An AI-driven model accurately detects imaging-based extranodal extension in HPV-positive oropharyngeal cancer from CT scans, improving outcome prediction beyond expert radiologist assessments and identifying patients at higher risk of poore
This study developed and validated machine learning models with high accuracy to predict early pediatric sepsis and septic shock within 48 hours using multisite electronic health record data in emergency departments.
This retrospective national cohort study reveals that Medicare Advantage (MA) enrollees undergoing major cancer surgery are less likely to receive care at high-quality hospitals compared to traditional Medicare beneficiaries, raising concer
This study demonstrates that an AI algorithm provides more reproducible and prognostically relevant assessment of tumor-infiltrating lymphocytes in melanoma compared to traditional pathologist methods, offering a promising tool to improve c
AI-detected tumor-infiltrating lymphocytes on routine histology independently predict response and survival in advanced melanoma treated with anti-PD-1 therapies, outperforming manual scoring and offering an accessible biomarker for immune
デジタルヘルスプログラムは、高リスク個体の肺がんスクリーニング率を大幅に向上させ、予防医療の改善にデジタルツールの潜在力を示しています。
A digital health program significantly increased lung cancer screening rates among high-risk individuals, underscoring digital tools’ potential to improve preventive care.
Artificial intelligence is reshaping health care, offering vast potential benefits alongside significant challenges. The JAMA Summit emphasizes the need for robust evaluation, equitable deployment, and multi-stakeholder collaboration to ens
AI demonstrates high accuracy in identifying mpox and other anogenital conditions from clinical images, yet significant research gaps and validation needs remain before clinical adoption.
A cross-sectional study identifies four distinct profiles of nonverbal learning disability (NVLD) in children, revealing significant heterogeneity in visual-spatial deficits, academic skills, and psychiatric diagnoses, highlighting implicat
This study assesses the educational quality and popularity of Spanish-language medical microvideos on TikTok in Latin America, revealing a discordance between engagement metrics and content quality, emphasizing the need for integrated evalu
A recent study assessed the quality, reliability, and popularity of anemia-related videos on YouTube, revealing moderate variability and highlighting the importance of health professionals’ involvement to ensure accurate online conten
A comprehensive analysis of 903 FDA-approved AI medical devices reveals limited clinical performance data and demographic inclusivity, underscoring the need for ongoing evaluation to ensure safe, effective clinical application.
Integrated radiomic-clinical machine learning models outperform traditional clinical biomarkers in predicting survival and treatment response in unresectable hepatocellular carcinoma patients undergoing atezolizumab plus bevacizumab therapy
AIRE-CHB, a novel AI-enhanced ECG model, significantly improves prediction of incident complete heart block, outperforming traditional bifascicular block assessment and offering promising clinical utility in risk stratification and manageme
AI-assisted digital pathology improves inter-pathologist agreement on fibrosis staging in metabolic dysfunction-associated steatohepatitis (MASH), enhancing clinical trial accuracy and efficiency.
A new AI model, Delphi-2M, predicts the risk of over 1,000 diseases up to 20 years in the future with remarkable accuracy, transforming disease prevention and personalized health management.
A multicenter trial demonstrated that integrating AI-based computer-aided detection with water exchange colonoscopy significantly increases adenomas per colonoscopy without prolonging procedure time or increasing non-neoplastic lesion detec
This review evaluates AI agent systems built on large language models in healthcare, demonstrating improved clinical task accuracy over base models, especially in complex scenarios. Multi-agent architectures show promise but require further
An RCT evaluated an AI-assisted framework for electrodiagnostic report interpretation, finding no significant improvement over standard physician reporting but highlighting potential workflow benefits for routine cases.
Open language-specific specialty feeds and department pages.