, United States.

American Journal of Physiology. Heart and Circulatory Physiology
|September 12, 2025
PubMed
Summary
This summary is machine-generated.

Traditional systematic reviews are too slow for the growing volume of research. This new protocol uses AI-assisted rule-based automation to complete systematic reviews in just one week, accelerating evidence synthesis.

Keywords:
AI-assisted code generationartificial intelligence in researchdigital research methodsrule-based automationsystematic reviews

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Area of Science:

  • Biomedical research and evidence synthesis
  • Medical informatics and research methodology

Background:

  • Exponential growth in academic publishing necessitates faster systematic review methods.
  • Traditional systematic reviews (6-24 months) cause delays in clinical implementation.
  • Existing automation tools have limitations, including machine learning dependencies and high computational costs.

Purpose of the Study:

  • To present a novel 1-week systematic review acceleration protocol.
  • To leverage rule-based automation and AI for efficient evidence synthesis.
  • To overcome barriers associated with traditional and current automated review methods.

Main Methods:

  • A 1-week protocol using rule-based automation with AI assistance for code generation.
  • Researchers define screening criteria; AI language models (Claude, ChatGPT) act as coding assistants.
  • Two-phase screening: rule-based title/abstract screening followed by rule-based full-text analysis, adhering to Cochrane and PRISMA guidelines.

Main Results:

  • Demonstrated application in a case study on cardiac fatty acid oxidation in heart failure.
  • Validated through a review of e-cigarette vs. traditional cigarette cardiopulmonary effects, processing 3,791 records.
  • The rule-based system offers immediate implementation, transparency, and a validation framework to minimize false negatives.

Conclusions:

  • The protocol significantly accelerates systematic reviews to a 1-week timeframe.
  • It eliminates machine learning dependencies, enabling immediate implementation and regulatory compliance.
  • This advancement makes high-quality evidence synthesis more accessible across scientific disciplines.