Best AI tools for< Healthcare Informaticist >

InformAI is a technology company dedicated to advancing healthcare through the development of AI-driven solutions. We focus on crafting high-quality, AI-driven enterprise software solutions that address specific clinical needs. We target critical medical fields including radiology, radiation oncology, and high acuity informatics. Our portfolio, featuring our flagship RadOncAI and our development pipeline products of SinusAI and TransplantAI, is designed to deliver impactful solutions that enhance healthcare quality, safety and efficiency.

DeepScribe is an AI medical scribe application that leverages advanced speech recognition technology to capture clinical conversations with extreme accuracy. It empowers clinicians and health systems with real-time AI insights, offers customization options to match provider workflows, and ensures trust and safety through features that promote AI transparency. DeepScribe is designed to save time, improve accuracy, drive adoption, and maximize revenue for doctors, hospitals, and health systems. The application is built for enterprise use, allowing users to unlock the power of AI and modernize their patient data strategy.

Healthcare IT News is an AI-powered platform that provides the latest news and updates in the healthcare IT industry. The platform covers a wide range of topics including video analytics, artificial intelligence, cloud computing, EHR, government & policy, interoperability, patient engagement, population health, precision medicine, privacy & security, and telehealth. It offers insights, articles, and special projects related to AI, ML intelligence, cybersecurity, and more. Healthcare IT News aims to keep healthcare professionals informed about the latest trends and developments in the field.

Health Imaging is an AI-powered platform that focuses on providing cutting-edge solutions in medical imaging and healthcare management. The platform offers a wide range of features and tools that leverage artificial intelligence to enhance diagnostic accuracy, streamline workflows, and improve patient care. From advanced imaging technologies to AI-based training solutions, Health Imaging is at the forefront of innovation in the healthcare industry.

Health AI Partnership (HAIP) is an AI tool designed to empower healthcare professionals to effectively, safely, and equitably use AI through community-informed up-to-date standards. The platform offers resources, publications, events, and a practice network to advance the use of AI in healthcare and support professionals in implementing AI solutions.

Decode Health is an AI and analytics platform that accelerates precision healthcare by supporting healthcare teams in launching machine learning and advanced analytics projects. The platform collaborates with pharmaceutical companies to enhance patient selection, biomarker identification, diagnostics development, data asset creation, and analysis. Decode Health offers modules for biomarker discovery, patient recruitment, next-generation sequencing, data analysis, and clinical decision support. The platform aims to provide fast, accurate, and actionable insights for acute and chronic disease management. Decode Health's custom-built modules are designed to work together to solve complex data problems efficiently.

Suki Assistant is an enterprise-grade AI assistant designed for clinicians in the healthcare industry. It offers ambient documentation, dictation, ICD-10 and HCC coding, and question answering capabilities. Suki ensures content is clinician-reviewed before being sent to the EHR, minimizing the risk of errors. With deep integrations with major EHR systems, Suki sets the standard for EHR integrations in the healthcare sector. The application aims to save clinicians time, allowing them to focus more on patient care.

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.

Oatmeal Health is an AI-powered platform that specializes in cancer screening and clinical trials. The platform seamlessly integrates cancer screenings into Community Health Centers, utilizing AI risk assessments, care navigation, and advanced diagnostic screenings to support patients and track outcomes. Oatmeal Health aims to improve patient health, lower the overall cost of cancer care, and generate revenue for healthcare centers through evidence-based care and value-based initiatives.

Mendel AI is an advanced clinical AI tool that deciphers clinical data with clinician-like logic. It offers a fully integrated suite of clinical-specific data processing products, combining OCR, de-identification, and clinical reasoning to interpret medical records. Users can ask questions in plain English and receive accurate answers from health records in seconds. Mendel's technology goes beyond traditional AI by understanding patient-level data and ensuring consistency and explainability of results in healthcare.

Medlabreport.com is an AI-powered platform that helps users understand their medical exam results easily. By uploading a file, users receive a comprehensive report within 5 minutes, focusing on personalized insights based on symptoms, age, and other factors. The platform's advanced AI analyzes symptoms, provides recommendations, and prioritizes focus areas. While the reports are not a substitute for licensed medical diagnosis, they offer a fast second opinion and complementary perspective to traditional healthcare. Users can take control of their health by accessing easy-to-understand reports that may help them identify potential health issues sooner.

TORTUS is an AI application designed for doctors and clinicians, offering an AI interface for Electronic Health Records (EHR). It aims to provide faster, better, and kinder care by automating tasks such as record transcription, note generation, and clinical coding. The application is fully compliant with healthcare regulations and prioritizes data security. O.S.L.E.R. is positioned as a digital worker for digital work, enhancing patient outcomes and relieving clinician burnout through clinician-AI co-working.

