[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 …

Machine learning in precision diabetes care and cardiovascular risk prediction

EK Oikonomou, R Khera - Cardiovascular Diabetology, 2023 - Springer
Artificial intelligence and machine learning are driving a paradigm shift in medicine,
promising data-driven, personalized solutions for managing diabetes and the excess …

A deep learning system for predicting time to progression of diabetic retinopathy

L Dai, B Sheng, T Chen, Q Wu, R Liu, C Cai, L Wu… - Nature Medicine, 2024 - nature.com
Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk
of DR progression is highly variable among different individuals, making it difficult to predict …

Deep learning for diabetic retinopathy analysis: a review, research challenges, and future directions

MW Nadeem, HG Goh, M Hussain, SY Liew… - Sensors, 2022 - mdpi.com
Deep learning (DL) enables the creation of computational models comprising multiple
processing layers that learn data representations at multiple levels of abstraction. In the …

Leveraging physiology and artificial intelligence to deliver advancements in health care

A Zhang, Z Wu, E Wu, M Wu, MP Snyder… - Physiological …, 2023 - journals.physiology.org
Artificial intelligence in health care has experienced remarkable innovation and progress in
the last decade. Significant advancements can be attributed to the utilization of artificial …

Leveraging machine learning and artificial intelligence to improve peripheral artery disease detection, treatment, and outcomes

AM Flores, F Demsas, NJ Leeper, EG Ross - Circulation research, 2021 - Am Heart Assoc
Peripheral artery disease is an atherosclerotic disorder which, when present, portends poor
patient outcomes. Low diagnosis rates perpetuate poor management, leading to limb loss …

Potential applications and performance of machine learning techniques and algorithms in clinical practice: a systematic review

EM Nwanosike, BR Conway, HA Merchant… - International journal of …, 2022 - Elsevier
Purpose The advent of clinically adapted machine learning algorithms can solve numerous
problems ranging from disease diagnosis and prognosis to therapy recommendations. This …

Automated detection and diagnosis of diabetic retinopathy: A comprehensive survey

V Lakshminarayanan, H Kheradfallah, A Sarkar… - Journal of …, 2021 - mdpi.com
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few
years, artificial intelligence (AI) based approaches have been used to detect and grade DR …

Artificial intelligence in ophthalmology: The path to the real-world clinic

Z Li, L Wang, X Wu, J Jiang, W Qiang, H **e… - Cell Reports …, 2023 - cell.com
Artificial intelligence (AI) has great potential to transform healthcare by enhancing the
workflow and productivity of clinicians, enabling existing staff to serve more patients …

Artificial intelligence for diabetes care: current and future prospects

B Sheng, K Pushpanathan, Z Guan, QH Lim… - The Lancet Diabetes & …, 2024 - thelancet.com
Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise
care for people with diabetes and adapt treatments for complex presentations. However, the …