3 citations · 6 across the 5 of their papers we have counts for
5 papers
An Agentic AI Framework Overcomes Fundamental Limitations of Large Language Models for Glaucoma Detection from Fundus Photography
Jalil Jalili, Hossein Taghizad, Anuwat Jiravarnsirikul +8
Large language models (LLMs) show promise in medical image interpretation but suffer from hallucination, limited accuracy, and run-to-run inconsistency. We developed and validated…
Glaucoma Detection and Structured OCT Report Generation via a Fine-tuned Multimodal Large Language Model
Jalil Jalili, Yashraj Gavhane, Evan Walker +11
Objective: To develop an explainable multimodal large language model (MM-LLM) that (1) screens optic nerve head (ONH) OCT circle scans for quality and (2) generates structured clin…
Open Data Sharing in Clinical Research and Participants Privacy: Challenges and Opportunities in the Era of Artificial Intelligence
Shahin Hallaj, Anna Heinke, Fritz Gerald P. Kalaw +16
Sharing clinical research data is key for increasing the pace of medical discoveries that improve human health. However, concern about study participants' privacy, confidentiality,…
ODFormer: Semantic Fundus Image Segmentation Using Transformer for Optic Nerve Head Detection
Jiayi Wang, Yi-An Mao, Xiaoyu Ma +8
Optic nerve head (ONH) detection has been a crucial area of study in ophthalmology for years. However, the significant discrepancy between fundus image datasets, each generated usi…
One-Vote Veto: Semi-Supervised Learning for Low-Shot Glaucoma Diagnosis
Rui Fan, Christopher Bowd, Nicole Brye +4
Convolutional neural networks (CNNs) are a promising technique for automated glaucoma diagnosis from images of the fundus, and these images are routinely acquired as part of an oph…