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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.
Evaluating Diagnocat AI: Accuracy in Detecting Periapical Radiolucencies on CBCT Scans of Molars
A study assessing the AI-based platform Diagnocat reveals high sensitivity but moderate specificity in detecting periapical radiolucencies on CBCT scans of non-root-filled molars, with decreased accuracy post-root treatment, underscoring th
Harnessing AI in Surgical Education: Deep Language Learning Model-Based Simulation Enhances Undergraduate History-Taking Skills
A randomized controlled trial demonstrates that integrating deep language learning model (DLM) simulations into surgical training significantly improves history-taking skills and communication confidence among senior medical students.
Enhancing Dental Diagnostics: The Role of AI Assistance in Detecting Periapical Radiolucencies
A randomized controlled trial demonstrates that AI assistance moderately improves dentists’ accuracy in diagnosing periapical radiolucencies, mainly by reducing false positives, with significant benefits for junior clinicians and a shift to
Harnessing Machine Learning to Predict High-Risk Coronary Artery Disease: Insights from the SCOT-HEART Trial
Machine learning models trained on clinical data improve prediction of coronary artery disease on CT scans compared to traditional risk scores, potentially guiding better resource utilization in cardiovascular care.
Harnessing AI: LLM-Based Conversational Agents Reduce Depression and Anxiety Among Young Adults in Short Video App Trial
A 28-day RCT demonstrates that a conversational AI trained on a large language model effectively reduces mild depression and anxiety symptoms in young adults through dialog interventions integrated in short video apps.
AI-Assisted Optical Diagnosis in Colonoscopy: Ensuring Safety While Reducing Unnecessary Polypectomies
A randomized trial demonstrates that leaving small non-neoplastic colorectal polyps in situ with AI-assisted optical diagnosis is as safe and effective as systematic removal, supporting less invasive colonoscopy strategies.
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