arXiv AI June 2, 2026 By Zahra Tabatabaei, Jon Sporring, Mark Bremholm Elleb{\ae}k, Alaa El-Hussuna

Predicting the risk of colorectal anastomotic leak based on preoperative mapping of the blood supply of the bowel

arXiv:2606. 02156v1 Announce Type: cross Abstract: Anastomotic leak remains one of the most serious complications following colorectal cancer surgery, substantially affecting patient outcomes, recovery trajectories, and healthcare costs.

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 Machine Learning

Aug 3

What Is Missing in Surgical Risk Stratification and Outcome Prediction: A Scoping Review of End-to-End Machine Learning Approaches

arXiv:2607. 29090v1 Announce Type: new Abstract: Postoperative adverse events, including mortality and morbidity, remain a major global burden, many of which are preventable through early identification of high-risk patients and targeted perioperative care.

By Yizhi Dong, Yuhe Ke, Hairil Rizal Abdullah, Yucheng Xing, Kevan Kai Bing Teo, Ling Huang, Mengling Feng

multimodal benchmarks

arXiv AI

Jul 1

AI for Quality Assurance in the Operating Room

arXiv:2606. 30657v1 Announce Type: cross Abstract: Surgical outcomes depend not only on patient factors and postoperative care but are also strongly influenced by the quality of the operation itself.

By Pietro Mascagni, Lalith Sharan, Deepak Alapatt, Nicolas Padoy

safety

arXiv Computer Vision

Aug 21

Artificial Intelligence for Workflow Analysis in Colorectal Surgery: A Multicentric, Cross-Procedural Development and Generalization Study

arXiv:2608. 20154v1 Announce Type: new Abstract: Minimally invasive colorectal surgeries (MIS-CRS) are characterised by significant variability and inconsistent outcomes.

By Pietro Mascagni, Julia Alekseenko, Pooja P Jain, Marta Goglia, Andrea Balla, Ludovica Baldari, Gianfranco Silecchia, Claudio Fiorillo, Vincenzo Tondolo, Salvador Morales-Conde, Luigi Boni, Sergio Alfieri, Nicolas Padoy

llms computer-vision fine-tuning

arXiv Computer Vision

Aug 28

Surgical Video Generation From Diffusion to World Models: A Survey

This survey reviews recent advances in surgical video generation, categorizing methods into unconditional, conditional, and world modeling generation. It highlights a shift from creating visually plausible frames to modeling the causal dynamics of surgical scenes, and discusses challenges such as pixel-level fidelity versus clinical plausibility, generalization, physical realism, controllability, and interpretability. The paper also compiles experimental results from public datasets to serve as a quantitative benchmark for the field.

By Fuxiang Huang, Chenxu Zhang, Liang Han, Lei Zhang

diffusion robotics multimodal safety

arXiv AI

Jul 1

Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines

arXiv:2605. 23995v4 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) is increasingly used in medical image analysis to reduce dependence on costly expert annotations by learning transferable representations from unlabeled data.

By Chathura Wimalasiri, Kishor Nandakishor, Marimuthu Palaniswami

computer-vision

arXiv Machine Learning

Jun 16

Navigating Distribution Shifts in Medical Image Analysis: A Survey

arXiv:2411. 05824v4 Announce Type: replace-cross Abstract: Medical Image Analysis (MedIA) has become indispensable in modern healthcare, enhancing clinical diagnostics and personalized treatment.

By Zixian Su, Jingwei Guo, Xi Yang, Qiufeng Wang, Frans Coenen, Amir Hussain, Kaizhu Huang

fine-tuning safety