Solving the explainable AI conundrum by bridging clinicians’ needs and developers’ goals

Explainable artificial intelligence (XAI) has emerged as a promising solution for addressing the implementation challenges of AI/ML in healthcare. However, little is known about how developers and clinicians interpret XAI and what conflicting goals and requirements they may have. This paper presents...

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Detalles Bibliográficos
Autores principales: Bienefeld, Nadine, Boss, Jens Michael, Lüthy, Rahel, Brodbeck, Dominique, Azzati, Jan, Blaser, Mirco, Willms, Jan, Keller, Emanuela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Brief Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202353/
https://www.ncbi.nlm.nih.gov/pubmed/37217779
http://dx.doi.org/10.1038/s41746-023-00837-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202353/
https://www.ncbi.nlm.nih.gov/pubmed/37217779
http://dx.doi.org/10.1038/s41746-023-00837-4

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