Explainable artificial intelligence in ophthalmology
Ting Fang Tan, Peilun Dai, Xiaoman Zhang, Liyuan Jin, Stanley Poh, Dylan Hong, Joshua Lim, Gilbert Lim, Zhen Ling Teo, Nan Liu, Daniel Shu Wei Ting- Ophthalmology
- General Medicine
Purpose of review
Despite the growing scope of artificial intelligence (AI) and deep learning (DL) applications in the field of ophthalmology, most have yet to reach clinical adoption. Beyond model performance metrics, there has been an increasing emphasis on the need for explainability of proposed DL models.
Recent findings
Several explainable AI (XAI) methods have been proposed, and increasingly applied in ophthalmological DL applications, predominantly in medical imaging analysis tasks.
Summary
We summarize an overview of the key concepts, and categorize some examples of commonly employed XAI methods. Specific to ophthalmology, we explore XAI from a clinical perspective, in enhancing end-user trust, assisting clinical management, and uncovering new insights. We finally discuss its limitations and future directions to strengthen XAI for application to clinical practice.