Explainable Artificial Intelligence (XAI) for Oncological Ultrasound Image Analysis: A Systematic Review-Reference-Cited by-同舟云学术

Explainable Artificial Intelligence (XAI) for Oncological Ultrasound Image Analysis: A Systematic Review

Published:2024-09-10 Issue:18 Volume:14 Page:8108
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Wyatt Lucie S.¹²,van Karnenbeek Lennard M.¹,Wijkhuizen Mark¹,Geldof Freija¹^ORCID,Dashtbozorg Behdad¹^ORCID

Affiliation:

1. Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands

2. Technical Medicine, Faculty of Mechanical, Maritime, and Materials Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands

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 25 March and 14 April 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.

Publisher

MDPI AG

Link

https://www.mdpi.com/2076-3417/14/18/8108/pdf

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