Systematic, AI-powered, Medical Scientific Article Review Assistant
🔹 Take your first step toward crafting evidence-based meta-analyses, systematic reviews, and new research directions in health sciences.
🔹 Let AI filter and extract relevant research based on your input, providing new directions to build upon.
DApp designed for easy participation in our ecosystem. Connect your Web3 wallet or create a new one to manage your $MLHS balance.
🔹 BNB Network ensures fast transactions
🔹 Reown AppKit enables smooth wallet connection
🔹 1inch Aggregator provides fair and optimized fees
Hybrid AI built on a Tensorflow.js and empowered by Gemini and Deepseek. 🔍 What does it do?
🔹 Individual risk estimation for developing varicose veins
🔹 Supports patients and healthcare professionals with the latest scientific knowledge on diagnosis, treatment, and prevention of the disease
Science 3.0 is a DApp and the utility token of the Web3 Journal: ML in Health Science, designed to preserve published articles and valuable health science research data on the opBNB blockchain using the SCI3 Token . 🔑 Use your author or reviewer login credentials for the 📔Web3 Journal: ML in Health Science to access the system for free.
This web application is a poll associated with the corresponding editorial, "Artificial Intelligence in Academic Writing: Time for Science 3.0," published in Web3: Journal of ML in Health Science. Participate in the poll and share your thoughts in the article's comments section.
Computer vision utilizes numerical color values in the RGB scale for image preprocessing, resulting in 255 x 255 x 255 possible color variants for each pixel. This complexity can make the analysis of high-resolution images challenging. Dividing images into smaller patches helps balance computational resources. This tool demonstrates how visual content is transformed into numerical values for each patch and the entire image.
This experimental tool uses Markov Chains and TensorFlow.js Universal Sentence Encoder to generate new text on the client side based on input information, without relying on large language models.
The computer vision model provides the ability to predict the probability of lower limb salvage or loss in peripheral artery disease patients using pedal angiograms (DSA) in sagittal view.
Open-source AI chat powered by the Gemma2b. It runs on your device, ensuring full privacy - crucial in healthcare settings!
🔹 Tasks: Text generation, context extraction, and conversation, including chat with PDF documents.
⚠️CAVE: Mobile devices are currently not supported! During the first start, Gemma2b will be loaded, please be patient. Subsequent starts will be much faster.
Learn the principles of machine learning through gameplay. The Snake is guided by a reinforcement learning using neural network predictions, with training data generated from auto gameplay. Experiment with parameters and observe the performance. If you would like to see how the ML works, right-click, select "Inspect," open the Console of your desktop browser, and enjoy the heartbeat of the machine.
🗃️️️ In the list of deployed WebAI applications you can find model trained and deployed in the Web AI Playground. Each model has an individual link to use and share. This list is moderated. If you find an invalid model, please contact us.
Create your own web applications using the AI model trained in the Playground. Simply upload the model, add a cover image, and press the "Submit" button. After submission, you can add a description, your custom data, and your Web3 wallet if you wish to receive donations and monetize your work.
Beta-Test. Control the position of the circle indicator with your voice.
Build custom machine learning regression and classification web applications from numerical data using feedforward neural networks powered by TensorFlow.js. After training, the model can be saved for deployment, sharing, or retraining with new data. All computations are performed locally on the user’s device, ensuring full data privacy and security.
Build custom computer vision web applications from images utilizing transfer learning with the MobileNetV2 layer model and the TensorFlow.js library. The model can be stored after training for deployment, sharing, and retraining with new data. All calculations are performed on the user side and are not transferred elsewhere, ensuring data privacy and safety.
This web app overlays GLOBOCAN cancer incidence data with NASA's Earth emissivity maps to explore the potential association between chronic infrared exposure from Earth's emissivity and overall cancer incidence rates. For more information, please refer to the accompanying article.
This Web AI app is a computer vision tool for classifying femoropopliteal angiograms based on the FP GLASS classification, as detailed in the accompanying article. For comprehensive guidance, please refer to the research article.
Immersive content offers unique and engaging experiences by creating a deeper sense of presence and interaction. Upload an image, press the 'Transform to 3D' button, and explore how 2D objects are transformed into immersive 3D visuals.
Proper search strategy and systematic literature review are essential for successful and accurate information extraction, with keywords playing a crucial role in this process. Enter any text, press the "Generate Keywords" button, and observe how key terms are extracted and prioritized - demonstrating how machines assist in keyword extraction.
This web application is a poll associated with the corresponding article, "Confounders in Predictive Medical Models: The Role of Religion," published in Web3 Journal: of ML in Health Science. Participate in the poll and share your thoughts in the article's comments section.
This web application is a poll associated with the corresponding article, "Confounders in Predictive Medical Models: Roles of Nationality and Immigrant Status," published in Web3 Journal: of ML in Health Science. Participate in the poll and share your thoughts in the article's comments section.
This web app calculates the complexity score of CME online courses to evaluate their resistance to AI agents. For more detailed information, please refer to the accompanying article.
This web application is a poll associated with the corresponding article, "Do You Consent to the Use of Your Biological Data for Training ML and AI Models? Online Survey Targeting Clinicians and Researchers" published in Web3 Journal: of ML in Health Science. Participate in the poll and share your thoughts in the article's comments section.
This web application is a poll associated with the corresponding article, "Do You Agree with AI Making Decisions About Your Treatment? A Comparative Survey of IT and Healthcare Practitioners," published in Web3 Journal: of ML in Health Science. Participate in the poll and share your thoughts in the article's comments section.
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