ReviewNPJ digital medicine2025
Multimodal generative AI for interpreting 3D medical images and videos.
Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- A Bilingual Benchmark for Evaluating Diagnostic Performance of Multimodal Large Language Models in Radiology (RadM-Bench): Evaluation Development and Validation.Journal of medical Internet research · 2026Article
- Prompt mechanisms in medical imaging: A comprehensive survey.Innovation (Cambridge (Mass.)) · 2026Review
- Benchmarking Multimodal Vision Frontier Models With Lumbar Spine MRIs for Grading Lumbar Spinal Stenosis.Global spine journal · 2026Article
- Diagnostic accuracy of multimodal large language models in differentiating epileptic from functional seizures in smartphone recorded videos.Scientific reports · 2026Article
- HoloTrauma 3X Triadic AI Co reasoning for robot assisted emergency maxillofacial reconstruction.NPJ digital medicine · 2026Article
- Vision-Enabled AI scribes reduce omissions in clinical conversations: evidence from simulated medication histories.NPJ digital medicine · 2026Article
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Multimodal machine learning for staging laparoscopy: a combined image analysis and morphologic tool for the discrimination of peritoneal metastasis.International journal of surgery (London, England) · 2026Article
- Diagnostic Accuracy of GPT-4 With Vision in Neuroradiology Board-Style Exam Questions: Cross-Sectional Case-Based Study.JMIR neurotechnology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
This perspective proposes adapting video-text generative AI to 3D medical imaging (CT/MRI) and medical videos (endoscopy/laparoscopy) by treating 3D images as videos. The approach leverages modern video models to analyze multiple sequences simultaneously and provide real-time AI assistance during procedures. The paper examines medical imaging's unique characteristics (synergistic information, metadata, and world model), outlines applications in automated reporting, case retrieval, and education, and addresses challenges of limited datasets, benchmarks, and specialized training.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.