How AI Improves Access to Heart Health Technology

Case Tuesday: Cardiac CT A patient presents with chest pain. The question is urgent: is this a heart attack waiting to happen, or something else? A CT coronary angiogram is performed. For the radiologist, this means carefully assessing coronary arteries, looking for stenosis, calcifications, and subtle plaques. The challenge: Coronary CTs generate hundreds of slices, often complex to interpret. Subtle plaques can be easily overlooked. Quantifying calcium scores and stenosis consistently takes significant time. This is where #AI is showing real promise: Automated calcium scoring to assess cardiovascular risk Plaque detection and quantification to support precise diagnosis Tools that standardize reporting and improve communication with cardiologists The radiologist’s expertise is essential in interpretation and clinical context but AI ensures that the assessment is faster, more reproducible, and more actionable. The impact: Earlier detection of coronary artery disease. Better risk stratification for patients with chest pain. Closer collaboration between radiology and cardiology teams As Chief Medical Officer at GE HealthCare, I see cardiac CT as a shining example of how AI doesn’t just enhance workflows it helps us move toward preventive, precision medicine that saves lives before catastrophe strikes. Do you see AI as the tipping point that will make cardiac CT more widely adopted as a first-line test for chest pain? #CaseTuesday #CardiacCT #AIinHealthcare #Radiology #HeartHealth #GEHealthcare

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.  

Imagine catching a #heart or #kidney disease before you feel a single symptom... all with a blink at a retinal camera? That’s exactly what Mediwhale, a South Korean health tech startup, is making possible. Their AI-powered tool, Reti-CVD, uses deep learning to analyze retinal images and predict risks for cardiovascular, kidney, and even eye diseases... no blood tests or CT scans required. Why the #Retina? The retina is a window to your vascular and nervous system. By analyzing blood vessels in the eye, Mediwhale’s AI can spot early warning signs of “silent killers” like heart and kidney disease, which often don’t show symptoms until it’s too late. For patients with chronic conditions like diabetes or obesity, this kind of early detection is a game-changer. How It Works: 1. A quick, non-invasive retina photo is taken. 2. AI scans for biomarkers like blood pressure, kidney function, and cardiovascular risk. 3. The system flags patients as low, medium, or high risk, helping doctors prioritize care. Real-World Impact: - Already used in hospitals across South Korea, Dubai, Italy, and Malaysia. - Over 7,200 cases in 57 Korean hospitals as of August 2024. - Approved in the EU, UK, Korea, and aiming for US FDA approval in 2025. - Raised $12 million in 2024 to expand globally and enhance its AI. Why It Matters: - Fast, affordable, and radiation-free screening. - Helps doctors make smarter, quicker decisions, potentially preventing irreversible harm. - Patients take their health more seriously when they see their risks visualized. The Human Touch: Mediwhale’s founder, Kevin Choi lost much of his vision to glaucoma before he knew he was at risk. His mission is to help others catch disease early, when it’s still treatable. What’s Next? With FDA approval on the horizon and plans to expand into chronic kidney disease prediction, the company is setting a new standard for early, AI-powered preventive care. Would you be willing to give this a wink?

From Acquisition to Analysis: How AI is Revolutionizing Cardiac MRI by Solenn Toupin, Ph.D. and Théo Pezel, M.D., Ph.D. (Lariboisière Hospital, MIRACL.ai, Multimodality Imaging for Research and Analysis Core Laboratory: Artificial Intelligence, AP-HP, Paris, France). Artificial intelligence (#AI) is emerging as a powerful ally in cardiac #MRI, addressing many of the challenges that previously limited its efficiency and accessibility. By automating and optimizing steps from protocol planning and image acquisition to reconstruction, analysis, and integration with clinical data, AI can make cardiac MRI faster, more consistent, and more widely available. Far from replacing clinicians, AI supports them by reducing repetitive tasks, improving reproducibility, and enabling the extraction of advanced diagnostic and prognostic information. An important aspect of this evolution is the integration of cardiac MRI into a multimodality framework where it is combined with other imaging techniques such as echocardiography or CT, and with clinical, biological, and electrophysiological data. This approach paves the way for advanced concepts like the digital twin – a virtual model of the patient’s heart that can guide diagnosis and therapy planning, further enhancing precision and personalization in cardiovascular care. The authors explore how AI is transforming their cardiac MRI practice in four main domains: 1. Planning and acquisition: including automated plane prescription and parameter optimization 2. Image reconstruction: accelerating acquisitions and improving image quality 3. Image analysis and post-processing: enabling rapid and consistent quantification 4. Development of diagnostic and prognostic tools: integrating imaging with multisource and multimodal patient data Continue reading: https://lnkd.in/di6k3PED #MagnetomWorld #WhyCMR #CardiacMRI Gaia Banks Siemens Healthineers 

