Document - XAI in healthcare

2024

Conference article Open Access

XAI in healthcare

Gezici G., Metta C, Beretta A., Pellungrini R., Rinzivillo S., Pedreschi D., Giannotti F.

XAI, Healthcare, Deep Learning

The evolution of Explainable Artificial Intelligence (XAI) within healthcare represents a crucial turn towards more transparent, understandable, and patient-centric AI applications. The main objective is not only to increase the accuracy of AI models but also, and more importantly, to establish user trust in decision support systems through improving their interpretability. This extended abstract outlines the ongoing efforts and advancements of our lab addressing the challenges brought up by complex AI systems in healthcare domain. Currently, there are four main projects: Prostate Imaging Cancer AI, Liver Transplantation & Diabetes, Breast Cancer, and Doctor XAI, and ABELE.

Source: CEUR WORKSHOP PROCEEDINGS, vol. 3825, pp. 69-73. Malmö, Sweden, 10-11/06/2024

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BibTeX entry

@inproceedings{oai:iris.cnr.it:20.500.14243/525128,
	title = {XAI in healthcare},
	author = {Gezici G. and Metta C and Beretta A. and Pellungrini R. and Rinzivillo S. and Pedreschi D. and Giannotti F.},
	booktitle = {CEUR WORKSHOP PROCEEDINGS, vol. 3825, pp. 69-73. Malmö, Sweden, 10-11/06/2024},
	year = {2024}
}