Leveraging Artificial Intelligence for Evidence Synthesis: Screening, Extraction, and Synthesis

Presentation Time: 08:30 AM - 11:30 AM

Abstract Keywords: Artificial Intelligence, Information Retrieval, Information Extraction
Working Group: Consumer Health Informatics Working Group
Primary Track: Foundations

This three-hour workshop explores the integration of Artificial Intelligence (AI) into the evidence synthesis process,
including systematic reviews and meta-analyses, essential for quickly evolving informatics and digital health
research. AI has the potential to enhance efficiency by automating key tasks, but researchers must carefully assess
AI’s limitations, such as inaccuracies, biases, or “hallucinations,” to ensure the integrity of synthesized evidence.
This workshop will provide a structured overview of the evidence synthesis process, with participants introduced to
both traditional workflows and AI-based tools for different phases of evidence synthesis. Through hands-on activities, participants will work in small groups to apply AI tools in evidence synthesis while evaluating the
advantages, challenges, and ethical considerations of these tools.

The workshop is structured around three key phases, where participants will learn to use, test, and evaluate the pros and cons of various AI tools:

1. Literature Search and Screening: systematically searching databases and screening studies based on
predefined criteria to identify relevant literature.
2. Data Extraction: collecting key information from selected studies, such as study design, population,
intervention, and outcomes.
3. Data Synthesis: analyzing and integrating extracted data to identify patterns, relationships, and gaps in the
evidence base, forming the basis for conclusions.

The workshop will conclude with a group discussion and Q&A session, allowing participants to reflect on their
experience, discuss ethical considerations, and critically evaluate AI-generated outputs. By the end of the workshop,
participants will have a comprehensive understanding of how AI can support evidence synthesis and will gain
practical insights into responsible, transparent, and effective AI use.

Speakers:
Christie Martin, PhD, MPH, RN-BC, LHIT-HP
University of Minnesota School of Nursing

Grace Gao, PhD, DNP
St Catherine University

Jenna Marquard, PhD
University of Minnesota

Scott Sittig, PhD, MHI, RHIA
University of Louisiana at Lafayette

Vishala Mishra, MBBS
Duke

Julie Doberne, MD, PhD
Oregon Health & Science University

Zainab Balogun, MS, MA
University of Maryland Baltimore County

Authors:
Christie Martin, PhD, MPH, RN-BC, LHIT-HP - University of Minnesota School of Nursing; Grace Gao, PhD, DNP - St Catherine University; Jenna Marquard, PhD - University of Minnesota; Scott Sittig, PhD, MHI, RHIA - University of Louisiana at Lafayette; Vishala Mishra, MBBS - Duke; Julie Doberne, MD, PhD - Oregon Health & Science University; Zainab Balogun, MS, MA - University of Maryland Baltimore County; Sayantani Sarkar, PhD - Yale University; Ming-tse Tsai, MD MSHI - Kura Care; Akshitha Gopikrishnan, High School Student - Rock Hill High School; Caitlin Bakker, PhD(c), MLIS, AHIP-D - Dr. John Archer Library, University of Regina;

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