How AI Improves Access to Heart Health Technology

⭐ Deep Learning takes on heart failure: AI model offers a non-invasive future ⭐ Exciting developments on the horizon! Researchers from Massachusetts Institute of Technology and Harvard Medical School have introduced an innovative Deep Learning model, CHAIS, that could redefine how we monitor and prevent heart failure. Traditionally, invasive procedures like Right Heart Catheterization (RHC) have been the gold standard for assessing heart health. But CHAIS offers a groundbreaking alternative: a non-invasive approach using ECG signals to predict heart failure risk, with accuracy comparable to RHC. ✔️ Key Benefits: ➜ Noninvasive and convenient: Patients wear a simple patch on their chest, providing continuous monitoring without the need for hospital visits. ➜ Accurate and timely: Predicts heart health risks with impressive precision, allowing early intervention. ➜ Broad impact: Could significantly reduce hospital readmissions and ease pressure on healthcare workers. This AI-driven approach is poised to improve patient outcomes and make high-quality heart care accessible to everyone, regardless of location or socioeconomic status. 🌍❤️ #AIinHealthcare #Innovation #HeartHealth #MIT #HarvardMedicalSchool #DeepLearning #ArtificialIntelligence

When every second counts ⏱️ Heart failure, where the heart struggles to pump enough blood, is often diagnosed too late—typically in hospitals. But AI technology is changing that. Our team at Ardas collaborated with hardware developers to create an AI-powered stethoscope system designed to make heart disease diagnostics faster, more accessible, and more accurate: - For healthcare professionals: It delivers real-time analysis of heart and lung sounds, helping detect heart failure and arrhythmias earlier. - For patients: Securely tracks and analyzes health data for personalized care and early intervention, even at home. - For administrators: Integrates with EHRs and HIS for smooth, secure, and compliant data flow. By using cloud, IoT, and AI, we’re contributing to more efficient, data-driven healthcare and better patient outcomes. ➡️ Read more about how this innovation is shaping healthcare: https://lnkd.in/eXnznhh6 What are your thoughts on AI’s role in healthtech? Let’s discuss this in the comments. #HealthTech #AI #IoT #DigitalHealth

🫀 In a recent interview, I explained how AI is changing the way we see the heart—literally. Because when it comes to your heart, every second—and every pixel—matters.  From CT to MRI to Ultrasound. At Philips, we’re using AI to make heart scans faster, clearer, and more comfortable for patients. Our AI-powered tools are already helping doctors spot heart problems earlier—sometimes even before symptoms appear. Here’s how: 🔹 CT 5300 – Built for AI-based image reconstruction, delivering clearer images with less radiation. 🔹 Spectral CT 7500 – Enables 15-minute Spectral scans that can rule out multiple CV conditions at once. 🔹 Precise Cardiac – AI that compensates for heart motion, improving diagnostic confidence. 🔹 SmartSpeed MRI – Uses Dual AI engines to sharpen image quality and cut scan times. 🔹 Compact Ultrasound 5500CV – Portable and powerful, it reduces scan time by up to 50% and improves consistency. And we’re not stopping there. Our collaboration with NVIDIA will build a powerful MRI foundational model to push diagnostic accuracy even further, helping us generate new MRI applications. More here->  https://lnkd.in/eZKcX8WE #Cardiac #AI

Earlier this year, I witnessed how AI and machine learning can enhance patient care in cardiology in practical, impactful ways.   A speaker at the AI Cures conference at MIT shared how ML can be applied to data from minimally invasive home monitoring devices like ECGs.   A patient’s hemodynamic measures are incredibly useful in monitoring a patient, however given the equipment involved, can only be done in the hospital. With this new algorithm that was presented, the model can actually infer a patient's hemodynamic measurements, like pressures, fairly accurately from the ECG waveform data alone. I found that rather amazing. And useful!   This means patients could be monitored closely at home, with the ML model providing cardiologists with clinical indicators like pressure risks they wouldn't otherwise have without bringing the patient in.   Examples like this, where ML provides incremental advantages and empowers clinicians, excite me most about AI in healthcare.   The technology is maturing to the point where we can apply it to increase access to care, fill in gaps, and connect disparate data sources - rather than pursue AI applications for their own sake.   What other opportunities exist where AI/ML could provide an extra layer of insight to improve clinicians' abilities? I'd love to hear your ideas! #AI #artificialintelligence #codetocare