AI in health and medicine
Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the
experiences of both clinicians and patients. We discuss key findings from a 2-year weekly …
experiences of both clinicians and patients. We discuss key findings from a 2-year weekly …
Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
Deep learning (DL) has the potential to transform medical diagnostics. However, the
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …
Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective
The simultaneous maturation of multiple digital and telecommunications technologies in
2020 has created an unprecedented opportunity for ophthalmology to adapt to new models …
2020 has created an unprecedented opportunity for ophthalmology to adapt to new models …
[HTML][HTML] A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study
Background There is no simple model to screen for Alzheimer's disease, partly because the
diagnosis of Alzheimer's disease itself is complex—typically involving expensive and …
diagnosis of Alzheimer's disease itself is complex—typically involving expensive and …
Applications of deep learning in fundus images: A review
The use of fundus images for the early screening of eye diseases is of great clinical
importance. Due to its powerful performance, deep learning is becoming more and more …
importance. Due to its powerful performance, deep learning is becoming more and more …
[HTML][HTML] From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare
The medicine and healthcare sector has been evolving and advancing very fast. The
advancement has been initiated and shaped by the applications of data-driven, robust, and …
advancement has been initiated and shaped by the applications of data-driven, robust, and …
Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey
Background Recent advances in deep learning techniques have led to improved diagnostic
abilities in ophthalmology. A generative adversarial network (GAN), which consists of two …
abilities in ophthalmology. A generative adversarial network (GAN), which consists of two …
Artificial intelligence to detect papilledema from ocular fundus photographs
Background Nonophthalmologist physicians do not confidently perform direct
ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk …
ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk …
[HTML][HTML] Trustworthy AI: closing the gap between development and integration of AI systems in ophthalmic practice
C González-Gonzalo, EF Thee, CCW Klaver… - Progress in retinal and …, 2022 - Elsevier
An increasing number of artificial intelligence (AI) systems are being proposed in
ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as …
ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as …
Cost-effectiveness of artificial intelligence as a decision-support system applied to the detection and grading of melanoma, dental caries, and diabetic retinopathy
Objective To assess the cost-effectiveness of artificial intelligence (AI) for supporting
clinicians in detecting and grading diseases in dermatology, dentistry, and ophthalmology …
clinicians in detecting and grading diseases in dermatology, dentistry, and ophthalmology …