Best AI tools for< Generate Clinical Notes >

MDHub is a clinical AI assistant designed to support behavioral health clinicians in their practice. It offers a user-friendly interface for seamless integration into workflows, time-saving features like instant audio transformation for charting, and personalized treatment plan recommendations. The application aims to enhance patient care by automating documentation tasks and improving efficiency in mental health practices.

Sunoh is a medical AI scribe that uses ambient listening technology to convert natural conversations between healthcare providers and patients into clinical documentation. It offers a unique and immersive experience for both doctors and patients, making the documentation of clinical notes faster and more efficient than ever before. Sunoh can be used with your EHR to accelerate your documentation.

AutoNotes is a leading healthcare AI Progress Note tool that offers AI-powered clinical documentation templates for generating SOAP Notes, DAP Notes, Treatment Plans, and more. It provides a user-friendly interface for therapists and healthcare professionals to create detailed and customizable clinical notes efficiently. With features like summarizing sessions, editing and downloading notes, and simple pricing plans, AutoNotes aims to streamline the documentation process in healthcare settings. The platform also offers advanced features like template customization, secure document storage, and dictation for voice-to-text conversion. Users can benefit from the platform's customization options, seamless integration with workflows, and responsive customer support.

Heidi is an AI-powered medical scribe that helps clinicians save time and improve patient care. It uses natural language processing to capture every detail of a patient visit, and then automatically generates a note that is tailored to the clinician's preferences. Heidi can also be used to create letters, add billing codes, and generate patient summaries. It is trusted by clinicians and healthcare staff in over 35 countries.

SOAP Note AI is an AI-powered tool designed to generate HIPAA-compliant, fast, and efficient SOAP notes for various healthcare specialties, including Physical Therapy, Occupational Therapy, Nursing, Mental Health, SLP, Dentistry, Podiatry, Massage, Acupuncture, Chiropractic, Veterinary, and Pharmacy. The tool helps healthcare professionals save time by quickly converting shorthand notes, audio dictations, or AI Scribe session recordings into comprehensive SOAP notes. It offers specialized SOAP note templates, access to note history for up to 30 days, and a feedback feature for instant review. SOAP Note AI simplifies the documentation process, ensures HIPAA compliance, and offers flexible pricing plans for different user needs.

Everbility is an AI-powered clinical documentation tool designed for Allied Health Professionals. It helps in writing reports, synthesizing client notes, brainstorming ideas, and focusing on client care. The tool saves time by generating progress notes, letters, and assessment reports, while ensuring data privacy and compliance with regulations like HIPAA and Australian Privacy Principles.

Lyrebird Health is an AI-powered medical scribe that automates documentation tasks for healthcare providers. It uses natural language processing (NLP) to listen in on patient encounters and generate accurate, medico-legally compliant notes, letters, and assessments. Lyrebird Health is designed to save clinicians time and reduce burnout, allowing them to focus on providing better care to their patients.

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.

S10.AI is an AI-powered medical scribe application designed to streamline medical documentation processes for healthcare professionals. It offers seamless integration with any EMR system, providing accurate and efficient transcription of patient conversations. The application saves time, ensures confidentiality, and adapts to various medical templates and workflows. S10.AI is praised for its precision, efficiency, and support, making it a valuable asset for practitioners looking to enhance administrative tasks without compromising patient care.

Regard is an AI-powered healthcare solution that automates clinical tasks, making it easier for clinicians to focus on patient care. It integrates with the EHR to analyze patient records and provide insights that can help improve diagnosis and treatment. Regard has been shown to improve hospital finances, patient safety, and physician happiness.

Insight is an AI-powered medical research tool that serves as a research assistant for generating scientific summaries, hypotheses, experimental designs, and target identification. It empowers scientists to navigate literature, formulate hypotheses, and design experiments by utilizing peer-reviewed databases to provide reliable outputs. With integrated features like NIH PubMed access, NIH Reporter insights, and MYGENE & MYVARIANT deep dives, Insight streamlines the research process and accelerates discoveries in the medical field.

Neural Consult is a cutting-edge AI-powered medical education platform that offers personalized learning tools to empower medical students. The platform provides unlimited board questions, Anki cards, clinical simulations, and more, tailored to individual study needs. With features like question generation, lecture summaries, differential diagnosis, and AI-powered case simulations, Neural Consult revolutionizes the way medical students learn and prepare for exams. Trusted by students at top medical schools, Neural Consult aims to enhance clinical reasoning, memorization skills, and overall learning efficiency through innovative AI technology.

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.

CompliantChatGPT is a HIPAA-compliant platform that allows users to utilize OpenAI's GPT models for healthcare-related tasks while maintaining data privacy and security. It anonymizes protected health information (PHI) by replacing it with tokens, ensuring compliance with HIPAA regulations. The platform offers various modes tailored to specific healthcare needs, including bloodwork analysis, PHI anonymization, diagnosis assistance, and treatment planning. CompliantChatGPT streamlines healthcare tasks, enhances productivity, and provides user-friendly assistance through its intuitive interface.

Protocol Pal is a website designed to help users with building protocols. It is a user-friendly platform created by Basam Alasaly, Oceanexplains, and Tkruer. The website aims to simplify the process of creating protocols for various purposes. Users can easily navigate through the platform to generate structured protocols efficiently. Protocol Pal is a valuable tool for researchers, scientists, and professionals who require well-defined protocols for their work.

