368 citations · 415 across the 10 of their papers we have counts for
10 papers
DeepER-Med: Advancing Deep Evidence-Based Research in Medicine Through Agentic AI
Zhizheng Wang, Chih-Hsuan Wei, Joey Chan +19
Trustworthiness and transparency are essential for the clinical adoption of artificial intelligence (AI) in healthcare and biomedical research. Recent deep research systems aim to…
LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology
Zhenyue Qin, Yang Liu, Yu Yin +13
Vision-threatening eye diseases pose a major global health burden, with timely diagnosis limited by workforce shortages and restricted access to specialized care. While multimodal…
Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology
Aidan Gilson, Xuguang Ai, Thilaka Arunachalam +19
Despite the potential of Large Language Models (LLMs) in medicine, they may generate responses lacking supporting evidence or based on hallucinated evidence. While Retrieval Augmen…
AI Workflow, External Validation, and Development in Eye Disease Diagnosis
Qingyu Chen, Tiarnan D L Keenan, Elvira Agron +35
Timely disease diagnosis is challenging due to increasing disease burdens and limited clinician availability. AI shows promise in diagnosis accuracy but faces real-world applicatio…
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
Rui Yang, Qingcheng Zeng, Keen You +13
This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation. Ascle is tailored for biomedical researchers and healthcar…
Multi-modal, multi-task, multi-attention (M3) deep learning detection of reticular pseudodrusen: towards automated and accessible classification of age-related macular degeneration
Qingyu Chen, Tiarnan D. L. Keenan, Alexis Allot +14
Objective Reticular pseudodrusen (RPD), a key feature of age-related macular degeneration (AMD), are poorly detected by human experts on standard color fundus photography (CFP) and…
Predicting risk of late age-related macular degeneration using deep learning
Yifan Peng, Tiarnan D. Keenan, Qingyu Chen +5
By 2040, age-related macular degeneration (AMD) will affect approximately 288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-t…
A deep learning approach for automated detection of geographic atrophy from color fundus photographs
Tiarnan D. Keenan, Shazia Dharssi, Yifan Peng +5
Purpose: To assess the utility of deep learning in the detection of geographic atrophy (GA) from color fundus photographs; secondary aim to explore potential utility in detecting c…
A multi-task deep learning model for the classification of Age-related Macular Degeneration
Qingyu Chen, Yifan Peng, Tiarnan Keenan +5
Age-related Macular Degeneration (AMD) is a leading cause of blindness. Although the Age-Related Eye Disease Study group previously developed a 9-step AMD severity scale for manual…
DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs
Yifan Peng, Shazia Dharssi, Qingyu Chen +5
In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AM…