Version 1
: Received: 28 June 2024 / Approved: 28 June 2024 / Online: 1 July 2024 (09:08:36 CEST)
How to cite:
Wyatt, L.; van Karnenbeek, L.; Wijkhuizen, M.; Geldof, F.; Dashtbozorg, B. Explainable Artificial Intelligence (XAI) for Oncological Ultrasound Image Analysis: A Systematic Review. Preprints2024, 2024070036. https://doi.org/10.20944/preprints202407.0036.v1
Wyatt, L.; van Karnenbeek, L.; Wijkhuizen, M.; Geldof, F.; Dashtbozorg, B. Explainable Artificial Intelligence (XAI) for Oncological Ultrasound Image Analysis: A Systematic Review. Preprints 2024, 2024070036. https://doi.org/10.20944/preprints202407.0036.v1
Wyatt, L.; van Karnenbeek, L.; Wijkhuizen, M.; Geldof, F.; Dashtbozorg, B. Explainable Artificial Intelligence (XAI) for Oncological Ultrasound Image Analysis: A Systematic Review. Preprints2024, 2024070036. https://doi.org/10.20944/preprints202407.0036.v1
APA Style
Wyatt, L., van Karnenbeek, L., Wijkhuizen, M., Geldof, F., & Dashtbozorg, B. (2024). Explainable Artificial Intelligence (XAI) for Oncological Ultrasound Image Analysis: A Systematic Review. Preprints. https://doi.org/10.20944/preprints202407.0036.v1
Chicago/Turabian Style
Wyatt, L., Freija Geldof and Behdad Dashtbozorg. 2024 "Explainable Artificial Intelligence (XAI) for Oncological Ultrasound Image Analysis: A Systematic Review" Preprints. https://doi.org/10.20944/preprints202407.0036.v1
Abstract
This review provides an overview of eXplainable AI (XAI) methods for oncological ultrasound image analysis and compares their performance evaluations. A systematic search of Medline Embase and Scopus between March 25 and April 14 2024 identified 17 studies describing 14 XAI methods, including visualization, semantics, example-based, and hybrid functions. These methods primarily provided specific, local, and post-hoc explanations. Performance evaluations focused on AI model performance, with limited assessment of explainability impact. Standardized evaluations incorporating clinical end-users are generally lacking. Enhanced XAI transparency may facilitate AI integration into clinical workflows. Future research should develop real-time methodologies and standardized quantitative evaluative metrics.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