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.

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.

Medical Brain is an AI-powered clinical assistant designed for both patients and providers. It engages with users to identify health risks and care gaps early, providing actionable insights and guidance to improve outcomes and intercept high-cost ER visits. The platform monitors patients 24/7, aggregates and understands all patient data, and generates real-time actions based on AI clinical decision support and automation. Medical Brain incorporates evidence-based best practices in various clinical modules and continuously learns from user experiences to enhance efficiency and intelligence.

Ubie is a medical AI application that offers a symptom checker and helps users find possible causes for their symptoms. Developed by doctors, Ubie's AI-powered system generates personalized reports on potential health issues based on a user's symptoms and personal information. The application aims to provide quick and accurate health information to users, guiding them on when to see a doctor, what might be causing their symptoms, and treatment information. Ubie values quality and safety, continuously improving its AI in collaboration with medical professionals worldwide.

Generate is an experiment to generate data from LLM. It is now deprecated.

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.

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 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**

BAML is a config file format for declaring LLM functions that you can then use in TypeScript or Python. With BAML you can Classify or Extract any structured data using Anthropic, OpenAI or local models (using Ollama) ## Resources  [Discord Community](https://discord.gg/boundaryml)  [Follow us on Twitter](https://twitter.com/boundaryml) * Discord Office Hours - Come ask us anything! We hold office hours most days (9am - 12pm PST). * Documentation - Learn BAML * Documentation - BAML Syntax Reference * Documentation - Prompt engineering tips * Boundary Studio - Observability and more #### Starter projects * BAML + NextJS 14 * BAML + FastAPI + Streaming ## Motivation Calling LLMs in your code is frustrating: * your code uses types everywhere: classes, enums, and arrays * but LLMs speak English, not types BAML makes calling LLMs easy by taking a type-first approach that lives fully in your codebase: 1. Define what your LLM output type is in a .baml file, with rich syntax to describe any field (even enum values) 2. Declare your prompt in the .baml config using those types 3. Add additional LLM config like retries or redundancy 4. Transpile the .baml files to a callable Python or TS function with a type-safe interface. (VSCode extension does this for you automatically). We were inspired by similar patterns for type safety: protobuf and OpenAPI for RPCs, Prisma and SQLAlchemy for databases. BAML guarantees type safety for LLMs and comes with tools to give you a great developer experience:  Jump to BAML code or how Flexible Parsing works without additional LLM calls. | BAML Tooling | Capabilities | | ----------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | BAML Compiler install | Transpiles BAML code to a native Python / Typescript library (you only need it for development, never for releases) Works on Mac, Windows, Linux  | | VSCode Extension install | Syntax highlighting for BAML files Real-time prompt preview Testing UI | | Boundary Studio open (not open source) | Type-safe observability Labeling |

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.

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.

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.

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.

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.

Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. _For interpretability in NLP, check out our new package:imodelsX _

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. |  |

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 provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

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.

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.

BasicLingua is a Python library that provides functionalities for linguistic tasks such as tokenization, stemming, lemmatization, and many others. It is based on the Gemini Language Model, which has demonstrated promising results in dealing with text data. BasicLingua can be used as an API or through a web demo. It is available under the MIT license and can be used in various projects.

Awesome LLM Strawberry is a collection of research papers and blogs related to OpenAI Strawberry(o1) and Reasoning. The repository is continuously updated to track the frontier of LLM Reasoning.

Apollo is a multilingual medical LLM that covers English, Chinese, French, Hindi, Spanish, Hindi, and Arabic. It is designed to democratize medical AI to 6B people. Apollo has achieved state-of-the-art results on a variety of medical NLP tasks, including question answering, medical dialogue generation, and medical text classification. Apollo is easy to use and can be integrated into a variety of applications, making it a valuable tool for healthcare professionals and researchers.

Voice-to-text, Vision-to-text transcription, Transcript-to-‘Clinical format’ integrated with CDS. Writes clinical notes, referral letter, generate PDF,prepare discharge summary. (Ultimate aid for clinicians)

Eyecare Clinical Assistant

An academic assistant for psychiatrists, creating educational content and practice questions. (Not for use in clinical decision making, verify all information, as model may produce errors)

Engaging, precise assistant for medical professionals.

私と製薬ネタで遊びましょう【注意】私のアウトプットはフィクションです。実在の人物や団体などとは関係ありません。

Regenerative medicine virtual assistant 'Regen'.

A Medical Research Assistant Chatbot

Especialista em dados de pós-graduação em Fisiologia

Explores emerging trends and innovations in medicine.

Rapid, precise medical research expert in orthopaedics.

Generates Angular components based on requirements, with a focus on code-first responses.

Transforms lines into detailed prompts for visual storytelling.

Discover how your writing can shine with a unique and human style. This prompt guides you to create rich and varied texts, surprising with original twists and maintaining coherence and originality. Transform your writing and challenge AI detection tools!

Generate captions for your image and choose the vibe you like.

I generate top 10 PLG strategies in a table format

I generate viral GPT ideas based on your inputs.

Generate your eslint rule

I generate witty trash talk for fantasy football leagues.

Generate dreamy landscapes with silhouette persons

Helps you generate script keyframes, for better experience please visit museclip.ai