The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies
Indexed inarxivcrossrefpubmed
Abstract
Artificial intelligence (AI) has huge potential to improve the health and well-being of people, but adoption in clinical practice is still limited. Lack of transparency is identified as one of the main barriers to implementation, as clinicians should be confident the AI system can be trusted. Explainable AI has the potential to overcome this issue and can be a step towards trustworthy AI. In this paper we review the recent literature to provide guidance to researchers and practitioners on the design of explainable AI systems for the health-care domain and contribute to formalization of the field of explainable AI. We argue the reason to demand explainability determines what should be explained as this…
Citation impact
741
total citations
- FWCI
- 35.64
- Percentile
- 100%
- References
- 144
Citations per year
Authors
3Topics & keywords
Topics
Keywords
- Interpretability
- Computer science
- CLARITY
- Transparency (behavior)
- Terminology
- Trustworthiness
- Health care
- Field (mathematics)
No related works found for this paper.