Background Deep learning offers considerable promise for medical diagnostics. We aimed
to evaluate the diagnostic accuracy of deep learning algorithms versus health-care …

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions
in retinal images and expediting the diagnosis of eye diseases and systemic disorders 1 …

Health data that are publicly available are valuable resources for digital health research.
Several public datasets containing ophthalmological imaging have been frequently used in …

Background Deep learning has the potential to transform health care; however, substantial
expertise is required to train such models. We sought to evaluate the utility of automated …

A number of large technology companies have created code-free cloud-based platforms
that allow researchers and clinicians without coding experience to create deep learning …

Deep learning may transform health care, but model development has largely been dependent
on availability of advanced technical expertise. Herein we present the development of a …

Among the most noteworthy developments in ophthalmology over the last decade has been
the emergence of quantifiable high-resolution imaging modalities, which are typically non-…

In recent years, there has been considerable interest in the prospect of machine learning
models demonstrating expert-level diagnosis in multiple disease contexts. However, there is …

Purpose To apply a deep learning algorithm for automated, objective, and comprehensive
quantification of optical coherence tomography (OCT) scans to a large real-world dataset of …

Importance The potential association of schizophrenia with distinct retinal changes is of clinical
interest but has been challenging to investigate because of a lack of sufficiently large and …