14 citations · 20 across the 8 of their papers we have counts for
10 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…
Vibe coding for clinicians: democratising bespoke software development for digital health innovation
Ariel Yuhan Ong, Iain Livingstone, Caroline Kilduff +5
Clinicians often face workflow problems that are perceived as either too bespoke or low stakes to attract commercial attention. Historically, most do not have the technical knowled…
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…
Complementary Human-AI Clinical Reasoning in Ophthalmology
Mertcan Sevgi, Fares Antaki, Abdullah Zafar Khan +26
Vision impairment and blindness are a major global health challenge where gaps in the ophthalmology workforce limit access to specialist care. We evaluate AMIE, a medically fine-tu…
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…
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…
Large language models perpetuate bias in palliative care: development and analysis of the Palliative Care Adversarial Dataset (PCAD)
Naomi Akhras, Fares Antaki, Fannie Mottet +4
Bias and inequity in palliative care disproportionately affect marginalised groups. Large language models (LLMs), such as GPT-4o, hold potential to enhance care but risk perpetuati…
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…
RetinaVR: Democratizing Vitreoretinal Surgery Training with a Portable and Affordable Virtual Reality Simulator in the Metaverse
Fares Antaki, Cédryk Doucet, Daniel Milad +3
We developed and validated RetinaVR, an affordable and immersive virtual reality simulator for vitreoretinal surgery training, using the Meta Quest 2 VR headset. We focused on four…
Predicting Visual Improvement after Macular Hole Surgery: a Cautionary Tale on Deep Learning with Very Limited Data
M. Godbout, A. Lachance, F. Antaki +2
We investigate the potential of machine learning models for the prediction of visual improvement after macular hole surgery from preoperative data (retinal images and clinical feat…