Oncora Medical is an AI-powered platform that revolutionizes oncology care management. The platform offers a Clinical Data Platform for automating document generation and enhancing registry informatics. It provides AI-enabled case findings, revenue cycle management solutions, and cancer registry automation. Oncora's comprehensive AI solutions streamline workflows, improve outcomes, and capture revenue through intelligent automation. Trusted by top healthcare organizations worldwide, Oncora empowers healthcare organizations to achieve remarkable results in oncology care.

Radiology Business is an AI tool designed to provide insights and solutions for professionals in the radiology field. The platform covers a wide range of topics including management, imaging, technology, and conferences. It offers news, analysis, and resources to help radiologists stay informed and make informed decisions. Radiology Business aims to leverage artificial intelligence to improve workflow efficiency and enhance the overall experience in the radiology ecosystem.

JMIR AI is a new peer-reviewed journal focused on research and applications for the health artificial intelligence (AI) community. It includes contemporary developments as well as historical examples, with an emphasis on sound methodological evaluations of AI techniques and authoritative analyses. It is intended to be the main source of reliable information for health informatics professionals to learn about how AI techniques can be applied and evaluated.

The website is an AI tool focused on providing news, insights, and updates on the application of artificial intelligence in the healthcare industry. It covers a wide range of topics such as digital transformation, care delivery, and AI regulations. The platform aims to educate and inform healthcare professionals, industry watchers, and stakeholders about the latest trends, challenges, and opportunities in leveraging AI for improving patient care and healthcare operations.

The Medical Futurist is a digital health and AI-focused media platform that provides insights, research, and educational resources to healthcare professionals and industry leaders. It covers topics such as artificial intelligence in medicine, the future of pharma, and emerging trends in digital health. The platform also offers keynote speeches, courses, and e-books on these topics.

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.

ZeOmega is an AI-powered healthcare solutions platform that offers population health analytics, care management, benefits administration, and operational efficiency. It provides a comprehensive set of practical, AI-powered solutions for various healthcare stakeholders, including health plans, health systems, Medicare, Medicaid, and ACOs. ZeOmega's platform integrates data to create a single source of truth, drives intelligent automation, and optimizes quality of care through AI-powered insights and automated workflows. The platform is designed to streamline operations, improve efficiency, and enhance the overall quality of healthcare services.

GenHealth.ai is a cutting-edge AI application focused on generative healthcare AI. The platform offers innovative solutions for healthcare organizations by leveraging a Large Medical Model (LMM) trained on vast amounts of patient data. GenHealth.ai provides automated prior authorization, healthcare analytics, and predictive modeling to help healthcare leaders make informed decisions and optimize patient care. The application stands out for its ability to simulate patient futures, predict healthcare costs, and integrate with healthcare standards like FHIR, HL7, and X12.

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.

Taiyi (太一) is a bilingual large language model fine-tuned for diverse biomedical tasks. It aims to facilitate communication between healthcare professionals and patients, provide medical information, and assist in diagnosis, biomedical knowledge discovery, drug development, and personalized healthcare solutions. The model is based on the Qwen-7B-base model and has been fine-tuned using rich bilingual instruction data. It covers tasks such as question answering, biomedical dialogue, medical report generation, biomedical information extraction, machine translation, title generation, text classification, and text semantic similarity. The project also provides standardized data formats, model training details, model inference guidelines, and overall performance metrics across various BioNLP tasks.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

LLMs4TS is a repository focused on the application of cutting-edge AI technologies for time-series analysis. It covers advanced topics such as self-supervised learning, Graph Neural Networks for Time Series, Large Language Models for Time Series, Diffusion models, Mixture-of-Experts architectures, and Mamba models. The resources in this repository span various domains like healthcare, finance, and traffic, offering tutorials, courses, and workshops from prestigious conferences. Whether you're a professional, data scientist, or researcher, the tools and techniques in this repository can enhance your time-series data analysis capabilities.

Awesome-TimeSeries-SpatioTemporal-LM-LLM is a curated list of Large (Language) Models and Foundation Models for Temporal Data, including Time Series, Spatio-temporal, and Event Data. The repository aims to summarize recent advances in Large Models and Foundation Models for Time Series and Spatio-Temporal Data with resources such as papers, code, and data. It covers various applications like General Time Series Analysis, Transportation, Finance, Healthcare, Event Analysis, Climate, Video Data, and more. The repository also includes related resources, surveys, and papers on Large Language Models, Foundation Models, and their applications in AIOps.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

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.

