Abstract
To enhance the radiotherapy workflow, many artificial intelligence (AI) applications have been proposed. To date, only a limited number of the proposed AI applications have been implemented into clinical practice. Lack of trust is often mentioned as the limiting factor due to the inherent black-box characteristics of AI. Explainable AI (xAI) methods are being introduced as tool to alleviate the lack of trust in these non-transparent systems. To study the effect that xAI has on clinicians' trust, a survey was developed and distributed. Preliminary findings conclude that clinicians do not necessarily mistrust AI, yet, they seem to find transparency important. xAI could serve as a shared mental model (SMM) between the clinician and AI to maximize human-AI collaboration. Future work will look at the role that xAI plays in SMMs and how xAI must be designed to fully exploit AI for radiotherapy whilst remaining safe and ethical.
Original language | English |
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Pages (from-to) | 217-224 |
Number of pages | 8 |
Journal | CEUR Workshop Proceedings |
Volume | 3554 |
Publication status | Published - 1 Jan 2023 |
Event | Joint 1st World Conference on eXplainable Artificial Intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC - Lisbon, Portugal Duration: 26 Jul 2023 → 28 Jul 2023 https://xaiworldconference.com/2023/ |
Keywords
- Healthcare
- Implementation
- Radiotherapy
- Shared Mental Models
- Trust
- xAI design