Medical damage liability risk of medical AI: from the perspective of DeepSeek's large-scale deployment in Chinese hospitals. [PDF]
Wang Y, Zhou Z.
europepmc +1 more source
Threshold‐optimized machine learning models using routine clinical and laboratory data in 623 adults undergoing appendectomy. Logistic regression (AUC = 0.765) and random forest (AUC = 0.785) were the best‐performing models for appendicitis detection and complicated appendicitis prediction, respectively.
Ivan Males +8 more
wiley +1 more source
FTIR Spectroscopy of Vitreous Humor for Postmortem Interval Estimation: A Multivariate Regression Approach. [PDF]
Țurlea IR +5 more
europepmc +1 more source
This meta‐analysis of 208 cases shows that salvage esophagectomy for cT4 esophageal squamous cell carcinoma achieves a 72% R0 resection rate, offering a curative pathway for selected patients. However, it remains a high‐risk procedure with an 18% anastomotic leak rate and 30% major complications (Clavien–Dindo ≥ III).
Makoto Sakai +4 more
wiley +1 more source
Multi-epigenome-wide analyses and meta-analysis of child maltreatment in judicial autopsies and intervened children and adolescents. [PDF]
Nishitani S +20 more
europepmc +1 more source
A Survey of the Qualifications of Magistrates Authorized to Issue Warrants [PDF]
core +1 more source
Innovations in Gastric Cancer Surgery During Early Minimally Invasive Era and Future Perspectives
With continuing revelations in tumor biology and the emergence of artificial intelligence, new horizons for surgical innovation are opening. At the center of this transformative journey stands the innovative surgeon, driven by passion, guided by data, and steadfast in the commitment to patient safety and quality of life.
Reut El‐On, Young‐Woo Kim
wiley +1 more source
Does AI help humans make better decisions? A statistical evaluation framework for experimental and observational studies. [PDF]
Ben-Michael E +5 more
europepmc +1 more source
I Know the Child Is My Client, but Who Am I? [PDF]
England, Sharon S. +1 more
core +1 more source
AI in chemical engineering: From promise to practice
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source

