This review evaluates the generalizability of AI pathology models in predicting EGFR mutations, highlighting significant performance variations across genetic ancestries and specimen types, and their role in clinical triage.
This multi-institutional study validates Neuropath-AI, a deep-learning system that predicts CNS tumor types by inferring molecular features from histopathology slides. Achieving 80% top-1 accuracy in high-confidence samples, the model offer
A single-center study demonstrates that an AI pipeline for CT-based lymph node segmentation and extranodal extension classification significantly improves prognostic accuracy for HPV-positive oropharyngeal cancer compared to traditional rad
A longitudinal study published in NEJM AI evaluates seven generative AI chatbots for alcohol misuse support, finding that while they excel in empathy and non-judgmental language, they frequently provide low-quality or inaccurate medical inf
Researchers developed EEGSurvNet, a deep survival model that analyzes routine EEG to predict seizure risk over two years. Outperforming clinical models, it achieved an AUROC of 0.80 at two months and showed high efficacy in patients without
This independent validation study confirms that AI-enhanced ECG models effectively detect left ventricular systolic dysfunction across diverse populations, achieving AUROCs up to 0.93. However, significant barriers remain regarding model tr
A randomized multimethod study reveals that while AI clinical decision support systems for antibiotic switching are well-received, their primary influence is reinforcing conservative prescribing. The study underscores that usability and cli
A randomized controlled trial demonstrates that an mHealth management model using the Better Pregnancy app significantly reduces GDM incidence, improves glycemic control, and enhances maternal self-efficacy among high-risk pregnant women, p
A validated multimodal AI model using digital pathology and clinical data significantly improves risk stratification for prostate cancer patients with biochemical recurrence, identifying those who derive the greatest benefit from salvage ho
A deep learning analysis of the PanaMa trial reveals that the benefit of adding panitumumab to maintenance therapy for RAS wild-type mCRC is significantly higher in patients with a high muscle-bone ratio, suggesting a new AI-driven approach
A deep learning-derived sarcopenia marker, the muscle-bone ratio (MBR), has been shown to predict the efficacy of anti-EGFR maintenance therapy in patients with RAS wild-type metastatic colorectal cancer, potentially identifying those who t
The PECTUS-AI study demonstrates that AI-based identification of thin-cap fibroatheromas using OCT provides superior prognostic value for cardiovascular events compared to manual analysis, particularly when assessing the entire imaged coron
The novel PILOT machine learning architecture integrates time-phased perioperative data and regeneration-associated biomarkers to predict post-hepatectomy liver failure within six hours of surgery, significantly outperforming traditional cl
A multicenter study demonstrates that deep transfer learning (DTL) algorithms using preimplant MRI can predict spoken language development in children with cochlear implants with over 92% accuracy, significantly outperforming traditional ma
A secondary analysis of the TEAM trial using machine learning demonstrates that enhanced early mobilization in mechanically ventilated patients has highly individualized effects, ranging from a 34% mortality reduction to a 39% increase in r
AI-driven analysis of routine chest CTs reveals that coronary artery calcium is highly prevalent and strongly predictive of MACE and mortality in patients with IMIDs, identifying a critical treatment gap in this high-risk population.
An mHealth text messaging intervention significantly improves smoking cessation rates and reduces mortality among TB patients compared to usual care, supporting its implementation in TB programs.
A comprehensive meta-analysis indicates that rule-based chatbots provide a modest but statistically significant reduction in depressive symptoms within a 4-8 week window, whereas evidence for the clinical efficacy of Large Language Model (L
This article explores a landmark mixed-methods study establishing a digital biomarker ontology for the early detection of Cytokine Release Syndrome (CRS). By identifying core physiological markers, researchers aim to transform immunotherapy
Recent robust randomized trials demonstrate that smartphone-delivered digital cognitive behavioral therapy provides significant, sustained improvements in generalized anxiety disorder, overcoming key access barriers and highlighting its pot
Open language-specific specialty feeds and department pages.