Me LLaMA introduces a suite of open-source medical Large Language Models (LLMs), including Me LLaMA 13B/70B and their chat-enhanced versions. Developed through innovative continual pre-training and instruction tuning, these models leverage a vast medical corpus comprising PubMed papers, medical guidelines, and general domain data. Me LLaMA sets new benchmarks on medical reasoning tasks, making it a significant asset for medical NLP applications and research. The models are intended for computational linguistics and medical research, not for clinical decision-making without validation and regulatory approval.

This repository is dedicated to summarizing papers related to large language models with the field of law. It includes applications of large language models in legal tasks, legal agents, legal problems of large language models, data resources for large language models in law, law LLMs, and evaluation of large language models in the legal domain.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

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.

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.

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.

This repository, Awesome-LLM-Survey, serves as a comprehensive collection of surveys related to Large Language Models (LLM). It covers various aspects of LLM, including instruction tuning, human alignment, LLM agents, hallucination, multi-modal capabilities, and more. Researchers are encouraged to contribute by updating information on their papers to benefit the LLM survey community.

This tool is designed to detect and classify Alzheimer's Disease using Deep Learning and Machine Learning algorithms on an early basis, which is further optimized using the Crow Search Algorithm (CSA). Alzheimer's is a fatal disease, and early detection is crucial for patients to predetermine their condition and prevent its progression. By analyzing MRI scanned images using Artificial Intelligence technology, this tool can classify patients who may or may not develop AD in the future. The CSA algorithm, combined with ML algorithms, has proven to be the most effective approach for this purpose.

HuatuoGPT2 is an innovative domain-adapted medical large language model that excels in medical knowledge and dialogue proficiency. It showcases state-of-the-art performance in various medical benchmarks, surpassing GPT-4 in expert evaluations and fresh medical licensing exams. The open-source release includes HuatuoGPT2 models in 7B, 13B, and 34B versions, training code for one-stage adaptation, partial pre-training and fine-tuning instructions, and evaluation methods for medical response capabilities and professional pharmacist exams. The tool aims to enhance LLM capabilities in the Chinese medical field through open-source principles.

HuatuoGPT-o1 is a medical language model designed for advanced medical reasoning. It can identify mistakes, explore alternative strategies, and refine answers. The model leverages verifiable medical problems and a specialized medical verifier to guide complex reasoning trajectories and enhance reasoning through reinforcement learning. The repository provides access to models, data, and code for HuatuoGPT-o1, allowing users to deploy the model for medical reasoning tasks.

Implementation of the general framework for AMIE, from the paper Towards Conversational Diagnostic AI, out of Google Deepmind. This repository provides a Pytorch implementation of the AMIE framework, aimed at enabling conversational diagnostic AI. It is a work in progress and welcomes collaboration from individuals with a background in deep learning and an interest in medical applications.

The AWS Healthcare and Life Sciences AI/ML Immersion Day workshops provide hands-on experience for customers to learn about AI/ML services, gain a deep understanding of AWS AI/ML services, and understand best practices for using AI/ML in the context of HCLS applications. The workshops cater to individuals at all levels, from machine learning experts to developers and managers, and cover topics such as training, testing, MLOps, deployment practices, and software development life cycle in the context of AI/ML. The repository contains notebooks that can be used in AWS Instructure-Led Labs or self-paced labs, offering a comprehensive learning experience for integrating AI/ML into applications.

HL7-FHIR® expert for developers and healthcare data managers

Medical engineer specializing in HL7/FHIR data conversion.

Aids in informatics for nurses and midwives.

Provides info on Kerala's healthcare facilities.

Insights on AI in healthcare and wellness

高血压临床决策支持

Guide to healthcare services in India.

Leading in Smart Healthcare AI, Powered by OpenAI

A navigator for finding healthcare services and scheduling appointments.

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

I can help you with healthcare UX design

Healthcare futurist discussing trends, innovations, and predictions, inspired by space, resilience, and precision medicine.

Expert in healthcare claim appeals

Healthcare Coordination AI Assistant

A guide on healthcare legal planning

Simply type "news" for easy to digest updates in the Healthcare industry, or be as specific as you want and get the top healthcare news from reliable sources. Healthtech, patient care, insurance policies, provider networks, value based care, behavioral health, condition management, therapy.

Friendly assistant for healthcare and welfare.

Guides through healthcare app legalities

Professional advice for healthcare managers

Guiding healthcare conversations with insights.