A review of evaluation approaches for explainable AI with applications in cardiology.
Artif Intell Rev
; 57(9): 240, 2024.
Article
em En
| MEDLINE
| ID: mdl-39132011
ABSTRACT
Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-024-10852-w.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Artif Intell Rev
Ano de publicação:
2024
Tipo de documento:
Article
País de publicação:
Reino Unido