AI Applications in Cardiac Care

Recently a 14 year old tech whiz developed an AI app that can detect signs of heart failure in 7 seconds. His invention Circardian AI uses a smartphone's microphone to record heart sounds which are then analyzed with AI providing rapid pre-screening for potentially life threatening conditions. The phone has to simply be placed near the chest and Circadian AI can "listen" to the heart. It has achieved 96% accuracy in clinical trials involving almost 19,000 patients across the US and India. In the US, efforts may focus on integrating a tool like this into existing hospital IT systems, streamlining data ingestion, and securely storing information in the cloud. However, in lower resource settings, where access to preventative care is limited and the nearest cardiologist may be a day’s travel away, this AI model could have an immediate and transformative impact. According to the World Health Organization cardiovascular disease remains the leading cause of death worldwide, highlighting the critical role AI-driven tools can play in addressing this global health challenge. #cardiologyAI #digitalhealth #healthcareIT #cardiology #globalhealth More about the creator: Siddharth Nandyala https://lnkd.in/dhge4thy About Circadian AI: https://lnkd.in/dURf7dz7

Atrial fibrillation (AF) is something I see often in patients— But it’s not always easy to detect, and even harder to manage long-term. It’s the most common sustained arrhythmia and can increase the risk of stroke, heart failure, and hospitalizations. What’s exciting is how AI is beginning to step in— Not to replace care, but to enhance it. Wearables and smart ECG tools can now detect AF early, sometimes before symptoms even start. During treatment, AI-assisted mapping can help guide ablation procedures with more precision and fewer recurrences. And on the bigger scale, AI can sift through vast patient data to personalize treatment—predicting stroke risk, adjusting medications, and even helping us understand which lifestyle changes are most impactful for each individual. It’s not a cure-all, but it’s a helpful hand. A reminder that thoughtful tech can support thoughtful care. Follow Zain Khalpey, MD, PhD, FACS for more on Ai & Healthcare. #AtrialFibrillation #AFib #HeartHealth #AIinHealthcare #DigitalHealth #WearableTech #PreventiveCare #Cardiology #MachineLearning #MedTech #Electrophysiology #Arrhythmia #StrokePrevention #SmartMedicine #DataDrivenCare #ClinicalAI #HealthcareTech #PrecisionMedicine #PatientCare #AIforGood #HealthcareInnovation

The headline that caught my eye this week was "AI Trial to Spot Heart Condition Before Symptoms." Here's my take: Artificial intelligence holds substantial promise to improve quality and reduce costs in healthcare. One example from Leeds involves an algorithm that scours medical records for early warning signs of atrial fibrillation (AF) before symptoms appear — potentially preventing thousands of strokes. The results suggest that by analyzing existing medical records for patterns that human physicians might miss, AI can flag high-risk patients for early intervention. The trial has already identified cases like a 74-year-old former Army captain who had no symptoms but can now manage his condition effectively. This is particularly significant given that AF contributes to around 20,000 strokes annually in the UK alone. As Professor Chris Gale notes, too often the first sign of undiagnosed AF is a stroke — an outcome this technology could help prevent. The broader implication here is about AI's role in healthcare: not replacing physicians but augmenting their ability to identify risks earlier and intervene before conditions become critical.  

“Researchers have developed a new artificial intelligence (AI) model capable of evaluating electrocardiogram (ECG) results and identifying signs of occlusion myocardial infarction faster than other modern techniques. The group shared its findings in Nature Medicine, noting that the model’s performance was much better than expected.[1] “When a patient comes into the hospital with chest pain, the first question we ask is whether the patient is having a heart attack or not. It seems like that should be straightforward, but when it’s not clear from the ECG, it can take up to 24 hours to complete additional tests,” lead author Salah Al-Zaiti, PhD, RN, an associate professor of emergency medicine and cardiology at the University of Pittsburgh, said in a prepared statement. “Our model helps address this major challenge by improving risk assessment so that patients can get appropriate care without delay.” Al-Zaiti et al. trained their algorithm using ECG data from more than 4,000 patients who presented with chest pain at one of three Pittsburgh hospitals. They validated the model using data from nearly 3,300 patients seen at a different health system. Researchers have developed a new artificial intelligence (AI) model capable of evaluating electrocardiogram (ECG) results and identifying signs of occlusion myocardial infarction faster than other modern techniques. The group shared its findings in Nature Medicine, noting that the model’s performance was much better than expected.[1] “When a patient comes into the hospital with chest pain, the first question we ask is whether the patient is having a heart attack or not. It seems like that should be straightforward, but when it’s not clear from the ECG, it can take up to 24 hours to complete additional tests,” lead author Salah Al-Zaiti, PhD, RN, an associate professor of emergency medicine and cardiology at the University of Pittsburgh, said in a prepared statement. “Our model helps address this major challenge by improving risk assessment so that patients can get appropriate care without delay.” Al-Zaiti et al. trained their algorithm using ECG data from more than 4,000 patients who presented with chest pain at one of three Pittsburgh hospitals. They validated the model using data from nearly 3,300 patients seen at a different health system.”

⭐ 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