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20212026
most citedMedical Visual Question Answering: A Survey

173 citations · 210 across the 18 of their papers we have counts for

collaborators

20 papers

cs.AI2026

Empowering Locally Deployable Medical Agent via State Enhanced Logical Skills for FHIR-based Clinical Tasks

Wanrong Yang, Zhengliang Liu, Yuan Li +6

While Large Language Models demonstrate immense potential as proactive Medical Agents, their real-world deployment is severely bottlenecked by data scarcity under privacy constrain…

cs.CV2025

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…

cs.HC2025

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…

cs.CV2025

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…

eess.IV2025

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…

q-bio.TO2025

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…

cs.CL2025

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…

eess.IV2025

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…

eess.IV2024★ 4 cited

EyeDiff: text-to-image diffusion model improves rare eye disease diagnosis

Ruoyu Chen, Weiyi Zhang, Bowen Liu +5

The rising prevalence of vision-threatening retinal diseases poses a significant burden on the global healthcare systems. Deep learning (DL) offers a promising solution for automat…

eess.IV2024★ 1 cited

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…

cs.CV2024

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…

cs.CV2024★ 9 cited

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…

eess.IV2024

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…

eess.IV2024

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…

eess.IV2024

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…

cs.CV2024★ 9 cited

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…

cs.CL2024★ 4 cited

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…

cs.CL2024★ 4 cited

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…

cs.CV2022★ 6 cited

Camera Adaptation for Fundus-Image-Based CVD Risk Estimation

Zhihong Lin, Danli Shi, Donghao Zhang +3

Recent studies have validated the association between cardiovascular disease (CVD) risk and retinal fundus images. Combining deep learning (DL) and portable fundus cameras will ena…

cs.CV2021★ 173 cited

Medical Visual Question Answering: A Survey

Zhihong Lin, Donghao Zhang, Qingyi Tao +5

Medical Visual Question Answering~(VQA) is a combination of medical artificial intelligence and popular VQA challenges. Given a medical image and a clinically relevant question in…