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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
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.
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.
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 reveals that nearly 20% of online rhinoplasty photos on a major platform are digitally manipulated, using an AI model with high accuracy, highlighting the need for authenticity checks in aesthetic surgery imaging.
ChatGPT-4o achieves high accuracy on critical care board questions but shows notable limitations in image interpretation and reasoning, posing potential clinical risks.
This review synthesizes perspectives from key stakeholders on AI integration in home care, highlighting benefits, risks, and governance challenges to inform equitable, sustainable AI adoption.
Study found that use of an AI-driven gamified mobile app integrating home blood pressure monitoring significantly improved blood pressure control compared to usual care in hypertensive patients.
This study assesses the perceptual realism of synthetic laryngeal images created using StyleGAN3, revealing high ambiguity between real and synthetic frames and highlighting efficient training durations for realistic image generation.
A novel AI-driven risk prediction model for ovarian cancer demonstrates high accuracy and potential to improve early detection in screening settings.
A novel deep learning algorithm accurately detects moderate or greater aortic stenosis using AI-guided focused cardiac ultrasound acquired by novice operators, presenting a scalable solution to expand screening accessibility.
This review examines emerging evidence on AI-assisted LC-OCT as a noninvasive, accurate tool for detecting subclinical basal cell carcinoma in high-risk patients, highlighting diagnostic performance, subtype classification, and translationa
Machine learning models can effectively predict major 30-day complications following glossectomy for tongue cancer, improving individualized risk assessment over traditional methods.
