Developing AI diagnostic tool for various medical professionals

Now, clinicians can utilize AI to read ECGs and detect diseases faster! After months of research, iteration, and testing, the eNest AI software development team is proud to share a major #HealthcareInnovation: we have developed an AI diagnostics solution that helps researchers and clinical teams automatically separate clear ECG data from background noise. An electrocardiogram (ECG) is a non-invasive, cheap, and widely used method of heart checking. Yet, ECG data can be filled with motion artifacts, baseline wander, and power-line interference, which can obscure the picture.   💡 What our tool does: > Removes common noise types (e.g., EMG, baseline drift, power-line interference) > Retains critical waveform features (P, QRS, T) > Supports real-time and offline denoising > Easily integrates into existing data pipelines or medical device workflows This ECG denoising AI tool can have wide applications in #PrecisionMedicine and #SmartHealthcare. The illustration below already shows promising results in pilot testing and a potential for a wide variety of use cases, including AI-powered diagnostics, AI clinical research on early detection of cardiac issues, and training robust AI models. If you are interested in learning more about the use of AI for diagnostics and ECG, feel free to contact our team here: https://lnkd.in/dj4hUZxF #SignalProcessing #HealthTech #AiinHealthcare #ClinicalAI #ECG

The Real Value of Imaging Isn’t in the Pictures—It’s in the Data....... AI scans are powerful. But without data integrity, interoperability, and resilient systems, innovation stalls. Medical imaging is exploding—not just in volume, but in value. AI-enhanced MRI is cutting scan times. CT-guided interventions are reducing risk. Mammography screening is enabling earlier cancer detection. Yet, the real story isn’t the images themselves—it’s the data behind them. Every scan creates a wealth of structured and unstructured information. If properly aggregated, analyzed, and shared, this data could: * Improve predictive diagnostics and treatment personalization * Reduce the cost of repeat imaging and unnecessary procedures * Enable real-time clinical decision support But today, much of this imaging data remains siloed—locked in proprietary systems or fragmented across providers. That gap limits the benefits AI can bring to both clinicians and patients. What will it take to close that gap? * Devices that are tested with data integrity in mind—ensuring signals captured at the board and subsystem level remain trustworthy all the way to analysis. * Sustained engineering support to manage component obsolescence and keep systems current with evolving data and compliance standards. * Faster prototyping and iteration cycles so hardware and software can evolve in step with AI models and regulatory pathways. * Resilient supply chains that guarantee the reliability of life-critical imaging equipment, even when global component availability shifts. The takeaway for industry leaders: unlocking imaging data interoperability is as important as the AI models themselves. Without it, innovation risks being incremental rather than transformative. With it, imaging could shift from being a diagnostic tool to a true population health engine. #MedicalImaging #DigitalHealth #HealthcareInnovation #AIinMedicine #MedTech #PatientOutcomes #HealthcareData

In AI-driven healthcare, the quality of your data determines the quality of your diagnosis.    At LexData Labs, we help medical AI innovators to unlock the full potential of their models through high-precision image annotation.  Here’s how targeted labeling can make a difference: ✔ Vessel Segmentation - We mark every artery and vein with pixel-perfect accuracy so AI can reliably detect vascular structures.  ✔ Lesion & Plaque Annotation - Pinpointing blockages and plaques helps train AI to quantify stenosis severity and spot early disease.  ✔ Anatomical Landmarks - Consistent tagging of bifurcations and branch points improves longitudinal tracking of patient scans.  ✔ Disease Stage Tagging - Clear labeling of early, moderate, and severe PAD cases enables faster triage.  ✔ Validation Sets - Gold-standard datasets help meet clinical accuracy and compliance benchmarks. 💡The result? AI models that are smarter, faster, and more clinically reliable empowering physicians to detect underdiagnosed vascular disease earlier and plan treatment with confidence.  Let’s talk about how we can make your medical AI more precise and impactful. 🌐 www.lexdatalabs.com  📩reach@lexdatalabs.com  #LexDataLabs #ImageProcessing #ComputerVision #Automation #DataAnnotation #AI #ScalableSolutions #DataOps 

Last year, the NHS deployed an AI tool across every stroke centre in England, designed to rapidly analyse CT scans. By automating the initial scan review, the tool aims to speed up diagnosis, reduce human error, and ensure that patients receive timely care. Early reports indicate that compared with manual review: Average time-to-treatment has dropped from 140 minutes to 79 minutes. The proportion of patients making a full recovery has nearly tripled – from 16% to 48%. Trained on thousands of imaging datasets, the model uses image texture analysis and pattern recognition to detect subtle variations in lesions and brain tissue that may be missed or take longer for human radiologists to interpret. The technology estimates the presence and severity of a stroke as well as the time since stroke onset, the potential reversibility of brain tissue damage, and helps determine the most appropriate treatment. Over 100,000 people are affected by stroke in the UK every year, meaning we likely all have, or will have direct experience of how quickly it can change lives. I'm grateful to see the growing maturity of AI in clinical medicine, and fascinated by how quickly early pattern recognition algorithms have moved through to increasingly sophisticated inference about tissue states and treatment options, to measurable real-world benefits in outcomes and efficiency. #EvoMedica #ModernMedComms #AI #AIinHealthcare #MedicalCommunications #MedComms #Pharma #LifeSciences #MedTech #Biotech #HealthTech #LLMs #ScientificWriting #DigitalHealth

