Best AI tools for< Code Clinical Trials >

JADBio is an automated machine learning (AutoML) platform designed to accelerate biomarker discovery and drug development processes. It offers a no-code solution that automates the discovery of biomarkers and interprets their role based on research needs. JADBio can parse multi-omics data, including genomics, transcriptome, metagenome, proteome, metabolome, phenotype/clinical data, and images, enabling users to efficiently discover valuable insights. The platform is purpose-built for various conditions such as cancer, immune, endocrine, metabolic system, chronic diseases, aging, infectious diseases, and mental health, offering solutions for early biomarker discovery, drug repurposing, lead identification, compound optimization, trial monitoring, and response to treatment. JADBio is trusted by partners in precision health & medicine and is continuously evolving to disrupt drug discovery times and costs at all stages.

ICD AI is an advanced artificial intelligence tool designed to assist healthcare professionals in accurately assigning diagnostic codes to patient records. The tool utilizes machine learning algorithms to analyze medical data and suggest appropriate ICD codes, streamlining the coding process and reducing errors. With its user-friendly interface and robust features, ICD AI is revolutionizing medical coding practices and improving efficiency in healthcare facilities.

Rapid Claims AI is an autonomous medical coding and documentation solution powered by AI technology. It aims to streamline medical coding operations, reduce administrative costs, improve reimbursements, and ensure compliance for healthcare providers. The platform offers features like automated coding, personalized solutions, actionable insights, and customizable AI rule sets. Rapid Claims AI is designed to seamlessly integrate into existing workflows, catering to various healthcare setups and specialties. The application prioritizes security and privacy, with data encryption and secure cloud storage. It serves as a valuable tool for enhancing revenue cycle management processes in the healthcare industry.

Iodine Software is a healthcare technology company that provides AI-enabled solutions for revenue cycle management, clinical documentation integrity, and utilization management. The company's flagship product, AwareCDI, is a suite of solutions that addresses the root causes of mid-cycle revenue leakage from admission through post-billing review. AwareCDI uses Iodine's CognitiveML AI engine to spot what is missing in patient documentation based on clinical evidence. This enables healthcare organizations to maximize documentation integrity and revenue capture. Iodine Software also offers AwareUM, a continuous, intelligent prioritization solution for peak UM performance.

Suki Assistant is an enterprise-grade AI assistant designed to help clinicians save time by providing ambient documentation, dictation, ICD-10 and HCC coding, and answering questions in one solution. It offers deep EHR integrations with all major EHRs, ensuring safe AI practices, hassle-free partnership, proven ROI, and advanced EHR integrations. Suki is trusted by health systems across the country for its reliability, scalability, and convenience in clinical documentation.

Corti is an AI platform that provides advanced capabilities for patient consultations. It offers features such as Co-Pilot, a proactive AI scribe and assistant for clinicians, and Mission Control, an AI-powered conversation recorder. Corti's AI tools support various healthcare tasks, including high precision procedure and diagnosis coding, context-aware assistants for clinicians, and support for multiple languages in speech and text. The platform is trusted by major hospitals and healthcare providers worldwide.

Code & Pepper is an elite software development company specializing in FinTech and HealthTech. They combine human talent with AI tools to deliver efficient solutions. With a focus on specific technologies like React.js, Node.js, Angular, Ruby on Rails, and React Native, they offer custom software products and dedicated software engineers. Their unique talent identification methodology selects the top 1.6% of candidates for exceptional outcomes. Code & Pepper champions human-AI centaur teams, harmonizing creativity with AI precision for superior results.

Code Snippets AI is an AI-powered code snippets library for teams. It helps developers master their codebase with contextually-rich AI chats, integrated with a secure code snippets library. Developers can build new features, fix bugs, add comments, and understand their codebase with the help of Code Snippets AI. The tool is trusted by the best development teams and helps developers code smarter than ever. With Code Snippets AI, developers can leverage the power of a codebase aware assistant, helping them write clean, performance optimized code. They can also create documentation, refactor, debug and generate code with full codebase context. This helps developers spend more time creating code and less time debugging errors.

Code Generator for Arduino is an AI-powered tool that allows users to generate code for Arduino projects effortlessly. The website uses GPT-3.5-turbo, OpenAI's large-scale language-generation model, to create Arduino code. Users can easily create code for their Arduino devices by simply inputting their project requirements. The generated code must be reviewed before being uploaded to any hardware devices. Code Generator for Arduino is not affiliated with Arduino SA but provides a convenient solution for Arduino enthusiasts to streamline their coding process.

