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

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.

Schlagwörter

Fachgebiet (DDC)

330 - Wirtschaft
610 - Medizin und Gesundheit

Projekt

Veranstaltung

Startdatum der Ausstellung

Enddatum der Ausstellung

Startdatum der Konferenz

Enddatum der Konferenz

Datum der letzten Prüfung

ISBN

ISSN

2398-6352

Sprache

Englisch

Während FHNW Zugehörigkeit erstellt

Nein

Zukunftsfelder FHNW

Publikationsstatus

Veröffentlicht

Begutachtung

Peer-Review der ganzen Publikation

Open Access-Status

Gold

Lizenz

Zitation

Mortanges, A. P. d., Luo, H., Shu, S. Z., Kamath, A., Suter, Y., Shelan, M., Poellinger, A., & Reyes, M. (2024). Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging. Npj Digital Medicine, 7(195). https://doi.org/10.1038/s41746-024-01190-w

Komplettanzeige