72 citations · 108 across the 32 of their papers we have counts for
33 papers
Prediction bias in biological ageing markers
Yiqun Lin, Ariel Yuhan Ong, Matthew Yu Heng Wong +52
Biological ageing markers have attracted growing interest, with models estimating age from organ imaging or blood biomarkers. An estimated age above chronological age or the age-sp…
A specialized reasoning large language model for accelerating rare disease diagnosis: a randomized AI physician assistance trial
Haichao Chen, Songchi Zhou, Zhengyun Zhao +28
Rare diseases affect millions of individuals worldwide, yet timely diagnosis remains a major public health challenge due to scarcity of specialized clinical expertise. While large…
Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine
Peisong Zhang, Manqiang Peng, Yuxuan Wu +21
Estimating individualized treatment effects from longitudinal observational data is central to data-driven medicine, yet existing methods face a fundamental limitation: reducing co…
VOLMO: Versatile and Open Large Models for Ophthalmology
Zhenyue Qin, Younjoon Chung, Elijah Lee +16
Vision impairment affects millions globally, and early detection is critical to preventing irreversible vision loss. Ophthalmology workflows require clinicians to integrate medical…
Deliberative multi-agent large language models improve clinical reasoning in ophthalmology
Ehsan Misaghi, Sean T Berkowitz, Bing Yu Chen +10
Large language models (LLMs) show potential for ophthalmic clinical reasoning, yet individual models risk introducing harm. We evaluated whether multi-agent LLM deliberative counci…
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…
Toward Federated Large Language Models in Medicine: A Parameter-Efficient Framework for Privacy-Preserving, Multi-Institutional Adaptation
Anran Li, Yuanyuan Chen, Wenjun Long +16
Large language models (LLMs) are increasingly adapted for medical applications, but most are trained using data from a single institution because privacy and governance constraints…
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…
EVLF-FM: Explainable Vision Language Foundation Model for Medicine
Yang Bai, Haoran Cheng, Yang Zhou +40
Despite the promise of foundation models in medical AI, current systems remain limited - they are modality-specific and lack transparent reasoning processes, hindering clinical ado…
Generalist versus Specialist Vision Foundation Models for Ocular Disease and Oculomics
Yukun Zhou, Paul Nderitu, Jocelyn Hui Lin Goh +20
Medical foundation models, pre-trained with large-scale clinical data, demonstrate strong performance in diverse clinically relevant applications. RETFound, trained on nearly one m…
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…
Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implications
Anran Li, Lingfei Qian, Mengmeng Du +18
Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pre-training or fine-tuning on medical data to e…
FusionFM: Fusing Eye-specific Foundational Models for Optimized Ophthalmic Diagnosis
Ke Zou, Jocelyn Hui Lin Goh, Yukun Zhou +11
Foundation models (FMs) have shown great promise in medical image analysis by improving generalization across diverse downstream tasks. In ophthalmology, several FMs have recently…
Performance of GPT-5 Frontier Models in Ophthalmology Question Answering
Fares Antaki, David Mikhail, Daniel Milad +11
Large language models (LLMs) such as GPT-5 integrate advanced reasoning capabilities that may improve performance on complex medical question-answering tasks. For this latest gener…
Human-like Content Analysis for Generative AI with Language-Grounded Sparse Encoders
Yiming Tang, Arash Lagzian, Srinivas Anumasa +9
The rapid development of generative AI has transformed content creation, communication, and human development. However, this technology raises profound concerns in high-stakes doma…
EH-Benchmark Ophthalmic Hallucination Benchmark and Agent-Driven Top-Down Traceable Reasoning Workflow
Xiaoyu Pan, Yang Bai, Ke Zou +5
Medical Large Language Models (MLLMs) play a crucial role in ophthalmic diagnosis, holding significant potential to address vision-threatening diseases. However, their accuracy is…
An integrated language-vision foundation model for conversational diagnostics and triaging in primary eye care
Zhi Da Soh, Yang Bai, Kai Yu +28
Current deep learning models are mostly task specific and lack a user-friendly interface to operate. We present Meta-EyeFM, a multi-function foundation model that integrates a larg…
Benchmarking Next-Generation Reasoning-Focused Large Language Models in Ophthalmology: A Head-to-Head Evaluation on 5,888 Items
Minjie Zou, Sahana Srinivasan, Thaddaeus Wai Soon Lo +13
Recent advances in reasoning-focused large language models (LLMs) mark a shift from general LLMs toward models designed for complex decision-making, a crucial aspect in medicine. H…
Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers
Meng Wang, Tian Lin, Qingshan Hou +69
Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings,…
Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?
Qingshan Hou, Yukun Zhou, Jocelyn Hui Lin Goh +19
The advent of foundation models (FMs) is transforming medical domain. In ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1.4 million natural images and 1.…
Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection?
Samantha Min Er Yew, Xiaofeng Lei, Jocelyn Hui Lin Goh +26
Background: RETFound, a self-supervised, retina-specific foundation model (FM), showed potential in downstream applications. However, its comparative performance with traditional d…
Can OpenAI o1 Reason Well in Ophthalmology? A 6,990-Question Head-to-Head Evaluation Study
Sahana Srinivasan, Xuguang Ai, Minjie Zou +14
Question: What is the performance and reasoning ability of OpenAI o1 compared to other large language models in addressing ophthalmology-specific questions? Findings: This study ev…
Enhancing Community Vision Screening -- AI Driven Retinal Photography for Early Disease Detection and Patient Trust
Xiaofeng Lei, Yih-Chung Tham, Jocelyn Hui Lin Goh +7
Community vision screening plays a crucial role in identifying individuals with vision loss and preventing avoidable blindness, particularly in rural communities where access to ey…
LEME: Open Large Language Models for Ophthalmology with Advanced Reasoning and Clinical Validation
Hyunjae Kim, Xuguang Ai, Sahana Srinivasan +27
The rising prevalence of eye diseases poses a growing public health burden. Large language models (LLMs) offer a promising path to reduce documentation workload and support clinica…