Neurosymbolic Alignment couples a 7B clinical language model with a graph‑based physiological world model to score candidate responses using homeostatic constraints, multi‑hop plausibility, and drug‑interaction penalties. This training‑time framework drives iterative on‑policy updates and achieves a 90.8% Clinical Safety Score on the CSB benchmark, outperforming ORPO, GPT‑4 (5‑shot), and a self‑correction pipeline. Ablation studies show that the HGNN scoring and iterative training are the key contributors to the safety gains.
By Abdulhady Abas Abdullah, Erik Cambria, Milena Zivkovic
arXiv:2606. 15250v1 Announce Type: cross Abstract: Radiographic assessment of lower-limb alignment (LLA) is important for predicting joint health and surgical outcomes in total knee arthroplasty.
By Zhisen Hu, Antti Kemppainen, David Johnson, Egor Panfilov, Huy Hoang Nguyen, Timothy Cootes, Claudia Lindner, Aleksei Tiulpin
arXiv:2608.29336v1 Announce Type: new
Abstract: FISICA is a body-assessment and recommendation service running in production. One standing session with two photographs returns foot-loading measures,...
By Juhwan Song, Heejung Kim, Juntae Noh, Jonghak Ryu, Huiju Park, Junseong Lee, Dohyeon Ahn, Byungwoo Jo
arXiv:2601. 02088v3 Announce Type: replace Abstract: Orthognathic surgery repositions jaw bones to restore occlusion and enhance facial aesthetics.
By Jiahao Bao, Huazhen Liu, Yu Zhuang, Leran Tao, Xinyu Xu, Yongtao Shi, Mengjia Cheng, Yiming Wang, Congshuang Ku, Ting Zeng, Yilang Du, Siyi Chen, Shunyao Shen, Suncheng Xiang, Hongbo Yu
This scoping review examined 117 studies on video-based markerless motion capture, most published from 2024 onward and focused on healthy adults walking in laboratories. The studies identified five main pipeline architectures, but most reported only raw joint angles without biomechanical refinement, achieving sagittal lower‑limb agreement of about 5–6°, which falls short of clinical acceptability. Validation of out‑of‑plane kinematics, kinetics, and performance in older or pathological populations was rare, and emerging computer‑vision techniques such as foundation‑model mesh recovery and differentiable inverse kinematics were largely absent from validated work.
By Florian Delaplace (LAMHESS, CHU), Elodie Piche (LAMHESS), Fr\'ed\'eric Chorin (IUF, LAMHESS), Raphael Zory (IUF, LAMHESS)
arXiv:2608. 03927v1 Announce Type: new Abstract: Engineered Skeletal Muscle Tissues (ESMs) have become a key structure for biomedical disease modeling and pharmacological screening, yet their functional characterization often relies on simplistic metrics like peak force, discarding critical kinetic information.
By Mattias Luber, Timo Betz
arXiv:2606. 01374v3 Announce Type: replace Abstract: Observable performance is commonly used to characterize biological systems, yet aggregated outputs may remain insufficient for uniquely resolving observational conditions, and richer multivariate representations may retain substantial ambiguity.
By Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit
arXiv:2609.23826v1 Announce Type: new
Abstract: Mitral regurgitation is the most common heart valve disorder worldwide, affecting over 2% of the global population, rising to at least 10% in adults ov...
By Shawn Koohy, Wensi Wu, Matthew A Jolley, Paris Perdikaris
The paper introduces Anatomy-Informed Neural Networks (AINN), which embed soft and hard anatomical priors into the loss function and network architecture to prevent anatomically impossible predictions. AINN is applied to a clinical scenario of aortoiliac deformation caused by a guidewire, modeling vessel and wire dynamics in SE(3) and training with a Wasserstein-2 loss from 2D angiograms. The study verifies the kinematics and loss against ground truth but does not yet train a network, outlining future work to apply the model to real CT data.
By David P. Stonko
arXiv:2608.29928v1 Announce Type: new
Abstract: State-of-the-art monocular body recovery methods predict mesh vertices and angles on the corresponding kinematic tree, but their outputs lack biomechan...
By R. James Cotton, J. D. Peiffer, Lucinda Williamson, John Leske, Georgios Pavlakos
arXiv:2607. 29243v1 Announce Type: cross Abstract: Computed tomography angiography (CTA) is crucial for preprocedural TAVI planning, providing the anatomical information required for prosthesis sizing and vascular access assessment.
By Alessandra Zerillo, Stefano Cannata, Diego Bellavia, Daniele Ciriello, Simone Manini, Salvatore Pasta, Caterina Gandolfo
The paper presents a method for accurately simulating soft tissue deformation and predicting forces across varying material stiffnesses and geometries. It calibrates hyperelastic constitutive models in the SOFA Framework using gravity‑loaded silicone beams, then trains a softness‑conditioned equivariant graph neural network on the calibrated simulations. The resulting model achieves sub‑millimeter deformation accuracy with 0.010 s inference time, and demonstrates that force prediction quality depends on consistent upstream calibration.
By Madina Kojanazarova, Sidaty El Hadramy, Philippe C. Cattin