TY - JOUR
AU - Malinverno, Luca
AU - Barros, Vesna
AU - Ghisoni, Francesco
AU - Visonà, Giovanni
AU - Kern, Roman
AU - Nickel, Philip J.
AU - Ventura, Barbara Elvira
AU - Šimić, Ilija
AU - Stryeck, Sarah
AU - Manni, Francesca
AU - Ferri, Cesar
AU - Jean-Quartier, Claire
AU - Genga, Laura
AU - Schweikert, Gabriele
AU - Lovrić, Mario
AU - Rosen-Zvi, Michal
PY - 2023
DA - September
TI - A historical perspective of biomedical explainable AI research
JO - Patterns
VL - 4
IS - 9
DO - https://doi.org/10.1016/j.patter.2023.100830
AB - 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. Such methods can be broadly categorized into two main types: post hoc explanations and inherently interpretable algorithms. We aimed at analyzing the possible associations between COVID-19 and the push of explainable AI (XAI) to the forefront of biomedical research. We automatically extracted from the PubMed database biomedical XAI studies related to concepts of causality or explainability and manually labeled 1,603 papers with respect to XAI categories. To compare the trends pre- and post-COVID-19, we fit a change point detection model and evaluated significant changes in publication rates. We show that the advent of COVID-19 in the beginning of 2020 could be the driving factor behind an increased focus concerning XAI, playing a crucial role in accelerating an already evolving trend. Finally, we present a discussion with future societal use and impact of XAI technologies and potential future directions for those who pursue fostering clinical trust with interpretable machine learning models.
PB - Elsevier
UR - https://discovery.dundee.ac.uk/en/publications/839c69ca-1cb3-4cd3-9800-2342e4c78886
KW - Coronavirus (COVID-19)
KW - Digital health and technology
ER