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:2606. 23705v1 Announce Type: cross Abstract: Pain is assessed differently by patients, nurses, and clinicians, yet most computational approaches assume a single ground-truth label - effectively ignoring who is doing the rating.
By Saba A. Farahani, Elahe Khatibi, Thomas D. Hughes, Ariana M. Nelson, Hung Cao, Amir M. Rahmani
The paper introduces the Relational Hypergraph Transformer (RHT), a unified architecture that models relational databases as hypergraphs and learns pentadimensional embeddings (PentE). RHT applies sparse relational attention whose complexity scales with the average relational degree, making it computationally efficient for large, high‑dimensional, and high‑cardinality datasets. Experiments on the Synthea synthetic electronic health record dataset show that RHT produces more semantically coherent embeddings than tabular, relational, and temporal graph baselines, while remaining scalable, and the authors provide an open‑source implementation and plan clinical validation on MIMIC‑IV.
By Edouard Lansiaux, Hugo Kazzi, Aur\'elien Loison, Slim Hammadi, Emmanuel Chazard
arXiv:2606. 08311v1 Announce Type: new Abstract: Electronic health record (EHR) notes are dense medical documents containing large amounts of information, often filled with complex medical jargon.
By Mahshad Koohi Habibi Dehkordi, Shuxin Zhou, Yehoshua Perl, Fadi P. Deek, James Geller, Gai Elhanan, Andrew J. Einstein, Luke Lindemann, Vipina K. Keloth
arXiv:2608. 02692v1 Announce Type: new Abstract: Access to holistic, multimodal data improves the performance of Artificial Intelligence (AI) in medical classification tasks compared to utilizing single modalities or data sources.
By Julia Gehrmann, Lars Quakulinski, Hamza Naseem, Oya Beyan
arXiv:2608.28608v1 Announce Type: cross
Abstract: Ancient Indian medical texts like Sushruta Samhita have extensive information on diseases, treatments, and surgical techniques. Yet, their ancient fo...
By M. S. Rajeevan, B. Mini Devi, V. S. Anoop, C. Mallikarjuna