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Related Experiment Video

Updated: Apr 19, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

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Published on: December 11, 2016

Role of Generative Artificial Intelligence in Assisting Systematic Review Process in Health Research: A Systematic

Muhammed Rashid¹, Cheng Su Yi², Thipsukhon Sathapanasiri¹

¹Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research

|August 23, 2025

Summary

Generative AI (GAI) shows promise in healthcare systematic reviews for formulating questions and extracting data. However, inconsistent reliability means GAI is not yet recommended for literature searches or study selection.

Keywords:

GPT artificial intelligence evidence synthesis healthcare systematic review

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

Published on: June 13, 2025

Area of Science:

Healthcare research
Artificial intelligence applications
Systematic review methodology

Background:

Artificial intelligence (AI) is increasingly utilized in healthcare.
Generative AI (GAI) is emerging as a tool for systematic review (SR) processes.
Evaluating GAI's performance in SR is crucial for its adoption.

Purpose of the Study:

To summarize performance metrics of Generative AI (GAI) in systematic review (SR) processes.
Assess GAI's effectiveness across different stages of SR.

Main Methods:

Searched multiple databases (PubMed, EMBASE, Scopus, ProQuest) up to March 2025.
Included experimental studies comparing GAI with other GAIs or human reviewers.
Assessed study quality using modified Quality Assessment of Diagnostic Accuracy Studies v2.
Employed a narrative approach for summarizing findings.

Main Results:

30 studies were included, utilizing GAI tools like ChatGPT, Bard, and Microsoft Bing AI.
GAI demonstrated effectiveness in PICO (participant, intervention, comparator, outcome) formulation and data extraction.
GAI is not recommended for literature search and study selection due to inconsistent reliability.
Evidence on GAI for risk of bias assessment was mixed.
Studies on GAI for study selection were generally high quality.

Conclusions:

GAI offers promising support for PICO formulation and data extraction in SR.
GAI has the potential to improve healthcare SR workflows.
Further validation and practical application studies are necessary for full integration.