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A historical perspective of biomedical explainable AI research

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A historical perspective of biomedical explainable AI research

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Malinverno, L.; Barros, V.; Ghisoni, F.; Visonà, G.; Kern, R.; Nickel, PJ.; Ventura, BE.... (2023). A historical perspective of biomedical explainable AI research. Patterns. 4(9). https://doi.org/10.1016/j.patter.2023.100830

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/212440

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Title: A historical perspective of biomedical explainable AI research
Author: Malinverno, Luca Barros, Vesna Ghisoni, Francesco Visonà, Giovanni Kern, Roman Nickel, Philip J. Ventura, Barbara Elvira Simic, Ilija Stryeck, Sarah Manni, Francesca Ferri Ramírez, César Jean-Quartier, Claire Genga, Laura Schweikert, Gabriele Lovric, Mario
UPV Unit: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Issued date:
Abstract:
[EN] The black-box nature of most artificial intelligence (AI) models encourages the development of explainability methods to engender trust into the AI decision-making process. Such methods can be broadly categorized into ...[+]
Subjects: Artificial intelligence (AI) , Black-box , Explainability , Trust , Decision-making process , Post hoc explanations , Inherently interpretable algorithms , COVID-19
Copyrigths: Reconocimiento (by)
Source:
Patterns. (eissn: 2666-3899 )
DOI: 10.1016/j.patter.2023.100830
Publisher:
Cell Press
Publisher version: https://doi.org/10.1016/j.patter.2023.100830
Project ID:
info:eu-repo/grantAgreement/EC/H2020/813533/EU/Machine Learning Frontiers in Precision Medicine/
Thanks:
We are extremely grateful for Prof. Chris Holmes for his critical reading and valuable comments. We acknowledge the funding received from the European Union's Framework Programme for Research and Innovation Horizon 2020 ...[+]
Type: Artículo

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