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Explainable AI in Biomedical Research: A Systematic Review and Meta-Analysis
20 Pages Posted: 24 Jan 2023More...
Background: The black-box nature of most artificial intelligence (AI) models encourages the development of explainability methods to engender trust into the AI decision-making process. We aimed to analyze the possible role of Covid-19 in bringing explainable AI (XAI) to the forefront of biomedical research.
Methods: In this systematic review and meta-analysis, we searched in the PubMed database for studies published from Jan 1, 2010, to Nov 3, 2022. Biomedical XAI papers were extracted based on a set of pre-defined keywords. We automatically excluded papers that were not related to concepts of causality or explainability and manually labeled 1603 papers with respect to XAI categories that best describe the study. We compared the trends pre- and post- Covid -19 and fit a change point detection model to evaluate significant changes in publication rates.
Findings: We found an exponential growth of biomedical XAI papers. Specifically, an inflection period in the publication rate was observed in October 2020, when the quantity of XAI research in biomedical sciences surged upward significantly. When modeling the effect of the changepoint as a shift in time, we observed that such abrupt change would have been observed only 22 months later, had Covid -19 not happened.
Interpretation: The advent of Covid -19 in the beginning of 2020 could be the driving factor behind the increased focus concerning XAI, accounting for the several months necessary to analyze data and publish manuscripts. However, our analyses were done based on the review of titles and abstracts of the studies. Establishing the causal nature of this link would require careful mining of the content of these papers.
Funding: We acknowledge the funding received from the European Union’s Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Marie Skłodowska-Curie Grant Agreement No. 813533-MSCA-ITN-2018. I.S. was funded by the “DDAI” COMET Module within the COMET – Competence Centers for Excellent Technologies Programme, funded by the Austrian Federal Ministry for Transport, Innovation and Technology (bmvit), the Austrian Federal Ministry for Digital and Economic Affairs (bmdw), the Austrian Research Promotion Agency (FFG), the province of Styria (SFG) and partners from industry and academia. The COMET Programme is managed by FFG. Finally, we acknowledge the Big Data Value Association (BDVA), Brussels, Belgium.
Declaration of Interests: MRZ and VB are employees of IBM Research, Haifa, Israel. FM is an employee of Philips Research, Eindhoven, the Netherlands. IS has received funding from multiple funding agencies through a collaborative funding program and declare no support from any organization for the submitted work. P.J.N. receives funding from the Dutch Research Council (DWO) for the grant “Mobile Support Systems for Behavior Change”, of which he is the P.I. All other authors declare no competing interests.
Keywords: Explainable Artificial Intelligence, Systematic literature review, Covid-19
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