Table 3.

The clinical and technical challenges in building and deploying deep learning (DL) techniques from ’bench to bedside’

Steps	Potential challenges
1. Identification of training data sets	Patients’ consent and confidentiality issues. Varying standards and regulations between the different institutional review boards. Small training data sets for rare disease (eg, ocular tumours) or common diseases that are not captured in routine (eg, cataracts).
2. Validation and testing data sets	Lack of sample size—not sufficiently powered. Lack of generalisability—not tested widely in different populations or on data collected from different devices.
3. Explainability of the results	Demonstration of the regions ‘deemed’ abnormal by DL. Methods to generate heat maps—occlusion tests, class activation, integrated gradient method, soft attention map and so on.
4. Clinical deployment of DL Systems	Recommendation of the potential clinical deployment sites. Application of regulatory approval from health authorities (eg, US Food and Drug Administration, Europe CE marking and so on). Conducting prospective clinical trials. Medical rebate scheme and medicolegal requirement. Ethical challenges.