Importance of AI in Cardiology

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.  

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

AI Spots a Rare Heart Killer from a Single Echo—Now with FDA Breakthrough Status Cardiac amyloidosis is often missed until it’s too late. A new AI-enhanced echocardiography tool can flag it from one standard echo video clip—and it just received FDA Breakthrough Device designation. Why this matters: First commercially available AI echo tool to screen for amyloid cardiomyopathy Performance: AUROC 0.93, 85% sensitivity, 93% specificity, 96% NPV Impact: Earlier detection → earlier treatment → better outcomes for a notoriously underdiagnosed cause of heart failure Clinician takeaways: Consider AI screening in patients with unexplained LV wall thickening, HFpEF features, or overlapping phenotypes (e.g., HTN, HCM, AS) One clip, minimal workflow friction—ideal for broad screening in echo labs I’ve long advocated for the shift from reactive to proactive medicine. This is that future—arriving in the echo suite. Would you pilot this in your lab? What safeguards or workflows would you want in place first? (Link in comments.) #AIinHealthcare #Cardiology #Echocardiography #DigitalHealth #MedicalAI #Amyloidosis #HeartFailure #EarlyDiagnosis #HealthTech #FDA #MayoClinic

When nine cardiologists got access to AI, their error rate dropped by half. Results from a randomised controlled trial, just published in Nature Medicine. Clinical decision support has been discussed for years, but the evidence base has been thin. This is one of the first rigorous tests of what actually happens when doctors use AI at the point of care. General cardiologists were given 107 complex cardiac cases. Half the time they worked with a fine-tuned large language model. Half the time they worked alone. Decisions were evaluated blindly by subspecialists. AI-assisted decisions were rated better or equal in 67% of cases. Clinically significant errors dropped from 24% to 13%. Missing content dropped from 37% to 18%. The cardiologists themselves said AI improved their decisions 57% of the time and saved time in over half of cases. It is also worth remembering that this was conducted in the fall of 2024. The models available today are already meaningfully better. For years, the conversation has been about whether AI is ready for clinical use. This study suggests the conversation needs to move on.

Dr. Sajid Defended His PhD Today: Saving Lives with AI --------------------------------------------------- It is rare for a PhD thesis to have disruptive potential in day-to-day medical diagnostics and the power to save lives—but today, Dr. Muhammad Sajid presented such a achievement . Dr. Sajid successfully defended his PhD in the NUST College of EME, under the able supervision of Dr. Ali Hassan, where I had the privilege of being his co-supervisor. Towards the end of his presentation and Q&A session, I shared my thoughts: "For some students, you end up learning more from their work than you contribute. This was exactly the case with Dr. Sajid." His research, conducted in collaboration with the National University of Medical Sciences (NUMS), has led to a machine learning-based diagnostic system for Coronary Artery Disease (CAD). This innovation has the potential to revolutionize emergency cardiac care, ensuring that patients presenting with angina receive timely and accurate diagnoses. Doctors often face a life-or-death decision in emergency rooms—whether to send a patient for angiography (an invasive and costly procedure) or discharge them, only to later discover they had undiagnosed CAD, leading to a heart attack. Dr. Sajid’s work strengthens the field of cardiology by proving that a simple blood test—measuring just 3-5 key biomarkers—can decisively determine whether angiography is required. This non-invasive approach can: Reduce unnecessary angiographies Prevent misdiagnosed cardiac patients from being sent home Improve early detection and intervention—ultimately saving lives His research has already resulted in three publications in high-impact journals, and we now look forward to clinical trials that will translate this into real-world impact. Well done, Dr. Sajid! May your work pave the way for early and accurate cardiac diagnostics, bringing timely care to those who need it most.  Technical Highlights ----------------------- Coronary Artery Disease (CAD) is a leading cause of sudden cardiac arrest and remains a major global health challenge. Diagnosing CAD early in patients with chest pain is difficult, as angiography—though the gold standard—is invasive and not always accessible, especially in developing countries. Dr. Sajid’s research explored the potential of non-invasive biomarkers (including novel molecular and inflammatory markers) using machine learning (ML). His study combined datasets from NUMS-NIHD, applied feature selection, and tested ten ML classifiers to determine the most reliable biomarker-classifier combinations. Results: ✔️ Accuracy: 90.18% ✔️ Sensitivity: 92.33% ✔️ Specificity: 100% These findings not only validate previous medical studies but also significantly improve upon existing benchmarks, making this approach a game-changer for non-invasive CAD detection. AI-driven innovation is transforming healthcare. We will continue to advance this research, bringing life-saving diagnostics closer to reality.

AI models operating alone outperformed both physicians and physicians with AI access in a randomized diagnostic trial, and this state of the art review argues that designing how humans and AI interact matters as much as building better models. 1️⃣ Ischemic heart disease causes 9.44 million deaths and 185 million disability-adjusted life years globally each year, making cardiovascular disease the dominant target for AI-driven prevention. 2️⃣ In a randomized trial, physicians with LLM access scored the same as physicians without it (74%), while LLMs operating alone scored significantly higher (92%). 3️⃣ AI applied to surface electrocardiogram recordings can detect imminent atrial fibrillation, coronary artery occlusion, heart failure, and diastolic dysfunction, including via single-lead consumer smartwatch recordings. 4️⃣ An AI tool deployed at Mid and South Essex National Health Service Trust generated 1,910 additional patient visits and 377 fewer missed appointments in six months, projecting 80,000 additional visits per year at full rollout. 5️⃣ Chatbot responses to cardiovascular prevention questions were rated appropriate by clinical experts in 84% of cases, and chatbots outperformed verified physicians on a patient forum for both accuracy and empathy. 6️⃣ Machine learning designed drug candidates targeting key receptors in diabetes and obesity with up to sevenfold greater potency than traditionally designed molecules, signaling a shift in cardiovascular drug development. 7️⃣ A review of 200 AI healthcare cost studies found savings in the first year of deployment across all scenarios modelled; US annual AI savings are projected at $200 to $360 billion, with potential to reduce hospital stays by 25%. 8️⃣ The EU AI Act, the world's first comprehensive AI legal framework enacted in March 2024, introduces stricter requirements for training data, transparency, and human oversight in high-risk healthcare AI, though the authors warn it may slow implementation. 9️⃣ Only 38% of US patients trust medical AI despite nearly universal touchpoints with it, making consent frameworks and transparency a prerequisite for population-scale uptake, not an afterthought. 🔟 The review flags an emerging legal argument: failing to adopt a proven superior AI solution may itself expose health organizations to liability, as the ethics of withholding effective AI begin to mirror those of withholding effective treatments. ✍🏻 Benjamin Meder, Folkert Asselbergs, Euan Ashley. Artificial intelligence to improve cardiovascular population health. European Heart Journal. 2025. DOI: 10.1093/eurheartj/ehaf125 | Open Access

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 

🫀 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

Can AI help protect your heart from extreme heat? Extreme heat is more than uncomfortable—it places significant stress on the cardiovascular system. For people with heart disease, dehydration and heat can increase the risk of heart attacks, heart failure, arrhythmias, and stroke. Now imagine AI combining weather forecasts, wearable data, and your health profile to identify risk before symptoms develop and deliver personalized preventive alerts. The future of cardiology is not just treating disease—it’s predicting and preventing it. Climate is changing. Cardiology must evolve too. Could environmental data become the next vital sign in cardiovascular care? #Cardiology #ArtificialIntelligence #HeartHealth #DigitalHealth #ClimateHealth #PreventiveCardiology #PrecisionMedicine #Wearables #CCAD