This study aims to evaluate the association of optic cup-to-disc ratio (CDR) with magnification-adjusted optical coherence tomography (OCT) peripapillary retinal nerve fibre layer (RNFL) and macula ganglion cell complex (GCC) thickness in Asian myopic children, to help clinicians better evaluate glaucoma risk in myopic children with increased CDR. In a prospective clinical cohort study, myopic children aged 7–16 years were assessed annually over 2 years, excluding children with overt glaucomatous changes. Right eye data including visual acuity, intraocular pressure, cycloplegic autorefraction, axial length, fundus photographs and magnification-adjusted OCT of the optic disc and macula were collected. Children were divided into three OCT-assessed vertical CDR (vCDR) subgroups (0.00-0.39, 0.40–0.59, and 0.60–0.90) for analyses. Among 901 eyes, 162 (18.2
Extended reality (XR), encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies offer immersive and interactive experiences that are poised to transform clinical neurology. This review describes the technological foundations of XR systems, from multi-sensor headsets and real-time rendering pipelines to networked platforms, which together support emerging applications. In clinical practice, XR enables more sensitive and ecologically valid assessments of cognitive, motor, and sensory function; neurosurgical training and intraoperative XR guidance improve spatial understanding and decision support; and immersive rehabilitation programs for stroke, chronic neurological disorders, and pediatric conditions enhance engagement and functional recovery. Finally, we summarize adoption challenges and required supports for the clinical implementation of XR and highlight future directions. With continued interdisciplinary innovation and careful evaluation, XR is positioned to reshape neurology by providing powerful tools for understanding the brain and treating neurological disorders.
Developments in large language models (LLMs) in the past 2 years have shifted the focus from text, image, and audio generation to LLMs capable of multistep reasoning (thinking). The development of LLMs is particularly important for medicine and health care, but the translation of these models has been limited by the black-box nature of previous LLMs. New reasoning-driven LLMs incorporate chain-of-thought prompting and reveal intermediate reasoning steps, offering transparency and traceability, potentially improving the clinical adoption and utility of LLMs. In this Viewpoint, we examine four emerging reasoning-driven LLMs, namely OpenAI's o1 and o3-mini, Google's Gemini 2.0 Flash Thinking, and DeepSeek R1. We compare their methodological approaches, benchmark their performance on medical question-answering tasks, and assess their potential for clinical integration. We highlight both opportunities and challenges associated with deploying reasoning-driven LLMs. Key future considerations include real-world validation, rigorous benchmarking with ethical safeguards, and advancements in improving the efficiency and sustainability of reasoning-driven LLMs. Addressing these challenges will enable the fine-tuning of these LLMs for specific medical applications, enhancing their potential clinical decision support, patient education, medical training, and evidence synthesis.
Medical Large Language Models (MLLMs) play a crucial role in ophthalmic diagnosis, holding significant potential to address vision-threatening diseases. However, their accuracy is constrained by hallucinations stemming from limited ophthalmic knowledge, insufficient visual localization and reasoning capabilities, and a scarcity of multimodal ophthalmic data, which collectively impede precise lesion detection and disease diagnosis. Furthermore, existing medical benchmarks fail to effectively evaluate various types of hallucinations or provide actionable solutions to mitigate them. To address the above challenges, we introduce EH-Benchmark, a novel ophthalmology benchmark designed to evaluate hallucinations in MLLMs. We categorize MLLMs' hallucinations based on specific tasks and error types into two primary classes: Visual Understanding and Logical Composition, each comprising multiple subclasses. Given that MLLMs predominantly rely on language-based reasoning rather than visual processing, we propose an agent-centric, three-phase framework, including the Knowledge-Level Retrieval stage, the Task-Level Case Studies stage, and the Result-Level Validation stage. Experimental results show that our multi-agent framework significantly mitigates both types of hallucinations, enhancing accuracy, interpretability, and reliability. Our project is available at https://github.com/ppxy1/EH-Benchmark.
Glaucoma is the leading cause of irreversible blindness worldwide with heterogeneous progression rates. Artificial Intelligence (AI) may enable accurate progression predictions in clinical practice. We conducted a systematic review to survey quantitative AI performance and examine strengths and shortfalls in current AI approaches with future clinical implementation in mind. Two reviewers independently screened studies in English from MEDLINE, Embase, Web of Science, Cochrane CENTRAL and arXiv since 2014 and performed risk of bias assessment on eligible studies using QUADAS-2. 46 reports of 43 unique studies demonstrated moderate to good performance in predicting glaucoma conversion, biological deterioration and progression to surgery. Several challenges for clinical translation remain, including inconsistent reporting, limitations and heterogeneity in study design and poor AI generalisability and transparency. We encourage future studies to adopt robust study design and transparent reporting and propose the first glaucoma-specific list of recommended practices and reporting items for future clinical implementation.
