Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging.

Pahud de Mortanges, Aurélie; Luo, Haozhe; Shu, Shelley Zixin; Kamath, Amith; Suter, Yannick; Shelan, Mohamed; Pöllinger, Alexander; Reyes, Mauricio

Pahud de Mortanges, Aurélie; Luo, Haozhe; Shu, Shelley Zixin; Kamath, Amith; Suter, Yannick; Shelan, Mohamed; Pöllinger, Alexander; Reyes, Mauricio.

Afiliación

Pahud de Mortanges A; ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland. aurelie.pahuddemortanges@unibe.ch.
Luo H; ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Shu SZ; ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Kamath A; ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Suter Y; ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Shelan M; Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Pöllinger A; Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Reyes M; Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, Bern, Switzerland.

NPJ Digit Med ; 7(1): 195, 2024 Jul 22.

Article en En | MEDLINE | ID: mdl-39039248

ABSTRACT

ABSTRACT

Explainable artificial intelligence (XAI) has experienced a vast increase in recognition over the last few years. While the technical developments are manifold, less focus has been placed on the clinical applicability and usability of systems. Moreover, not much attention has been given to XAI systems that can handle multimodal and longitudinal data, which we postulate are important features in many clinical workflows. In this study, we review, from a clinical perspective, the current state of XAI for multimodal and longitudinal datasets and highlight the challenges thereof. Additionally, we propose the XAI orchestrator, an instance that aims to help clinicians with the synopsis of multimodal and longitudinal data, the resulting AI predictions, and the corresponding explainability output. We propose several desirable properties of the XAI orchestrator, such as being adaptive, hierarchical, interactive, and uncertainty-aware.

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: NPJ Digit Med Año: 2024 Tipo del documento: Article País de afiliación: Suiza Pais de publicación: Reino Unido

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