Code Companion AI is a desktop application powered by OpenAI's ChatGPT, designed to aid by performing a myriad of coding tasks. This application streamlines project management with its chatbot interface that can execute shell commands, generate code, handle database queries and review your existing code. Tasks are as simple as sending a message - you could request creation of a .gitignore file, or deploy an app on AWS, and CodeCompanion.AI does it for you. Simply download CodeCompanion.AI from the website to enjoy all features across various programming languages and platforms.

This tool uses AI to explain any piece of code you don't understand. Simply paste the code in the code editor and press "Explain Code" and AI will output a paragraph explaining what the code is doing.

AI Code Reviewer is a tool that uses artificial intelligence to review code. It can help you find bugs, improve code quality, and enforce coding standards.

Code Language Converter is an AI-powered tool that allows you to convert code from one programming language to another. Simply paste your code snippet into the converter and select the desired output language. The AI will then generate the converted code, which you can download or copy and paste into your project.Code Language Converter is a valuable tool for developers of all levels. It can save you time and effort by automating the code conversion process. Additionally, the converter can help you to learn new programming languages by providing you with a way to see how code is written in different languages.

AI Code Translator is an online tool that allows users to translate code or natural language into multiple programming languages. It is powered by artificial intelligence (AI) and provides intelligent and efficient code translation. With AI Code Translator, developers can save time and effort by quickly converting code between different languages, optimizing their development process.

The Robot Code Generator by Pantheon Robotics is a web application that allows users to generate executable robot code from natural language. The tool is designed to simplify the process of creating code for a generic robot based on a physical proof-of-concept, such as a car. By inputting instructions in plain language, users can quickly generate the necessary code for the robot to perform specific tasks. The application is powered by advanced AI technologies, including GPT-4 and Vercel AI SDK, to enhance code generation accuracy and efficiency.

No Code Camp is an online learning platform that teaches people how to use artificial intelligence (AI) and no-code tools to automate their work and build applications. The platform offers a live, 5-week cohort-based course that covers the essentials of no-code development, including data architecture, interface design, AI scaling, and no-code automation. The course is designed for people with no prior coding experience and is taught by experienced instructors who have built and scaled digital products using no-code tools.

No Code Camp is an AI tool that offers a live, 5-week cohort-based course to turn strategy and operations people into automation experts with AI and No Code. The platform enables non-technical individuals to build applications, automate workflows, and develop web platforms using graphical interfaces, AI, and tool configuration instead of writing code. No Code Camp democratizes software development, making it accessible to a broader audience, speeding up the development process, and reducing the reliance on specialized software development skills. The course covers essential topics such as Data Architecture, Interface Design, AI Scaling, and No Code Automation, equipping participants with the skills needed to automate business processes and build internal tools.

VBA Code Generator is an AI-powered tool that allows users to generate VBA code quickly and efficiently. By inputting requirements, users can instantly generate complex VBA code using simple text instructions with the help of AI. The tool is designed for both beginners and experienced users, offering a versatile application that can handle various VBA tasks, from Excel automation to Access database management. With a focus on saving time and streamlining workflows, VBA Code Generator simplifies the coding process and provides accurate formulas for users' specific needs.

Papers With Code is an AI tool that provides access to the latest research papers in the field of Machine Learning, along with corresponding code implementations. It offers a platform for researchers and enthusiasts to stay updated on state-of-the-art datasets, methods, and trends in the ML domain. Users can explore a wide range of topics such as language modeling, image generation, virtual try-on, and more through the collection of papers and code available on the website.

Master of Code Global is an AI development company that offers a wide range of AI, web, and mobile solutions to enhance customer experiences. They specialize in services such as chatbot development, conversational AI, generative AI development, web development, software product development, and more. With a focus on leveraging advanced technology to automate tasks, analyze data effectively, and personalize customer interactions, Master of Code Global aims to provide custom world-class digital experiences for web and mobile platforms empowered by AI.

Kaapana is an open-source toolkit for state-of-the-art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. Obtaining large amounts of medical data necessary for developing and training modern machine learning methods is an extremely challenging effort that often fails in a multi-center setting, e.g. due to technical, organizational and legal hurdles. A federated approach where the data remains under the authority of the individual institutions and is only processed on-site is, in contrast, a promising approach ideally suited to overcome these difficulties. Following this federated concept, the goal of Kaapana is to provide a framework and a set of tools for sharing data processing algorithms, for standardized workflow design and execution as well as for performing distributed method development. This will facilitate data analysis in a compliant way enabling researchers and clinicians to perform large-scale multi-center studies. By adhering to established standards and by adopting widely used open technologies for private cloud development and containerized data processing, Kaapana integrates seamlessly with the existing clinical IT infrastructure, such as the Picture Archiving and Communication System (PACS), and ensures modularity and easy extensibility.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

