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Updated: Dec 12, 2025
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1-Overview of Knowledge Discovery
Maurizio Sessa1, Abdul Rauf Khan1,2, David Liang1
1Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
Aim:
To perform a systematic review on the application of artificial intelligence (AI) based knowledge discovery techniques in pharmacoepidemiology.
Study Eligibility Criteria:
Clinical trials, meta-analyses, narrative/systematic review, and observational studies using (or mentioning articles using) artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded.
Data Sources:
Articles recorded from 1950/01/01 to 2019/05/06 in Ovid MEDLINE were screened.
Participants:
Studies including humans (real or simulated) exposed to a drug.
Results:
In total, 72 original articles and 5 reviews were identified via Ovid MEDLINE. Twenty different knowledge discovery methods were identified, mainly from the area of machine learning (66/72; 91.7%). Classification/regression (44/72; 61.1%), classification/regression + model optimization (13/72; 18.0%), and classification/regression + features selection (12/72; 16.7%) were the three most frequent tasks in reviewed literature that machine learning methods has been applied to solve. The top three used techniques were artificial neural networks, random forest, and support vector machines models.
Conclusions:
The use of knowledge discovery techniques of artificial intelligence techniques has increased exponentially over the years covering numerous sub-topics of pharmacoepidemiology.
Systematic Review Registration:
Systematic review registration number in PROSPERO: CRD42019136552.
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