Best AI tools for< clinical data manager >

DrugCard is an AI-enabled Data Intelligence platform designed to streamline drug safety routines for pharmacovigilance professionals. It offers solutions for local literature screening, providing continuous, transparent, and accurate monitoring of medical journals in multiple languages and regions. DrugCard helps pharmaceutical companies, Contract Research Organizations (CROs), Marketing Authorization Holders (MAHs), and freelancers improve screening efficiency, compliance, and overall productivity. The platform leverages AI technology to save time, reduce costs, and enhance pharmacovigilance processes.

Oncora Medical is a healthcare technology company that provides software and data solutions to oncologists and cancer centers. Their products are designed to improve patient care, reduce clinician burnout, and accelerate clinical discoveries. Oncora's flagship product, Oncora Patient Care, is a modern, intelligent user interface for oncologists that simplifies workflow, reduces documentation burden, and optimizes treatment decision making. Oncora Analytics is an adaptive visual and backend software platform for regulatory-grade real world data analytics. Oncora Registry is a platform to capture and report quality data, treatment data, and outcomes data in the oncology space.

Consensus is a healthcare interoperability platform that simplifies data exchange and document processing through artificial intelligence technologies. It offers solutions for clinical documentation, HIPAA compliance, natural language processing, and robotic process automation. Consensus enables secure and efficient data exchange among healthcare providers, insurers, and other stakeholders, improving care coordination and operational efficiency.

AKASA is a healthcare revenue cycle management company that uses generative AI (GenAI) to improve the revenue cycle for healthcare providers. AKASA's GenAI-powered platform and solutions help healthcare providers reduce denials, improve margins, and increase revenue. AKASA's platform includes solutions for authorization management, claim status, and claim attachment. AKASA's GenAI is trained on clinical and financial data and is optimized for healthcare. AKASA's clients include over 475 hospitals and 6,500 outpatient facilities across all 50 states.

AiCure provides a patient-centric eClinical trial management platform that enhances drug development through improved medication adherence rates, more powerful analysis and prediction of treatment response using digital biomarkers, and reduced clinical tech burden. AiCure's solutions support traditional, decentralized, or hybrid trials and offer flexibility to meet the needs of various research designs.

Intelligencia AI is a leading provider of AI-powered solutions for the pharmaceutical industry. Our suite of solutions helps de-risk and enhance clinical development and decision-making. We use a combination of data, AI, and machine learning to provide insights into the probability of success for drugs across multiple therapeutic areas. Our solutions are used by many of the top global pharmaceutical companies to improve their R&D productivity and make more informed decisions.

ClosedLoop is a healthcare data science platform that helps organizations improve outcomes and reduce unnecessary costs with accurate, explainable, and actionable predictions of individual-level health risks. The platform provides a comprehensive library of easily modifiable templates for healthcare-specific predictive models, machine learning (ML) features, queries, and data transformation, which accelerates time to value. ClosedLoop's AI/ML platform is designed exclusively for the data science needs of modern healthcare organizations and helps deliver measurable clinical and financial impact.

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.

HealthITAnalytics is a leading source of news, insights, and analysis on the use of information technology in healthcare. The website covers a wide range of topics, including artificial intelligence, machine learning, data analytics, and population health management. HealthITAnalytics also provides resources for healthcare professionals, such as white papers, webinars, and podcasts.

Iterative Health is a company that is dedicated to providing world-class GI care and treatment to patients around the world. They are on a mission to bring world-class care and treatment to patients around the world. Advances in machine learning and artificial intelligence are helping them create a complete GI ecosystem with the power to turn this vision into reality.

Covera Health is a clinical intelligence platform that supports the end-to-end delivery of clinical-grade, AI-powered quality insights for providers and insurers. The platform is seamlessly integrated across the healthcare ecosystem to elevate everything from diagnosis and care coordination to prior authorization and claims administration. Covera Health is certified by AHRQ as a Patient Safety Organization to safeguard access to provider and patient data.

Yseop offers Natural Language Generation (NLG) services that automate and translate data into actionable language, simplifying complex workflows. Its AI-based technologies generate core elements of specialist medical reports, including clinical study reports (CSR), patient narratives, and more. Yseop also automates the writing of financial reports, removing the risk of error in manual writing to ensure accuracy, consistency, and compliance. Additionally, Yseop provides bespoke NLG applications tailored to specific needs, helping streamline operations and empower workers with tailored information and insights.

Enlitic provides healthcare data solutions that leverage artificial intelligence to improve data management, clinical workflows, and create a foundation for real-world evidence medical image databases. Their products, ENDEX and ENCOG, utilize computer vision and natural language processing to standardize, protect, and analyze medical imaging data, enabling healthcare providers to optimize workflows, increase efficiencies, and expand capacity.

Apixio is a healthcare AI company that provides solutions for health plans, providers, and ACOs. Their AI-powered platform helps organizations improve administrative, clinical, and financial outcomes. Apixio's solutions include risk adjustment, payment integrity, health data management, and AI-as-a-service.

