TY - JOUR
T1 - Explainable AI in medical imaging
T2 - An overview for clinical practitioners - Saliency-based XAI approaches
AU - Borys, Katarzyna
AU - Schmitt, Yasmin Alyssa
AU - Nauta, Meike
AU - Seifert, Christin
AU - Krämer, Nicole
AU - Friedrich, Christoph M.
AU - Nensa, Felix
N1 - Publisher Copyright:
Copyright © 2023 Elsevier B.V. All rights reserved.
PY - 2023/5/1
Y1 - 2023/5/1
N2 - Since recent achievements of Artificial Intelligence (AI) have proven significant success and promising results throughout many fields of application during the last decade, AI has also become an essential part of medical research. The improving data availability, coupled with advances in high-performance computing and innovative algorithms, has increased AI's potential in various aspects. Because AI rapidly reshapes research and promotes the development of personalized clinical care, alongside its implementation arises an urgent need for a deep understanding of its inner workings, especially in high-stake domains. However, such systems can be highly complex and opaque, limiting the possibility of an immediate understanding of the system's decisions. Regarding the medical field, a high impact is attributed to these decisions as physicians and patients can only fully trust AI systems when reasonably communicating the origin of their results, simultaneously enabling the identification of errors and biases. Explainable AI (XAI), becoming an increasingly important field of research in recent years, promotes the formulation of explainability methods and provides a rationale allowing users to comprehend the results generated by AI systems. In this paper, we investigate the application of XAI in medical imaging, addressing a broad audience, especially healthcare professionals. The content focuses on definitions and taxonomies, standard methods and approaches, advantages, limitations, and examples representing the current state of research regarding XAI in medical imaging. This paper focuses on saliency-based XAI methods, where the explanation can be provided directly on the input data (image) and which naturally are of special importance in medical imaging.
AB - Since recent achievements of Artificial Intelligence (AI) have proven significant success and promising results throughout many fields of application during the last decade, AI has also become an essential part of medical research. The improving data availability, coupled with advances in high-performance computing and innovative algorithms, has increased AI's potential in various aspects. Because AI rapidly reshapes research and promotes the development of personalized clinical care, alongside its implementation arises an urgent need for a deep understanding of its inner workings, especially in high-stake domains. However, such systems can be highly complex and opaque, limiting the possibility of an immediate understanding of the system's decisions. Regarding the medical field, a high impact is attributed to these decisions as physicians and patients can only fully trust AI systems when reasonably communicating the origin of their results, simultaneously enabling the identification of errors and biases. Explainable AI (XAI), becoming an increasingly important field of research in recent years, promotes the formulation of explainability methods and provides a rationale allowing users to comprehend the results generated by AI systems. In this paper, we investigate the application of XAI in medical imaging, addressing a broad audience, especially healthcare professionals. The content focuses on definitions and taxonomies, standard methods and approaches, advantages, limitations, and examples representing the current state of research regarding XAI in medical imaging. This paper focuses on saliency-based XAI methods, where the explanation can be provided directly on the input data (image) and which naturally are of special importance in medical imaging.
KW - Black-Box
KW - Explainability
KW - Explainable AI
KW - Interpretability
KW - Medical imaging
KW - Radiology
KW - 2023 OA procedure
UR - http://www.scopus.com/inward/record.url?scp=85152164049&partnerID=8YFLogxK
U2 - 10.1016/j.ejrad.2023.110787
DO - 10.1016/j.ejrad.2023.110787
M3 - Review article
C2 - 37001254
AN - SCOPUS:85152164049
SN - 0720-048X
VL - 162
SP - 110787
JO - European journal of radiology
JF - European journal of radiology
ER -