Related Experiment Video
Updated: Jul 29, 2025
05:47
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
356
[Evidence synthesis 2.0: how artificial intelligence is making systematic reviews more efficient.]
1Dipartimento di Ricerca traslazionale e delle nuove tecnologie in medicina e chirurgia, Università di Pisa.
Recenti Progressi in Medicina
|May 25, 2023
Summary
Systematic reviews (SRs) are being automated using artificial intelligence (AI) and Natural Language Processing (NLP) tools to improve efficiency and accuracy. These advancements help researchers manage the growing volume of scientific literature for evidence-based medicine.
Area of Science:
- Medical Informatics
- Evidence Synthesis
- Artificial Intelligence in Research
Context:
- Systematic reviews (SRs) are crucial for evidence-based medicine but face challenges due to increasing publication volume.
- The average SR takes eleven months to complete, delaying evidence collection and application.
- Keeping pace with a 4.10% annual increase in scientific publications is difficult for traditional SR methods.
Purpose:
- To explore the role of automation and artificial intelligence (AI) in enhancing systematic review efficiency.
- To categorize AI tools for SR automation, including visualization, active learning, and NLP-based approaches.
- To highlight the benefits of AI, such as reduced time and human error, particularly in study screening.
Summary:
- AI tools, especially those using Natural Language Processing (NLP), are transforming SRs by automating tasks like primary study screening.
- Current popular AI tools often employ a "human-in-the-loop" approach, integrating reviewer oversight with machine learning.
- Living systematic reviews and AI offer solutions to accelerate evidence synthesis and improve overall review quality.
Impact:
- AI integration in SRs promises increased reviewer efficiency and enhanced overall review quality.
- Automation can help address the challenge of an ever-increasing volume of scientific literature.
- Adoption of AI tools signifies a shift towards more dynamic and responsive evidence synthesis in healthcare.
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