KG-RAG (Knowledge Graph-based Retrieval Augmented Generation) is a task agnostic framework that combines the explicit knowledge of a Knowledge Graph (KG) with the implicit knowledge of a Large Language Model (LLM). KG-RAG extracts "prompt-aware context" from a KG, which is defined as the minimal context sufficient enough to respond to the user prompt. This framework empowers a general-purpose LLM by incorporating an optimized domain-specific 'prompt-aware context' from a biomedical KG. KG-RAG is specifically designed for running prompts related to Diseases.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

This repository focuses on exploring tools that enhance the performance of language models for various tasks. It provides a structured list of literature relevant to tool-augmented language models, covering topics such as tool basics, tool use paradigm, scenarios, advanced methods, and evaluation. The repository includes papers, preprints, and books that discuss the use of tools in conjunction with language models for tasks like reasoning, question answering, mathematical calculations, accessing knowledge, interacting with the world, and handling non-textual modalities.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

This repository contains a collection of papers related to tool learning with large language models (LLMs). The papers are organized according to the survey paper 'Tool Learning with Large Language Models: A Survey'. The survey focuses on the benefits and implementation of tool learning with LLMs, covering aspects such as task planning, tool selection, tool calling, response generation, benchmarks, evaluation, challenges, and future directions in the field. It aims to provide a comprehensive understanding of tool learning with LLMs and inspire further exploration in this emerging area.

This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.

LangTest is a comprehensive evaluation library for custom LLM and NLP models. It aims to deliver safe and effective language models by providing tools to test model quality, augment training data, and support popular NLP frameworks. LangTest comes with benchmark datasets to challenge and enhance language models, ensuring peak performance in various linguistic tasks. The tool offers more than 60 distinct types of tests with just one line of code, covering aspects like robustness, bias, representation, fairness, and accuracy. It supports testing LLMS for question answering, toxicity, clinical tests, legal support, factuality, sycophancy, and summarization.

AI Hospital is a research repository focusing on the interactive evaluation and collaboration of Large Language Models (LLMs) as intern doctors for clinical diagnosis. The repository includes a simulation module tailored for various medical roles, introduces the Multi-View Medical Evaluation (MVME) Benchmark, provides dialog history documents of LLMs, replication instructions, performance evaluation, and guidance for creating intern doctor agents. The collaborative diagnosis with LLMs emphasizes dispute resolution. The study was authored by Zhihao Fan, Jialong Tang, Wei Chen, Siyuan Wang, Zhongyu Wei, Jun Xie, Fei Huang, and Jingren Zhou.

MOOSE 2.0 is a leaner, meaner, and stronger tool for 3D medical image segmentation. It is built on the principles of data-centric AI and offers a wide range of segmentation models for both clinical and preclinical settings. MOOSE 2.0 is also versatile, allowing users to use it as a command-line tool for batch processing or as a library package for individual processing in Python projects. With its improved speed, accuracy, and flexibility, MOOSE 2.0 is the go-to tool for segmentation tasks.

FuseMedML is a Python framework designed to accelerate machine learning-based discovery in the medical field by promoting code reuse. It provides a flexible design concept where data is stored in a nested dictionary, allowing easy handling of multi-modality information. The framework includes components for creating custom models, loss functions, metrics, and data processing operators. Additionally, FuseMedML offers 'batteries included' key components such as fuse.data for data processing, fuse.eval for model evaluation, and fuse.dl for reusable deep learning components. It supports PyTorch and PyTorch Lightning libraries and encourages the creation of domain extensions for specific medical domains.

Grand Challenge is a platform that provides access to large amounts of annotated training data, objective comparisons of state-of-the-art machine learning solutions, and clinical validation using real-world data. It assists researchers, data scientists, and clinicians in collaborating to develop robust machine learning solutions to problems in biomedical imaging.

Cyclops is a toolkit for facilitating research and deployment of ML models for healthcare. It provides a few high-level APIs namely: data - Create datasets for training, inference and evaluation. We use the popular 🤗 datasets to efficiently load and slice different modalities of data models - Use common model implementations using scikit-learn and PyTorch tasks - Use common ML task formulations such as binary classification or multi-label classification on tabular, time-series and image data evaluate - Evaluate models on clinical prediction tasks monitor - Detect dataset shift relevant for clinical use cases report - Create model report cards for clinical ML models

This repository serves as a comprehensive survey on the application of Large Language Models (LLMs) on tabular data, focusing on tasks such as prediction, data generation, and table understanding. It aims to consolidate recent progress in this field by summarizing key techniques, metrics, datasets, models, and optimization approaches. The survey identifies strengths, limitations, unexplored territories, and gaps in the existing literature, providing insights for future research directions. It also offers code and dataset references to empower readers with the necessary tools and knowledge to address challenges in this rapidly evolving domain.

Expert in healthcare data analysis, coding, and clinical trials.

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