We are seeing progress in integrating artificial intelligence into mainstream healthcare with the approval of several new Category I CPT codes for AI-enabled and algorithmic services in the 2026 code update. This development signals more than just formal recognition — it opens the door to reimbursement and broader adoption of clinically meaningful AI tools. Among the newly codified services are AI-driven analyses of coronary CTA to assess arterial plaque, algorithmic perivascular fat analysis for cardiac risk stratification, multispectral imaging for burn wound evaluation, and AI-assisted detection of cardiac dysfunction using acoustic and ECG data. The inclusion of these services under Category I — the standard designation for well-established and widely accepted medical procedures — reflects growing clinical consensus around the value of these technologies. Of course, coding is only part of the story. The next phase is reimbursement: CMS will review these codes, assign relative value units (RVUs), and determine how they will be reimbursed in the Medicare Physician Fee Schedule. Once that happens, both Medicare and private insurers will have the framework to pay for these AI-enabled services. Reimbursement will be critical for determining how quickly these innovations reach patients at scale. #healthtech #AIinHealthcare #CPT2026 #Reimbursement #DigitalHealth #ArtificialIntelligence #MedicalInnovation #MedTech #AI

Researchers at Scripps Research have developed an AI tool that enables accurate heart condition diagnoses using just three ECG leads instead of the traditional 12. By analyzing data from over 600,000 ECGs, the AI reconstructs full 12-lead readings from the reduced lead set. Clinical evaluations showed cardiologists achieved 81.4% accuracy in identifying heart attacks with AI-reconstructed ECGs, closely matching the 84.6% accuracy of standard 12-lead ECGs. This breakthrough could make ECG diagnostics more accessible and cost-effective, particularly in resource-limited settings. #AI #HealthcareInnovation #Cardiology #ECG #DigitalHealth #MedicalTechnology #HealthTech https://lnkd.in/dw3u6Bsf

🏠 The Future of Heart Failure Care: Bringing Treatment to the Patient’s Home 🚀 At #THT2025, I had the privilege of speaking about a critical shift in heart failure (HF) management—moving beyond episodic, hospital-based care to a patient-centered, home-based model. The reality is that our current system is unsustainable, with: 📊 1.1M HF hospital discharges & 1.3M ER visits annually 📈 $31B in HF-related costs, projected to hit $70B by 2030 👥 A 46% expected increase in HF patients by 2030 Why Home-Based Management? ✅ Reduce hospitalizations & readmissions, increase health days at home ✅ Ease the burden on care teams with streamlined workflows ✅ Leverage emerging digital & AI-driven tools for early intervention ✅ Addresses disparities in HF care access & outcomes, overcome inertia! Innovations Driving This Shift 🔹 Remote Monitoring & AI Algorithms Bioimpedance, ballistocardiograph, seismocardiography, phonocardiography, ECG, and other variables to identify congestion before it leads to hospitalization. 🔹Smartphone-based HF detection—improving accessibility & early intervention. 🔹 The “Hospital-at-Home” Model High-acuity care is delivered in the home through a 24/7 command center. Virtual visits + on-demand clinician dispatch to preserve continuity of care. FDA-collaborated remote tech enabling proactive, rather than reactive, HF care. By combining virtual management, predictive analytics, and AI-assisted triage, we can envision a future in which we drastically reduce hospital burden and improve patient outcomes. 🔹 What are your thoughts on the shift toward home-based HF care? 🔹 How can we scale these technologies while preserving health equity? #HeartFailure #DigitalHealth #AIinHealthcare #RemotePatientMonitoring #THT2025

A routine mammogram may be detecting heart disease—and most people don’t know it. AI is revealing something bigger: the value of a test isn’t just what it was designed to detect—it’s what else it can show. Thanks to work done through the Mayo Clinic and Emory University, researchers can now use AI to identify arterial calcification in breast tissue from standard mammograms—potentially signaling cardiovascular risk. That matters. Plaque rarely exists in isolation. If it’s present in one vascular bed, it may reflect disease elsewhere—including the coronary arteries. And in women, heart disease doesn’t always present in textbook ways. So the real question is: How many “two-for-one” diagnostics already exist in plain sight? If this scales, AI won’t just improve efficiency—it will reshape screening: Earlier detection Smarter intervention Better outcomes Not replacing clinicians—just helping us see more in the data we already have. AI isn’t just about automation. It is helping us extract more clinical insight from tests we’re already performing. If you haven’t had the chance to read about this yet, I encourage you to check it out via The Washington Post: https://lnkd.in/e8dKj5S8 #WomensHealth #AI

One of the more important shifts we’re seeing in structural heart care is how we think about timing. For years, clinicians have relied on established thresholds to guide when to intervene, but they don’t always capture how quickly disease can progress or which patients are quietly moving into higher-risk territory. A recent publication in JSCAI explores how AI is starting to change that by helping identify patients earlier and with more precision than we’ve had before. This has real implications for planning care and confirming who may benefit from closer follow up or earlier intervention. An interesting read as we continue to refine how we assess progression and think about timing in structural heart disease: https://bit.ly/3Qa7nYg #ArtificialIntelligence #StructuralHeart #AI