Related Experiment Video
Updated: Jul 16, 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
267
A historical perspective of biomedical explainable AI research
Luca Malinverno1, Vesna Barros2,3, Francesco Ghisoni1
1Porini SRL, Via Cavour, 222074 Lomazzo, Italy.
Patterns (New York, N.Y.)
|September 18, 2023
Summary
The COVID-19 pandemic accelerated the trend of explainable artificial intelligence (AI) in biomedical research. This study analyzed publication trends, showing a significant increase in explainable AI (XAI) research post-2020.
Area of Science:
- Biomedical research
- Artificial Intelligence
- Machine Learning
Background:
- The
- black-box
- nature of artificial intelligence (AI) models necessitates explainability methods to build trust in AI decision-making.
- Explainability methods are broadly categorized into post hoc explanations and inherently interpretable algorithms.
- The COVID-19 pandemic emerged as a significant global event in early 2020.
Purpose of the Study:
- To analyze the association between the COVID-19 pandemic and the increased focus on explainable AI (XAI) in biomedical research.
- To evaluate changes in publication rates of biomedical XAI studies before and after the onset of COVID-19.
Main Methods:
- Automated extraction of biomedical XAI studies related to causality or explainability from the PubMed database.
- Manual labeling of 1,603 papers into XAI categories.
- Application of a change point detection model to assess publication rate trends pre- and post-COVID-19.
Main Results:
- A significant increase in publication rates for biomedical XAI studies was observed following the advent of COVID-19 in early 2020.
- The COVID-19 pandemic appears to be a driving factor in accelerating the growing trend of XAI research in this field.
- The study identified a crucial role of the pandemic in intensifying the focus on XAI.
Conclusions:
- The COVID-19 pandemic has played a pivotal role in accelerating the adoption and research focus on explainable AI (XAI) within the biomedical domain.
- Future research should explore the societal impact and clinical applications of XAI technologies to foster trust in interpretable machine learning models.
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