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Evaluating reasoning in multimodal large language models for ophthalmology: a bilingual benchmark study using clinical vignettes and imaging.

BACKGROUND: Large language models (LLMs) excel in text-based medical exams, but their ability to integrate multimodal data, critical for ophthalmology, is underexplored. This study evaluates vision-language LLMs' accuracy and reasoning in complex ophthalmic questions. METHODS: We assessed three multimodal LLMs (CLM-V, ChatGPT-5, MiniCPM-V 4.5) on 316 bilingual ophthalmology questions (175 English ...

Development of a machine learning model using systemic and ophthalmic parameters to detect sleep-disordered breathing in glaucoma patients.

PURPOSE: To develop and validate a machine-learning model using systemic and ophthalmic parameters that predicts sleep-disordered breathing (SDB) in patients with open-angle glaucoma (OAG). STUDY DESIGN: Retrospective cross-sectional study METHODS: We analyzed 513 patients with OAG (955 eyes) treated at Seiryo Eye Clinic. All the participants underwent comprehensive ophthalmic examinations, includ...

Whole anterior visual pathway segmentation from high-resolution MRI using artificial intelligence.

OBJECTIVE: Manual segmentation of the whole anterior visual pathway (aVP) from high-resolution magnetic resonance imaging (MRI) is time-consuming and ...

Performance Evaluation of GPT-5, Grok 4, and DeepSeek R1 in Interpreting Complete Blood Count Reports for Hematologic Diseases: Retrospective Comparative Study.

BACKGROUND: Large language models (LLMs) demonstrate potential in the laboratory, yet rigorous clinical evaluation remains limited. The opacity of LLM...

Reply to Kristoffer Mazanti Cold, Lars Konge, and Sarah Hjartbro Bube's Letter to the Editor re. Roessler N, Miszczyk M, Miyajima K, et al. Harnessing Artificial Intelligence for Risk Stratification and Outcome Prediction in Urologic Cancers: A Systematic Review. Eur Urol Focus. In press. http://dx.doi.org/10.1016/j.euf.2025.12.007.

Large Language Model Authorship in Ophthalmic Publications.

PURPOSE: To assess for the likely presence of artificial intelligence (AI)-generated text in the published ophthalmology literature. METHODS: Abstract...

Dnq-unet: a two-level fusion framework for few-shot domain adaptation in cervical cancer CTV segmentation.

To develop and validate a two-level hierarchical fusion architecture enabling efficient few-shot domain adaptation for cervical cancer clinical target...

Limits of Artificial Intelligence Models for Skin Cancer Diagnosis in Realistic Settings.

IMPORTANCE: Artificial intelligence (AI) systems for skin cancer detection perform well in controlled settings but frequently underperform in everyday...

Multimodal Ultrasound Integration Pathways and Paradigm Innovations in Precision Diagnosis and Treatment of Thyroid Cancer.

RATIONALE AND OBJECTIVES: Thyroid cancer, the fastest-growing endocrine malignancy, is shifting from morphological evaluation to molecular-functional ...

Artificial intelligence in urology training Enhancing annotation, feedback, and evaluation in robotic, laparoscopic, and endoscopic surgery.

INTRODUCTION: The integration of artificial intelligence (AI) into surgical training is rapidly evolving, driven by advancements in machine learning. ...

Toward Autonomous Histotripsy: Integrating Deep Learning Segmentation With Robotic Control for Glioblastoma.

OBJECTIVE: Glioblastoma multiforme (GBM) is an aggressive brain tumor in which incomplete margin delineation during surgery can contribute to residual...

Skill Information Representation Imitation Learning for Long-Horizon Dexterous Robot Micromanipulation of Deformable Cell.

Robots performing collaborative long-horizon dexterity cell micromanipulation tasks are challenging and practically significant, such as peeling cell ...

CEUS-Based Microvascular Invasion Predictor in HCC: Improving Prognostic Stratification Following Thermal Ablation.

BACKGROUND & AIMS: Microvascular invasion (MVI) critically impacts hepatocellular carcinoma (HCC) management. We aimed to develop and validate a deep ...

Large Language Models for Ophthalmology Training in China: A Prospective Evaluation.

PURPOSE: This study explored large language models (LLMs) as a scalable solution to the global shortage and uneven distribution of ophthalmologists, p...

[Artificial intelligence in diabetic retinopathy screening].

INTRODUCTION: Visual impairment and blindness continue to represent a substantial disease burden in Hungary. According to national epidemiological dat...

Techniques and outcomes review of the endoscopic management of gastrointestinal bleeding.

INTRODUCTION: Gastrointestinal (GI) bleeding is a frequent and potentially life-threatening emergency that imposes a substantial healthcare burden wor...

MRI-based deep learning radiomics model for automated classification of disc degeneration in the lumbar spine.

Disc degeneration in the lumbar spine is a major cause of low back pain (LBP). The accurate grading of disc degeneration on magnetic resonance imaging...

CAHA-Net: A novel MR image classification model based on DenseNet incorporating coordinate attention and hybrid augmentation.

Traditional diagnostic approaches are time-consuming and labor-intensive, and the field currently lacks a comprehensive evaluation of mainstream model...

Artificial Intelligence-Powered Cystoscopy Diagnostic Support System: Clinical Application of Multiarchitecture Deep Learning Models.

PURPOSE: Cystoscopy is a crucial diagnostic tool in urology for the detection and evaluation of bladder lesions. However, its diagnostic accuracy (ACC...

Rapid Access Macular Screening and Evaluation: A Datasheet for a Dataset to Diagnose and Triage Macular Diseases.

PURPOSE: Macular disease can cause significant visual morbidity. Timely and accurate diagnosis and management is paramount. However, there is a lack o...