Section
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.
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.
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
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.
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 sh
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.
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.
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.
A novel deep learning model predicted retinal nerve fiber layer thickness from optic disc photos, enabling risk stratification for primary open-angle glaucoma in ocular hypertension patients, with baseline and longitudinal changes strongly
Deep learning models predicting RNFL thickness from optic disc photographs can identify ocular hypertension patients at higher risk for primary open-angle glaucoma, enhancing early risk assessment and monitoring disease progression.
スマートフォンを用いたスマートデジタル介入（Smart-T）は、低所得者成人において26週間の7日間禁煙率を有意に改善し、ニコチン置換療法と組み合わせた場合、標準的なQuitGuideアプリを上回ることが示されました。
