368 citations
- National Institutes of HealthUS7 papers
- National Center for Biotechnology InformationUS3 papers
- United States National Library of MedicineUS3 papers
- Duke-NUS Medical SchoolSG2 papers
- National University of SingaporeSG2 papers
- University of PittsburghUS2 papers
- Yale UniversityUS2 papers
- Abterra Biosciences (United States)US1 paper
- AGRF LtdAU1 paper
- Alexander T. Augusta Military Medical CenterUS1 paper
- Amsterdam University Medical CentersNL1 paper
- Argonne National LaboratoryUS1 paper
8 papers
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…
Hidden flaws behind expert-level accuracy of multimodal GPT-4 vision in medicine
Qiao Jin, Fangyuan Chen, Yiliang Zhou +15
Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily…
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…
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography
Roy Schwartz, Hagar Khalid, Sandra Liakopoulos +12
Purpose - To develop and validate a deep learning (DL) framework for the detection and quantification of drusen and reticular pseudodrusen (RPD) on optical coherence tomography sca…
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…
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…