XponentL Data’s Post

Quantum Medical AI integrates quantum theory and advanced data analysis for enhanced disease diagnosis and treatment. Its diagnostic approach enables early detection, especially for complex neurological diseases and cancer. The system employs machine learning algorithms trained on diverse medical data, aiding clinicians in diagnosis using imaging techniques like x-rays and CT scans. However, ethical concerns arise, including the unintended learning of sensitive patient attributes, potentially leading to biased care. Moreover, the regulatory landscape for medical AI is evolving, with gaps in oversight allowing biased algorithms and increased patient discrimination if left unaddressed.

Deep learning algorithms have shown remarkable promise in detecting structural heart disease. A study published in JAHA — Journal of the American Heart Association reveals that Eko's FDA-cleared algorithms, trained on over 15,000 heart sound recordings, outperform clinicians. In real clinical environments, they demonstrate a sensitivity of 97.9% and specificity of 90.6% when detecting clearly audible murmurs in adults. Moreover, the algorithms can differentiate between systolic and diastolic murmurs, offering valuable timing insights within the cardiac cycle. This revolutionizes cardiac auscultation, addressing interobserver variability among clinicians and improving the detection of structural heart disease. Learn more: https://bit.ly/3RMx0M5 #Cardiology #MachineLearning

Excited to share my first AWS blog post that I co-authored with Ramakant Joshi, Jake W. and Dariush Azimi. Skin cancer is a serious and potentially deadly disease. Discover how AWS is revolutionizing the usage of artificial intelligence in Healthcare and the benefits associated with it. Check out our blog to explore the capabilities. #AWS #AWSBlog #machinelearning #healthcare #sagemakercanvas #ai #nocode https://lnkd.in/g8GM-3Zc

📚 Add this to your reading list 📚 "Domain Generalization in Deep Learning-Based Mass Detection in Mammography: A Large-Scale Multi-Center Study" Written by the amazing Coordinators of RadioVal, among others. #EUfunded #breastcancer #research #AI

Alzheimer’s disease (AD) is now classified as a silent pandemic. Non-invasive techniques and artificial intelligence are offering new hope for early and accurate AD diagnosis, revolutionizing the approach to this devastating disease. Artificial intelligence has played a pivotal role in leveraging non-invasive data, addressing challenges related to the computational analysis of large quantities of data, and significantly improving diagnostic accuracy in the clinical setting. This innovative use of AI not only enhances patient health outcomes but also paves the way for a new era of non-invasive medicine in early AD detection. #AlzheimersDisease #artificialintelligence Source:

I'll do even a braver poll than last, but only because of this Eric Topol, MD article in Science Magazine :) Multimodal large AI models will be used in medical forecasting (disease risk, onset, trajectory) A) now B) soon but at population level only C) sooner than in diagnostics D) later than in diagnostics E) never https://lnkd.in/gGBHSptv

🚀 Exciting news in the world of AI and healthcare! A new study introduces a groundbreaking approach using transfer learning to predict the survival and grade of glioblastoma with impressive accuracy. The results show 65% accuracy in survival prediction and 97% accuracy in tumor grade prediction, surpassing current methods. This innovative method holds promise for improving diagnostic and treatment approaches for glioblastoma patients. #AI #HealthTech #TransferLearning #GlioblastomaPrediction #MedicalAI 🧠

Two out of three physicians reported at least one symptom of burnout in a study published last year—an alarming number underscoring the growing physician shortage crisis. So, what can we do about it? Kristine Lee, MD at The Permanente Medical Group, Inc. explains how artificial intelligence can empower physicians, improve health care, and help address burnout. We can use augmented intelligence technology to place people, patients, communities, and clinicians at the core of AI implementation—from using tools that categorize and prioritize notes for physicians, to identifying high-risk screening concerns with analytics-driven programs. We’re already testing computer vision to better examine radiology scans and images. For example, AI can analyze mammograms and correctly identify high-risk concerns before cancer cells even develop. These algorithms, when paired with workflows, could increase the identification of patients who may be at risk for breast cancer from 20% using traditional approaches, to over 60%. AI has the power to help physicians spend less time on administrative duties and more time on what truly matters—seeing, treating, and healing patients. #ArtificialIntelligence #HealthCareInnovation

How can we use transformers to enhance domain generalisation of deep learning models in medical imaging? In our paper: 🔍 We delve into best practices for domain generalization, a critical hurdle in deploying AI for changing clinical environments. 🚀 🔬 We compare 8 state-of-the-art detection methods, including Transformer models, trained in a single domain and tested in 5 unseen ones. 💪 Our single-source transformer-based pipeline outperforms state-of-the-art transfer learning-based approaches in 4 out of 5 domains. 🤔 Curious about the covariate shifts impacting detection? Our in-depth analysis identifies the key factors in patient age, breast density, mass size and malignancy. 🔑 This study is more than results—it's a roadmap! 🚗💨 Dive into the insights and best practices we uncovered for future deep learning in breast cancer detection research. #AIHealthcare #BreastCancerDetection #DeepLearningInMedicine Read the full paper here: https://lnkd.in/dZCMc8qb RadioVal and EuCanImagne