Shifting from black box decisions to informed decision-making in using artificial intelligence to analyze prostate MRI.
Authors
Affiliations (12)
Affiliations (12)
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Straße 2, Berlin, Germany.
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
- Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, Mount Sinai Hospital, and Women's College Hospital, University of Toronto, Toronto, Canada.
- Department of Radiology, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi, Genoa, Italy.
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Milan, Italy.
- Università Vita-Salute San Raffaele, Milan, Italy.
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, USA.
- Department of Urology, Institute of Science Tokyo, Tokyo, Japan.
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands.
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Baronissi, Italy.
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy. [email protected].
Abstract
Prostate magnetic resonance imaging (MRI) is central to the detection and localization of clinically significant prostate cancers. However, its interpretation is time-consuming and variable. Artificial intelligence (AI) tools for prostate MRI are rapidly emerging, with their performance approaching that of expert readers in select settings, although robust external validation in unselected real-world cohorts remains incomplete. Moreover, many deep learning models are limited in terms of transparency; that is, the "black box" problem hinders trust, safe deployment, and regulatory acceptance. This review summarizes the core concepts of interpretability and explainable artificial intelligence (XAI), highlights commonly used approaches to explain the output of AI models, and discusses how the lack of transparency hinders clinical deployment and how this can be addressed using standardized reporting frameworks. Ultimately, the purpose of this review is to draw attention to the pressing need for XAI and to spark interest and awareness of how standardized frameworks may help us to shift from "black box" decisions to informed decision-making in clinical practice.