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Title: | Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging |
Author: | Szabo, Liliana Raisi-Estabragh, Zahra Salih, Ahmed McCracken, Celeste Ruiz Pujadas, Esmeralda Gkontra, Polyxeni Kiss, Mate Maurovich-Horvath, Pal Vago, Hajnalka Merkely, Bela Lee, Aaron M. Lekadir, Karim Petersen, Steffen E. |
Keywords: | Intel·ligència artificial Aprenentatge automàtic Sistema cardiovascular Avaluació del risc per la salut Artificial intelligence Machine learning Cardiovascular system Health risk assessment |
Issue Date: | 8-Nov-2022 |
Publisher: | Frontiers Media |
Abstract: | A growing number of artificial intelligence (AI)-based systems are being proposed and developed in cardiology, driven by the increasing need to deal with the vast amount of clinical and imaging data with the ultimate aim of advancing patient care, diagnosis and prognostication. However, there is a critical gap between the development and clinical deployment of AI tools. A key consideration for implementing AI tools into real-life clinical practice is their 'trustworthiness' by end-users. Namely, we must ensure that AI systems can be trusted and adopted by all parties involved, including clinicians and patients. Here we provide a summary of the concepts involved in developing a 'trustworthy AI system.' We describe the main risks of AI applications and potential mitigation techniques for the wider application of these promising techniques in the context of cardiovascular imaging. Finally, we show why trustworthy AI concepts are important governing forces of AI development. |
Note: | Reproducció del document publicat a: https://doi.org/10.3389/fcvm.2022.1016032 |
It is part of: | Frontiers in Cardiovascular Medicine, 2022, vol. 9 |
URI: | http://hdl.handle.net/2445/194499 |
Related resource: | https://doi.org/10.3389/fcvm.2022.1016032 |
ISSN: | 2297-055X |
Appears in Collections: | Articles publicats en revistes (Matemàtiques i Informàtica) |
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