AbstractAims. Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intellig
/PRNewswire/ -- Garmin (NYSE: GRMN) today announced a product integration with Natural Cyclesº, the company behind the first and only FDA-cleared app for birth...
WHOOP secures $575m in Series G funding at a $10.1bn valuation to expand its AI-powered personalised health platform globally.
Oura dévoile son premier LLM propriétaire dédié à la santé des femmes. Une IA cliniquement ancrée pour un suivi personnalisé de la puberté à la ménopause.
Explore how IoT and AI improve patient outcomes through real-time monitoring, predictive analytics, automated alerts, and smarter healthcare workflows.
Explore how IoT and AI improve patient outcomes through real-time monitoring, predictive analytics, automated alerts, and smarter healthcare workflows.
Confier une décennie de battements cardiaques à une IA semblait être l'étape logique du « Quantified Self ». Mais quand l'assistant d'OpenAI transforme des approximations techniques en diagnostic alarmiste, le rêve de la médecine prédictive vire à la farce hypocondriaque.
Apple Watches helped researchers detect arrhythmia four times more often – proving that wearables can really save your life.
Apple Watches helped researchers detect arrhythmia four times more often – proving that wearables can really save your life.
Les montres connectées sont des outils d’observation et de tendance, utiles pour documenter le vécu subjectif et le contexte, mais pas comme substituts à une exploration du sommeil lorsqu’une pathologie est suspectée
Au CES 2026 de Las Vegas, Y-Brush ne se contente plus de promettre un brossage plus rapide. La marque française y présente une gamme élargie de brosses électriques, dont un modèle expérimental capable d'analyser l'haleine pour détecter certains signaux d'hygiène. La start-up avance donc du côt
Insulin resistance, a precursor to type 2 diabetes, is characterized by impaired insulin action in tissues. Current methods for measuring insulin resistance, while effective, are expensive, inaccessible, not widely available and hinder opportunities for early intervention. In this study, we remotely recruited the largest dataset to date across the US to study insulin resistance (N=1,165 participants, with median BMI=28 kg/m2, age=45 years, HbA1c=5.4%), incorporating wearable device time series data and blood biomarkers, including the ground-truth measure of insulin resistance, homeostatic model assessment for insulin resistance (HOMA-IR). We developed deep neural network models to predict insulin resistance based on readily available digital and blood biomarkers. Our results show that our models can predict insulin resistance by combining both wearable data and readily available blood biomarkers better than either of the two data sources separately (R2=0.5, auROC=0.80, Sensitivity=76%, and specificity 84%). The model showed 93% sensitivity and 95% adjusted specificity in obese and sedentary participants, a subpopulation most vulnerable to developing type 2 diabetes and who could benefit most from early intervention. Rigorous evaluation of model performance, including interpretability, and robustness, facilitates generalizability across larger cohorts, which is demonstrated by reproducing the prediction performance on an independent validation cohort (N=72 participants). Additionally, we demonstrated how the predicted insulin resistance can be integrated into a large language model agent to help understand and contextualize HOMA-IR values, facilitating interpretation and safe personalized recommendations. This work offers the potential for early detection of people at risk of type 2 diabetes and thereby facilitate earlier implementation of preventative strategies.
Wearable devices, from smartwatches to fitness trackers, have become ubiquitous, continuously capturing a rich stream of data about our lives. They record our heart rate, count our steps, track our fitness and sleep, and much more.
Des chercheurs viennent de présenter un accessoire médical révolutionnaire. Il s’agit d’un « simple » stéthoscope doté de capteurs alimentés par l’intelligence artificielle, capable de détecter de nombreuses pathologies en…
In an era where precision medicine is progressively reshaping the landscape of neurological care, a groundbreaking study published in npj Parkinson’s Disease unveils compelling evidence supporting the integration of wearable technology in the management of Parkinson’s disease.
The tech giant's SensorLM analyzes data from wearables to provide personalized insights into how everyday life affects one's body.
Your patient’s smartwatch tracks more than steps. It could drag you into a legal gray zone.
The device captures various types of heart data that could be used in AI models for cardiovascular parameters with the aim of lowering barriers to monitoring.
Wearable activity trackers offer an appealing, low-cost tool to address physical inactivity.
This systematic review of systematic reviews and meta-analyses (umbrella review) aimed
to examine the effectiveness of activity trackers for improving physical activity
and related physiological and psychosocial outcomes in clinical and non-clinical populations.
Seven databases (Embase, MEDLINE, Ovid Emcare, Scopus, SPORTDiscus, the Cochrane Library,
and Web of Science) were searched from database inception to April 8, 2021.
Faut-il avoir recours à l’intelligence artificielle pour mieux se réapproprier son corps ? Une multitude d’objets connectés, dopés à l’IA, ont envahi le marché de la santé, offran
In recent years, the landscape of Parkinson’s disease management has been dramatically transformed by the advent of wearable technology.A groundbreaking systematic review, led by Packer, Debelle, Bailey, and colleagues, published in npj Parkinson’s Disease, illuminates how these devices are revolutionizing...
In recent years, the landscape of Parkinson’s disease management has been dramatically transformed by the advent of wearable technology.A groundbreaking systematic review, led by Packer, Debelle, Bailey, and colleagues, published in npj Parkinson’s Disease, illuminates how these devices are revolutionizing...
In recent years, the landscape of Parkinson’s disease management has been dramatically transformed by the advent of wearable technology.A groundbreaking systematic review, led by Packer, Debelle, Bailey, and colleagues, published in npj Parkinson’s Disease, illuminates how these devices are revolutionizing...
The health tracker brand has unveiled several medical-minded features, like ECG and blood pressure monitoring, for its Whoop 5.0 and Whoop MG launch.
The health tracker brand has unveiled several medical-minded features, like ECG and blood pressure monitoring, for its Whoop 5.0 and Whoop MG launch.
medRxiv - The Preprint Server for Health Sciences...
The rapid increase in real-time health information collected from wearable devices has allowed digital biomarkers to emerge as a promising tool to support perioperative care, including surgical prehabilitation, intra-operative guidance, and post-operative monitoring.
The company's patch uses intradermal electrochemical sensors to monitor glucose levels and provide metabolic insights to individuals with type 2 diabetes not on insulin.
Wearable devices that measure physiological and behavioral signals have become commonplace. There is growing evidence that these devices can have a meaningful impact promoting healthy behaviors, detecting diseases, and improving the design and implementation of treatments.
Wearable devices that measure physiological and behavioral signals have become commonplace. There is growing evidence that these devices can have a meaningful impact promoting healthy behaviors, detecting diseases, and improving the design and implementation of treatments.
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Cette étude a développé un modèle d'apprentissage en profondeur pour le diagnostic cardiaque interprétable à partir d'électrocardiogrammes (ECG), utilisant les résultats prédits par les ECG pour la stratification du risque de mortalité. Les résultats ont montré des performances de niveau cardiologue dans le diagnostic des arythmies et une amélioration de la stratification du risque de mortalité. Le modèle a été validé sur une grande base de données de 2,3 millions d'ECG de 1,6 million de sujets, démontrant des avantages significatifs pour le diagnostic explicatif et la stratification du risque de mortalité.