Title: Healthcare AI Benchmarks: HealthBench vs MedAgentBench In healthcare AI, we are evolving beyond the textbook tests to real-world benchmarks. Two notable ones this year have been the OpenAI #HealthBench and the recent Stanford #MedAgentBench. They tackle two different aspects of real-world healthcare practice. They both apply to AI Agents, but in a different sense: #Reasoning_Agents and #Orchestration_Agents. I am building Patient Use AI #Reasoning_Agents (PearlsAI.org). These Agents use medical knowledge and reasoning to answer Clinical analysis questions. To my work and to those building Clinical Reasoning Co-Pilots, OpenAI HealthBench is most appropriate. Its use of test rubrics provides both in-depth testing as well as insights on what to improve. Then there are #Orchestration_Agents that take instructions from a doctor and carry-out multiple tasks involving EHRs, pharmacists, pathology, diagnostics, update medical records, auto-generate patient communications. For these agents, Stanford's #MedAgentBench is most suited. A word of caution. #Orchestration_Agents in healthcare are still fairly basic workflow executors. It is a common mistake to believe that once we have a good plan, its execution is a piece of cake. Ask any experienced surgeon, a pilot, a nuclear plant operator, or a military commander - Murphy inevitably strikes. And today's #Orchestration_Agents do not take into account Murphy or unanticipated situations, or the uncertainty. Both #Reasoning_Agents and #Orchestration_Agents hold much promise. We have just begun to scratch the surface. Note: Comment includes a quick summary of #HealthBench and #MedAgentBench. Eric Topol, MD Regina Barzilay Nigam Shah Pavan Nigam Jason Hunt Mihaela van der Schaar Laura Adams Susan Woods, MD MPH Dave deBronkart Manoj Sharma Indu Bhushan Shiv Raj Kathpalia Bharti Sinha Bhargav Patel, MD, MBA Karan Singhal Andrew Ng Jonathan H. Chen Daniel Yang, MD

Healthcare colleagues — some insight on a couple new healthcare AI benchmarks. In general, domain-specific benchmarks will be important for businesses as they move from general-purpose productivity models to task-specific models and agents.

We’re excited to announce the expansion of our AI-powered platform to a full vascular offering – including Aorta, Carotid, and Peripherals. Powered by deep learning, image-based real-world data, and one of the largest annotated clinical datasets in the field: 📊 350,000+ annotated imaging studies 🖼️ 70+ million annotated medical images 📚 20+ years of curated clinical data The platform enables comprehensive vascular management, including: ✔️ Auto-segmentation & multi-centerline ✔️ Automated measurements ✔️ Device modeling & virtual graft ✔️ Longitudinal tracking ✔️ Endoleak detection By combining advanced AI automation with decades of clinical expertise, we empower physicians to plan, treat, and monitor vascular disease with greater accuracy, efficiency, and confidence. #AIinMedicine #VascularCare #MedicalAI #HealthcareInnovation #AstuteImaging

Pleased to share an exciting milestone for Astute Imaging – the expansion of our AI-powered platform to a full vascular offering. In addition to the Aorta, we now support the Carotid and Peripheral vessels. This is another step toward transforming vascular care with AI, clinical insight, and decades of expertise — empowering physicians to deliver care with greater accuracy, efficiency, and confidence. #HealthcareInnovation #AIinMedicine #VascularCare #MedTech #AstuteImaging

Web-based. FDA-cleared. Secure. Happy to share that our AI vascular platform (Aorta | Carotid | Peripheral) puts planning, sizing, and follow-up in one browser—no installs. • Auto-segmentation & multi-centerline creation • Automated measurements • Device modeling & virtual graft • Longitudinal tracking with endoleak flags Why clinicians & partners trust it? • Trained/validated on thousands of annotated studies spanning diverse U.S. demographics & geographies. • Reviewed with—and used by—experts at leading U.S. health providers. Physicians: start a pilot. MedTech: integrate for trials & post-market follow-up. ▶️ Watch the demo below. #VascularSurgery #InterventionalRadiology #MedicalImaging #MedicalAI #DigitalHealth #MedTech #FDAcleared #WebBased #ClinicalAI #AstuteImaging

🔬 Revolutionizing Clinical Diagnostics with Specialized AI Models AI is rapidly transforming how diagnostics teams approach patient care. 🧠 Through specialized AI model development, healthcare organizations are achieving higher diagnostic accuracy and operational efficiency. 📊 AI-powered systems can now analyze medical images, enabling earlier disease detection and more informed clinical decisions. 💡 Machine learning models are helping radiology and data science teams process vast volumes of patient data, identify subtle patterns, and generate predictive diagnostic insights. ⚖️ As these technologies become more integrated into clinical workflows, it’s critical to address ethical considerations, data privacy, and system compatibility. 💬 How is your department preparing for the next wave of AI-driven diagnostics? Click here for more information! https://lnkd.in/gR-nNegP #HealthcareDiagnostics #AIinMedicine #MachineLearning #MedicalAIModels #DiagnosticAccuracy #RadiologyInnovation #ClinicalDataScience #SpecializedAIModelDevelopment Karthik Pichai Jared Redmer Sean Caputo Nafees Naim Sudarsana Rao Sunkara Milan Sahu Paresh Shinde Jordan Hayward