Potential of methods of artificial intelligence for quality assurance

Abstract

Procedures with artificial intelligence (AI), such as deep neural networks, show promising results in automatic analysis of ophthalmological imaging data. This article discusses to what extent the application of AI algorithms can contribute to quality assurance in the field of ophthalmology. Relevant aspects from the literature are discussed. Systems based on artificial deep neural networks achieve remarkable results in the diagnostics of eye diseases, such as diabetic retinopathy and are very helpful, for example by segmenting optical coherence tomographic (OCT) images and detecting lesion components with high fidelity. To train these algorithms large data sets are required. The quality and availability of such data sets determine the continuous improvement of the algorithms. The comparison between the AI algorithms and physicians for image interpretation has also enabled insights into the diagnostic concordance between physicians. Current challenges include the development of methods for modelling decision uncertainty and improved interpretability of automated diagnostic decisions. Systems based on AI can support decision making for physicians and thereby contribute to a more efficient quality assurance.