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The Future of Health Data Innovation: Accelerating Systematic Reviews: Using AI-Assisted Python Coding for Literature Screening [CME eligible]
The Future of Health Data Innovation
“Accelerating Systematic Reviews: Using AI-Assisted Python Coding for Literature Screening”
Featured Speaker
Lina Shehadeh, Ph.D.
Professor of Medicine, Division of Cardiology
University of Miami Miller School of Medicine
Research Health Scientist, Miami VA Medical Center
About Dr. Shehadeh
Dr. Lina Shehadeh is a Professor of Medicine in the Division of Cardiology at the University of Miami Miller School of Medicine and Research Health Scientist at the Miami VA Medical Center. Her NIH-, AHA-, and VA-funded laboratory investigates the role of metabolic dysfunction in Heart Failure with Preserved Ejection Fraction (HFpEF), and endothelial dysfunction in Long COVID.
Dr. Shehadeh’s expertise in computational biology, data mining, and machine learning enables her to leverage large genomic datasets to identify therapeutic targets. She is active in (bio)medical education research with a focus on integrating AI tools into research workflows, and recently published a methods paper in the American Journal of Physiology-Heart and Circulatory Physiology on using AI-assisted Python coding to accelerate systematic review screening.
Learning Objectives
At the conclusion of this activity, participants will be able to:
Describe how AI-assisted Python coding can be applied to screen hundreds to thousands of abstracts for systematic literature reviews
Identify the key steps in implementing a rule-based automation protocol for efficient and reproducible literature screening
Evaluate the advantages and limitations of AI-assisted screening compared to traditional manual approaches
Accreditation & Credits
Disclosures
Mitigation of Relevant Financial Relationships
The University of Miami adheres to the ACCME’s Standards for Integrity and Independence in Accredited Continuing Education. Any individuals in a position to control the content of a CE activity, including faculty, planners, reviewers or others are required to disclose all relevant financial relationships with ineligible entities (commercial interests). All relevant conflicts of interest have been mitigated prior to the commencement of the activity.
Contact Information
Organizer
Alex Gonzalez
786-708-7312 · axg6653@miami.edu
Event Category
Informatics and Health Data Science · Dept. of Medicine · Public Health Sciences