Cosign AI is an AI application that optimizes clinical practices by automating clinical documentation through an ambient scribe. The tool transforms conversations and dictations into clinical notes using large language models and customizable templates. It prioritizes HIPAA compliance and data security, ensuring a secure infrastructure for storing and processing protected health information. Clinicians can save time, reduce burnout, and improve note quality with this innovative solution.

Navina AI is a clinician-first AI platform designed to streamline patient care by providing key insights and actionable recommendations to healthcare providers, ACOs, MSOs, and health plans. The platform leverages proprietary AI technology to improve clinical decision-making, reduce administrative burden, and enhance quality management and risk adjustment processes. Navina AI offers efficient chart review, accurate risk adjustment, streamlined quality management, robust analytics, and a user-friendly interface that integrates seamlessly into the clinical workflow.

Woebot Health is an AI-driven enterprise solution for mental health that aims to increase access to mental health support and improve business outcomes. The platform offers a digital companion, Woebot, which provides in-the-moment mental health support through interactive conversations and tools based on elements from Cognitive Behavioral Therapy (CBT), Interpersonal Psychotherapy (IPT), and Dialectical Behavioral Therapy (DBT). Woebot Health also includes a data analytics platform, Population Insights Spotlight, to aggregate insights for clinicians and improve care quality. The application is designed to help individuals develop coping skills for symptoms of anxiety and depression, with a focus on emotional regulation, mood monitoring, and healthy lifestyle choices.

Biofourmis is a healthcare technology company that provides a connected technology platform for care delivery and drug development. The platform enables healthcare systems and pharmaceutical companies to deliver care and conduct clinical trials remotely, and to collect and analyze data to improve patient outcomes. Biofourmis's solutions have been shown to improve clinical, operational, and economic outcomes, such as reducing 30-day readmissions by 70%, detecting deterioration 21 hours sooner, and reducing cost of care by up to 38%.

Recursion is a techbio company that uses artificial intelligence to accelerate drug discovery. The company's platform combines hardware, software, and data to create a more efficient and effective drug discovery process. Recursion has a broad pipeline of drug candidates in development, and it has partnered with several leading pharmaceutical companies. The company is headquartered in Salt Lake City, Utah.

Upheal is an AI-powered platform designed to assist mental health professionals with progress notes, treatment plans, session analytics, and scheduling. It leverages AI technology to automate note-taking, provide insights into client sessions, and streamline clinical workflows, allowing therapists to focus more on their clients and less on administrative tasks.

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.

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.

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.

ABigSurveyOfLLMs is a repository that compiles surveys on Large Language Models (LLMs) to provide a comprehensive overview of the field. It includes surveys on various aspects of LLMs such as transformers, alignment, prompt learning, data management, evaluation, societal issues, safety, misinformation, attributes of LLMs, efficient LLMs, learning methods for LLMs, multimodal LLMs, knowledge-based LLMs, extension of LLMs, LLMs applications, and more. The repository aims to help individuals quickly understand the advancements and challenges in the field of LLMs through a collection of recent surveys and research papers.

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.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

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 includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.

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.

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

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 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.

Nuitrack™ is an ultimate 3D body tracking solution developed by 3DiVi Inc. It enables body motion analytics applications for virtually any widespread depth sensors and hardware platforms, supporting a wide range of applications from real-time gesture recognition on embedded platforms to large-scale multisensor analytical systems. Nuitrack provides highly-sophisticated 3D skeletal tracking, basic facial analysis, hand tracking, and gesture recognition APIs for UI control. It offers two skeletal tracking engines: classical for embedded hardware and AI for complex poses, providing a human-centric spatial understanding tool for natural and intelligent user engagement.

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

The repository 'LLM-for-Healthcare' provides a comprehensive survey of large language models (LLMs) for healthcare, covering data, technology, applications, and accountability and ethics. It includes information on various LLM models, training data, evaluation methods, and computation costs. The repository also discusses tasks such as NER, text classification, question answering, dialogue systems, and generation of medical reports from images in the healthcare domain.

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.

Clinical trial assistant answering questions about clinical trials.

HL7-FHIR® expert for developers and healthcare data managers

Comprehensive Guide in Clinical Trials & Regulations

Find active recruiting oncology clinical trials near you.

Answer anything about SELECT trial

Especialista em dados de pós-graduação em Fisiologia

Aids in informatics for nurses and midwives.

Explores emerging trends and innovations in medicine.

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

高血压临床决策支持

医学统计领域的专家，提供详细分析性回应。

Le GPT custom de Sclépios IA décrypte les termes médicaux pour les soignants, facilitant la compréhension des cas cliniques. Découvrez plus sur sclepios-mobile.com.

GPT for AI in Medicine

Expert in oncology trial matching, leveraging advanced GPT-4 Turbo techniques.

Accurate and timely matching of patients with clinical trials

Direct, insightful EEG and neurofeedback analysis specialist.

Expert in disease cures, medical research, and data analysis.

Medical engineer specializing in HL7/FHIR data conversion.

Expert in biostatistics, psychometrics, and general statistical analysis.