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RESEARCH, PAPERS, AND PEER-REVIEWED EVIDENCE
Digital technology and virtual health have sparked tremendous interest in healthcare, especially during the COVID-19 pandemic. Globally, 250 million people are visually impaired, among whom 36 million are blind.
Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.
Large language models (LLMs) can respond to free-text queries without being specifically trained in the task in question, causing excitement and concern about their use in healthcare settings. ChatGPT is a generative artificial intelligence (AI) chatbot produced through sophisticated fine-tuning of an LLM, and other tools are emerging through similar developmental processes
An artificial intelligence (AI) using a deep-learning approach can classify retinal images from optical coherence tomography for early diagnosis of retinal diseases and has the potential to be used in other image-based medical diagnoses.
The past decade has allowed the development of a multitude of digital tools. Now they can be used to remediate the COVID-19 outbreak.
Deep learning is a novel machine learning technique that has been shown to be as effective as human graders in detecting diabetic retinopathy from fundus photographs. We used a cost-minimisation analysis to evaluate the potential savings of two deep learning approaches as compared with the current human assessment: a semi-automated deep learning model as a triage filter before secondary human assessment; and a fully automated deep learning model without human assessment.
