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A novel artificial intelligence algorithm enables continuous, noninvasive monitoring of inspiratory muscle effort and patient-ventilator dyssynchrony during mechanical ventilation, demonstrating accuracy comparable to invasive gold standard
AI-enabled point-of-care EEG monitoring accurately measures seizure burden, showing a dose-dependent association with unfavorable clinical outcomes at discharge, highlighting its potential in critical care.
A study comparing physician-based surprise question, AI-driven GEST model, and a combined approach shows improved accuracy and efficiency in predicting 6-month mortality among older ED patients.
This article explores a novel AI framework that identifies distinct collagen deposition patterns in MASLD, offering improved molecular specificity and prognostic value beyond traditional fibrosis assessments.
A historically grounded review of how physicians, librarians, publishers, and now algorithms have filtered the expanding medical literature, shaping what counts as visible, credible, and clinically actionable knowledge.
A fine-tuned ensemble of tabular foundation models moderately predicted pathological complete response after TNT and showed potential to support watch-and-wait selection after recalibration.
In a large Korean health-screening cohort, chest radiograph-derived age acceleration was associated with both prevalent and incident PRISm and obstructive lung disease, supporting chest X-ray AI as a potential opportunistic marker of early
A retrospective cohort study using AI to analyze radiology reports reveals high prevalence of incidental thyroid findings, strong association with diagnostic cascades, and potential overdiagnosis of low-risk thyroid cancers.
A cross-sectional evaluation of AI-generated clinical notes found significant quality deficits compared to human-produced notes, particularly in thoroughness, organization, and usefulness.
A VITAL-AF trial analysis reveals that combining ECG-based artificial intelligence with clinical risk scores significantly improves atrial fibrillation screening efficiency. The study demonstrates that high-risk individuals benefit most fro
Researchers have developed an AI-powered framework that uncovers a non-linear relationship between tumor-stroma ratio and mortality in hepatocellular carcinoma, using a novel token-guided multimodal fusion approach to improve prognostic acc
Researchers developed and validated ADAPT-CEC, an artificial intelligence algorithm that can adjudicate cardiovascular events across different trial definitions. A hybrid approach combining AI with selective human review achieved 95.6% accu
A large-scale multicenter RCT investigating AI-assisted gastric neoplasm detection found no significant improvement in pathologically confirmed detection rates but demonstrated significant reductions in procedural blind spots and benefits f
This review evaluates the breakthrough ECG2HF model, a publicly available AI tool that outperforms standard clinical scores in predicting 10-year heart failure risk using 12-lead ECG waveforms.
A multicenter study in JAMA Ophthalmology introduces a deep learning model using domain-shift technology to accurately detect macular diseases across different OCT hardware vendors, achieving high negative predictive value and establishing
The MASAI trial demonstrates that AI-supported mammography screening achieves higher sensitivity and non-inferior interval cancer rates compared to standard double reading, while maintaining specificity and significantly reducing radiologis
The TRICORDER trial demonstrates that while AI stethoscopes are capable of detecting heart failure, atrial fibrillation, and valvular disease, their real-world implementation in UK primary care did not yield a statistically significant incr
A crossover study involving 30 ophthalmologists and trainees reveals that while Claude-3.5-Sonnet collaboration improves diagnostic accuracy in complex cases, it also increases confidence in incorrect decisions and fails to reach the perfor
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