arXiv AI By Sixu Chen, Mingrui Yang, Qiang Guan, Xiaojuan Li

OA-MAP: Evidence-Grounded Multi-Agent Multimodal Framework for Interpretable Knee Osteoarthritis Progression

Read the original on arXiv AI →

OA-MAP is an autonomous multi‑agent multimodal framework designed to predict knee osteoarthritis progression by integrating MRI, X‑ray, and clinical data. The system employs modality‑specific agents and a coordinator to autonomously select predictive tools, retrieve supporting literature, and provide interpretable risk estimates. In a study of 600 participants, the fusion models achieved AUROCs of 0.80 for structural progression and 0.68 for pain progression, and a case study demonstrated how OA‑MAP combines risk estimates, intermediate findings, literature support, and uncertainty indicators for interactive clinician review.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jun 6

An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)

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).

By Jincheng Yu, Haoyang Li, Yiwen Liu, Shen Liu, Rachel Yuanbao Chen, C. Kent Kwoh, Hongxu Ding, Xiaoxiao Sun
arXiv AI
Oct 7

Knee3DVLM: Dual-Sequence Full-Volume Vision-Language Modeling for Comprehensive Knee MRI Assessment

arXiv:2610.08482v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly being applied to three-dimensional medical imaging, but their application to knee MRI remains limited,...

By Maryam Baizhigitova, Andrew Seohwan Yu, Po-Hao Chen, Naveen Subhas, Sixu Chen, Xinxin Wang, Kunio Nakamura, Richard Lartey, Xiaojuan Li, Mingrui Yang
arXiv AI
Jun 17

Probing, Fusion, and Trustworthiness: A Systematic Evaluation of Foundation Model Representations for Multimodal Cancer Analysis

arXiv:2606. 17115v1 Announce Type: cross Abstract: Foundation models (FMs) have emerged as powerful representation extractors for medical data, yet their generalizability to datasets under distribution shift remains underexplored.

By Jingyu Hu, Giuseppe Tripodi, Reed Naidoo, Sarah F. McGough, Tapabrata Chakraborti