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This review synthesizes recent evidence on machine learning models integrating multimodal data for relapse prediction in MDD, highlighting moderate accuracy driven mainly by clinical risk factors and advocating for dynamic approaches to bet
A deep learning model identifying ARDS features on chest X-rays independently predicts 28-day mortality and ventilator duration beyond traditional clinical measures in patients undergoing invasive mechanical ventilation.
This review synthesizes evidence on the incremental predictive value of task-specific kinematic performance data combined with preoperative clinical variables for robotic surgery outcomes and surgeon experience prediction.
This study compared 11 AI platforms, including large language models and retrieval-augmented systems, for diagnosing and managing periodontal diseases using clinical vignettes rated by expert periodontists.
This study compares retinopathy of prematurity severity in US and Indian infants using an AI-derived vascular severity score, revealing epidemiological and pathophysiological differences that could impact diagnosis and management globally.
A deep learning model significantly improves lentigo maligna detection and dermatology resident diagnostic accuracy when applied to dermoscopic images of facial pigmented lesions, underscoring AI’s adjunctive role in clinical dermatol
A new machine learning model predicts post-rehabilitation walking ability in nontraumatic spinal cord injury patients with high accuracy, aiding early clinical decision-making and rehabilitation planning.
This qualitative study explores how ambient AI scribes affect ambulatory patient experience, revealing improvements in communication, visit thoroughness, and patient comfort influenced by demographic and relational factors.
An AI-generated panoramic dental imaging score correlates independently with diabetes, hypertension, ischemic heart disease, osteoporosis, and hyperlipidemia, supporting integrated medical-dental care.
This article explores the expanding role of artificial intelligence in plastic surgery, detailing medical benefits, legal risks, and strategies for risk mitigation to protect patients and practitioners.
A multicenter study demonstrates that interpretable machine learning applied to baseline OCT biomarkers accurately predicts chronicity in central serous chorioretinopathy, facilitating early risk stratification and personalized management.
This study assesses the safety, quality, and user acceptance of a conversational AI system aiding urgent primary care by engaging patients before appointments, demonstrating promising feasibility for clinical integration.
This article analyzes FDA-approved surgical AI devices, highlighting trends in technology, evidence quality, regulatory pathways, and implications for clinical adoption.
A novel deep learning model significantly improves diagnostic accuracy in distinguishing fibro-osseous jaw lesions (FD, COF, COD) from histology images, outperforming expert pathologists and offering a promising diagnostic support tool.
This review synthesizes evidence on medical interpreters’ critical roles in supporting resilience for non-English language preference patients during ambulatory diagnostic care, highlighting gaps and implications for AI interpretation
This study demonstrates promising accuracy of a patient-personalized AI system in detecting tracheostomy tube status and infant distress via video monitoring, highlighting potential for enhanced, continuous surveillance in high-risk infants
An AI-based automatic 3D virtual liver modeling tool accurately predicts intraoperative liver resection complexity (LRC) from preoperative CT scans, outperforming surgeons’ assessments and potentially improving surgical planning and p
The integration of artificial intelligence in obstetrics and gynecology necessitates clinical competence to ensure patient safety, verify outputs, and maintain physician accountability. Structured training and evaluation are essential for r
A no-code, agentic AI workflow successfully developed and externally validated a calibrated thyroid nodule malignancy classifier, demonstrating strong diagnostic performance across multiple cancer histologies and enhancing accessibility in
This study evaluates large language model (LLM) performance for predicting same-day hospital discharge, identifies primary error sources, and demonstrates that automated prompt optimization significantly improves predictive accuracy.
