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Advancing Early Pediatric Sepsis Detection: Machine Learning Models Predicting Onset Within 48 Hours
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.
Medicare Advantage and Cancer Surgery Outcomes: Are Patients Being Routed Away from High-Quality Hospitals?
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
Enhancing Melanoma Prognosis: AI-Driven Quantification of Tumor-Infiltrating Lymphocytes Outperforms Traditional Pathology
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
Artificial Intelligence-Detected Tumor-Infiltrating Lymphocytes as a Biomarker in Anti-PD-1-Based Therapy for Advanced Melanoma: Clinical Evidence and Translational Perspectives
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
Enhancing Lung Cancer Screening Uptake Through a Direct-to-Patient Digital Health Intervention: Insights from a Randomized Clinical Trial
A digital health program significantly increased lung cancer screening rates among high-risk individuals, underscoring digital tools’ potential to improve preventive care.
患者向けデジタルヘルス介入による肺がんスクリーニング受診率の向上：ランダム化比較試験からの洞察
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。
Navigating the AI Revolution in Health Care: Insights from the JAMA Summit
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
Harnessing Artificial Intelligence for Diagnosing Sexually Transmitted Infections and Anogenital Dermatoses: Insights from a Systematic Review and Meta-Analysis
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.
Decoding Nonverbal Learning Disability in Children: Distinct Profiles and Their Clinical Implications
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
Evaluating Medical Education Microvideos on TikTok: Quality Versus Popularity in Latin America
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
Evaluating Anemia-Related YouTube Videos: Reliable Education or Misinformation Risk?
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 content.
Evaluating the Clinical Generalizability of FDA-Approved AI-Enabled Medical Devices: Insights and Implications
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.
Advancing Precision Medicine in Hepatocellular Carcinoma: Machine Learning Radiomic Models Surpass Clinical Biomarkers in Predicting Immunotherapy Outcomes
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
Harnessing Artificial Intelligence for Enhanced Prediction of Complete Heart Block Risk via Electrocardiography
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
Enhancing Fibrosis Scoring in MASH: The Role of AI-Enhanced Digital Pathology
AI-assisted digital pathology improves inter-pathologist agreement on fibrosis staging in metabolic dysfunction-associated steatohepatitis (MASH), enhancing clinical trial accuracy and efficiency.
Delphi-2M: The AI That Predicts Your Health 20 Years Ahead
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.
Enhanced Adenoma Detection in Water Exchange Colonoscopy Using AI: Insights from a Two-Center Randomized Trial
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
Advancing Clinical Care with AI Agents: Systematic Review of Performance and Integration in Medicine
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
Evaluating AI-Enhanced EMG Reporting: Insights from a Randomized Controlled Trial
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.
Harnessing AI for Personalized Depression Treatment: Insights from the AID-ME Cluster Randomized Trial
The AID-ME trial demonstrates that an AI-powered clinical decision support system significantly improves remission rates and accelerates symptom improvement in moderate to severe major depressive disorder.
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