2 citations · 3 across the 3 of their papers we have counts for
5 papers
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…