Iterative Health的动态

?? Results & Analysis of Our COVID-19 Detection System ?? Excited to share the latest results from our project! Our CNN model achieved a training accuracy of 74.8% and a validation accuracy of 72.5%, with promising generalization. Highlights: High Precision and Recall: Excellent at accurately identifying COVID-19 cases. Model Comparison: Our CNN-based approach showed better accuracy than SVM and other traditional models. This project highlights how machine learning can aid healthcare, especially in rapid diagnostics. Kudos to my teammates Pranshu Verma, Mandeep singh Oberoi, and Raghav Sharma for all the hard work! Next up: Future enhancements and deployment possibilities! #COVIDDetection #MachineLearningResults #HealthcareAI #DeepLearning #MedicalImaging #AIForGood #Innovation #TechForGood #HealthTech

Curious about how AI models perform in healthcare? ?? Check out the Open Medical-LLM Leaderboard which offers valuable insights into how large language models perform across various medical tasks. By providing a public benchmark, it helps researchers & engineers identify which models are most effective in areas like medical Q&A, clinical decision support, and disease diagnosis. This is crucial for the healthcare industry as it drives innovation, ensuring AI models are both accurate and reliable for real-world applications. Ultimately, this tool aids in advancing personalized medicine and improving patient outcomes, reflecting our vision for the future. https://lnkd.in/gZ5WNdVg #HealthcareAI #MedicalAI #PersonalizedMedicine #AIInnovation #ClinicalDecisionSupport #DiseaseDiagnosis #AIBenchmarks #FutureOfHealthcare

Discover how AI is revolutionizing healthcare with Charbel Makhoul at the AI IXX event. Watch our latest video to explore Charbel's insights on AI's role in enhancing medical diagnostics, improving image quality, and accelerating patient care. Learn how AI supports doctors and technicians without replacing them, and see how it manages the growing data from medical diagnostics to improve treatment outcomes. Watch now to understand the future of AI in healthcare: https://lnkd.in/dsKVX8dv Perfect for professionals interested in the nexus of technology and medical science. #aiixx #ai #artificialintelligence #technology #innovation #medicialtechnology #transformation

AI-Enabled Segmentation. Save time and improve management over case inflow and your team’s expertise. Swiftly segment and label images for precise 3D models with AI-enabled algorithms. You’ll save time, enabling confident management of clinical case inflows without compromising quality. Materialise

AI in healthcare uses machine learning algorithms to analyze complex medical data, improve diagnostics, and personalize patient care, revolutionizing the industry. #AIinHealthcare #MedicalAI #HealthTech #medtechreviewmagazine

Road map for training a CT dataset for creating an AI software. This is what i figured out " a tear down skeletal framework" Don't know if this the right way. Any experts to comment on this ? Example Roadmap: 1. Collect Data: Public/clinical 2. Preprocess Data: Resize /crop/normalization /data augmentation by MONAI 3. Annotate Images: Use #ITK-SNAP /#MONAI 3. Choose Model: Select a #CNN architecture like #ResNet. 4. Train Model: Use #TensorFlow to train the model on 800 labeled images, validate on 100, and test on 100. 5. Evaluate Model: Check the model's accuracy and precision on the test set. 6. Text Analysis with Radiology- #Llama2: Generate and analyze radiology reports. Pranav Rajpurkar Qure.ai Biocliq AI Google Health Google for Developers MONAI Microsoft for Healthcare #MedicalImaging #AIinRadiology #RadiologyAI #HealthTech #AIinHealthcare #RadiologyInnovation #HealthcareInnovation #MedicalDeepLearning

I’m thrilled to share our latest research on predictive analytics in healthcare! ???? This study introduces a predictive framework for ICU length of stay (LOS) using hospital EHR data and machine learning models. Utilizing explainable AI (xAI), it achieves 98% AUC with XGBoost, enhancing hospital efficiency and patient outcomes by providing trustworthy LOS predictions and explaining model outputs. Belal Alsinglawi Omar Mubin #HealthcareInnovation #MachineLearning #PredictiveAnalytics #AIHospitalManagement #UAEU

It's no secret that funding is a major driver in most research endeavors. But what does that mean for the truth? As we enter the age of machine learning, we're beginning to uncover some uncomfortable truths about the biases that exist in just about every area of study. Check out this video to learn more: https://lnkd.in/g3VySuJx #MachineLearning #Research #Biases #Truths

Weekend AI: microsoft/Florence-2-large (arXiv:2311.06242) a foundation vision model. It offers advanced capabilities of Object detection, Masking and Segmentation using simple text prompts. Crime scene analysis, Medical pathology, Vision impaired assistance etc. are all solid uses cases where this tech can be used. Also, running this on WebGPU. This magic is possible because of Transformers.js (https://lnkd.in/gMX6A3vj) that is a javascript port of the transformers from huggingface. I am able to run the model locally on my web browser! Of course the model is heavily lobotomised, but still it works nice!

How does AiCure reduce the burden on participants and improve their #ClinicalTrial experience? The key lies in our ability to remotely assess how participants take their medication using: ?? Their smartphone ??? Computer vision ? AI Through AiCure, you're able to reduce the risk of missing data, detect if a participant is purposefully not taking their medications and train participants to successfully and safely dose. Learn how to enhance your clinical trials with AiCure here ?? https://lnkd.in/gayE-xBF