9 citations · 28 across the 19 of their papers we have counts for
21 papers
Operational digital twin clinics enable task-based evaluation of embodied AI
Xinyuan Wu, Jingrao Zhang, Mengdi Xu +3
Embodied artificial intelligence (AI) must be tested in the clinical environments where it will operate, but building realistic, robot-testable settings is costly and difficult to…
EyeWorld: A Generative World Model of Ocular State and Dynamics
Ziyu Gao, Xinyuan Wu, Xiaolan Chen +10
Ophthalmic decision-making depends on subtle lesion-scale cues interpreted across multimodal imaging and over time, yet most medical foundation models remain static and degrade und…
EyeAgent: An Agentic AI System for Multimodal Clinical Decision Support in Ophthalmology
Danli Shi, Xiaolan Chen, Bingjie Yan +24
Artificial intelligence has shown promise in medical imaging, yet most existing systems lack flexibility, interpretability, and adaptability - challenges especially pronounced in o…
APTOS-2024 challenge report: Generation of synthetic 3D OCT images from fundus photographs
Bowen Liu, Weiyi Zhang, Peranut Chotcomwongse +23
Optical Coherence Tomography (OCT) provides high-resolution, 3D, and non-invasive visualization of retinal layers in vivo, serving as a critical tool for lesion localization and di…
ChatMyopia: An AI Agent for Pre-consultation Education in Primary Eye Care Settings
Yue Wu, Xiaolan Chen, Weiyi Zhang +7
Large language models (LLMs) show promise for tailored healthcare communication but face challenges in interpretability and multi-task integration particularly for domain-specific…
Benchmarking Large Multimodal Models for Ophthalmic Visual Question Answering with OphthalWeChat
Pusheng Xu, Xia Gong, Xiaolan Chen +7
Purpose: To develop a bilingual multimodal visual question answering (VQA) benchmark for evaluating VLMs in ophthalmology. Methods: Ophthalmic image posts and associated captions p…
Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competition
Weiyi Zhang, Peranut Chotcomwongse, Yinwen Li +15
Diabetic macular edema (DME) significantly contributes to visual impairment in diabetic patients. Treatment responses to intravitreal therapies vary, highlighting the need for pati…
AI-powered virtual eye: perspective, challenges and opportunities
Yue Wu, Yibo Guo, Yulong Yan +5
We envision the "virtual eye" as a next-generation, AI-powered platform that uses interconnected foundation models to simulate the eye's intricate structure and biological function…
DeepSeek-R1 Outperforms Gemini 2.0 Pro, OpenAI o1, and o3-mini in Bilingual Complex Ophthalmology Reasoning
Pusheng Xu, Yue Wu, Kai Jin +3
Purpose: To evaluate the accuracy and reasoning ability of DeepSeek-R1 and three other recently released large language models (LLMs) in bilingual complex ophthalmology cases. Meth…
Fundus2Globe: Generative AI-Driven 3D Digital Twins for Personalized Myopia Management
Danli Shi, Bowen Liu, Zhen Tian +6
Myopia, projected to affect 50% population globally by 2050, is a leading cause of vision loss. Eyes with pathological myopia exhibit distinctive shape distributions, which are clo…
FFA Sora, video generation as fundus fluorescein angiography simulator
Xinyuan Wu, Lili Wang, Ruoyu Chen +6
Fundus fluorescein angiography (FFA) is critical for diagnosing retinal vascular diseases, but beginners often struggle with image interpretation. This study develops FFA Sora, a t…
Visual Question Answering in Ophthalmology: A Progressive and Practical Perspective
Xiaolan Chen, Ruoyu Chen, Pusheng Xu +4
Accurate diagnosis of ophthalmic diseases relies heavily on the interpretation of multimodal ophthalmic images, a process often time-consuming and expertise-dependent. Visual Quest…
Fundus to Fluorescein Angiography Video Generation as a Retinal Generative Foundation Model
Weiyi Zhang, Jiancheng Yang, Ruoyu Chen +7
Fundus fluorescein angiography (FFA) is crucial for diagnosing and monitoring retinal vascular issues but is limited by its invasive nature and restricted accessibility compared to…
EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis
Danli Shi, Weiyi Zhang, Jiancheng Yang +8
Early detection of eye diseases like glaucoma, macular degeneration, and diabetic retinopathy is crucial for preventing vision loss. While artificial intelligence (AI) foundation m…
Fundus2Video: Cross-Modal Angiography Video Generation from Static Fundus Photography with Clinical Knowledge Guidance
Weiyi Zhang, Siyu Huang, Jiancheng Yang +5
Fundus Fluorescein Angiography (FFA) is a critical tool for assessing retinal vascular dynamics and aiding in the diagnosis of eye diseases. However, its invasive nature and less a…
UWF-RI2FA: Generating Multi-frame Ultrawide-field Fluorescein Angiography from Ultrawide-field Retinal Imaging Improves Diabetic Retinopathy Stratification
Ruoyu Chen, Kezheng Xu, Kangyan Zheng +4
Ultrawide-field fluorescein angiography (UWF-FA) facilitates diabetic retinopathy (DR) detection by providing a clear visualization of peripheral retinal lesions. However, the intr…
Hypergraph Multi-modal Large Language Model: Exploiting EEG and Eye-tracking Modalities to Evaluate Heterogeneous Responses for Video Understanding
Minghui Wu, Chenxu Zhao, Anyang Su +8
Understanding of video creativity and content often varies among individuals, with differences in focal points and cognitive levels across different ages, experiences, and genders.…
Choroidal Vessel Segmentation on Indocyanine Green Angiography Images via Human-in-the-Loop Labeling
Ruoyu Chen, Ziwei Zhao, Mayinuer Yusufu +3
Human-in-the-loop (HITL) strategy has been recently introduced into the field of medical image processing. Indocyanine green angiography (ICGA) stands as a well-established examina…
EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging
Danli Shi, Weiyi Zhang, Xiaolan Chen +6
Artificial intelligence (AI) is vital in ophthalmology, tackling tasks like diagnosis, classification, and visual question answering (VQA). However, existing AI models in this doma…
Evaluating large language models in medical applications: a survey
Xiaolan Chen, Jiayang Xiang, Shanfu Lu +3
Large language models (LLMs) have emerged as powerful tools with transformative potential across numerous domains, including healthcare and medicine. In the medical domain, LLMs ho…
EyeGPT: Ophthalmic Assistant with Large Language Models
Xiaolan Chen, Ziwei Zhao, Weiyi Zhang +7
Artificial intelligence (AI) has gained significant attention in healthcare consultation due to its potential to improve clinical workflow and enhance medical communication. Howeve…