arXiv:2609.31461v1 Announce Type: new Abstract: Background: Large volumes of unlabeled knee MRI scans are available across repositories but remain insufficiently leveraged. We developed KneePreM, a k...
arXiv:2604. 23435v2 Announce Type: replace-cross Abstract: Grading knee osteoarthritis (KOA) on plain radiographs is poorly reproducible across readers.
arXiv:2606. 05357v1 Announce Type: new Abstract: Purpose: To develop an interpretable and trustworthy AI framework that combines deep learning based MRI Osteoarthritis Knee Score (MOAKS) prediction with interpretable statistical modeling to study structure-pain relationships at scale using data from the Osteoarthritis Initiative (OAI).
arXiv:2609.10125v1 Announce Type: new Abstract: Trochlear dysplasia (TD) is an abnormality of the femoral trochlea associated with anterior knee pain and patellar instability. The sulcus angle (SA) i...
arXiv:2606. 08364v1 Announce Type: cross Abstract: Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative condition whose osseous changes are often subtle on cone-beam CT (CBCT), making automated detection challenging.
arXiv:2607. 20028v1 Announce Type: cross Abstract: Robust out-of-the-box performance is essential for the clinical deployment of deep learning models in medical imaging.
The article reviews how multimodal large language models (MLLMs) are expanding radiology AI beyond image‑specific tasks to multimodal reasoning, yet volumetric radiology poses a representational challenge because clinical interpretation needs full 3‑D spatial context and quantitative data. It surveys over 200 studies, categorizing advances in volumetric representation, multimodal understanding, and agentic orchestration, and introduces a Claim‑Design‑Validation framework to align technical, workflow, and clinical claims. The review emphasizes that native volumetric modeling and agentic capabilities must match spatial, quantitative, contextual, and workflow demands, and that clinical credibility hinges on faithful 3‑D representation, traceable behavior, proper validation, and defined human oversight.
arXiv:2605.30984v2 Announce Type: replace-cross Abstract: Modern 3D medical vision-language models (VLMs) can generate fluent radiology-style text while exhibit critically low pathology detection and...
Automated report generation can ease the burden radiolo gists face when interpreting multi-sequence MRI studies. Unlike CT, MRI examinations comprise multiple sequences and imaging planes, each con tr...
arXiv:2608. 12689v1 Announce Type: cross Abstract: Multi-parametric magnetic resonance imaging (mpMRI) is a cornerstone for brain tumor diagnosis and treatment, yet current AI models face critical limitations: their lack of natural language interaction and interpretability impedes spatial information integration and cross-modal reasoning required clinically.
The study introduces a multitask conditional generative adversarial network (MT‑cGAN) that simultaneously synthesizes high‑resolution DESS‑like images and segments knee cartilage and menisci directly from quantitative MRI echo images. Evaluated on 508 knee MRIs from 361 subjects, MT‑cGAN achieved a mean Dice score of 0.84 for segmentation and the lowest coefficient of variation for T1ρ (1.84%) and T2 (1.81%) quantification, outperforming existing conditional GAN approaches. By eliminating the need for separate high‑resolution morphological scans, the method shortens scan times and supports clinical adoption of quantitative MRI.
arXiv:2603.28387v3 Announce Type: replace-cross Abstract: Trustworthy clinical AI must use real evidence and avoid relying on surface-level artifacts. We evaluate 12 open-weight vision-language model...