Hippocraticai

Health language model, pre-trained for compassion.

LLM model learning healthcare

Tool Information

Primary Task	Large Language Models
Category	ai-and-machine-learning
Country	United States

Hippocratic AI is an Artificial Intelligence (AI) tool for healthcare that pre-trains on trusted, evidence-based content, unlike most language models that pre-train on the common crawl of the internet, which can contain incorrect and misleading information. The tool is a state-of-the-art (SOTA) model that outperforms GPT-4 on 105 out of 114 healthcare exams and certifications. The aim of Hippocratic AI is to improve healthcare access, equity, and outcomes by providing safe Language Learning Models (LLMs) for healthcare. The tool focuses on the thousands of other applications for LLMs in healthcare, where bedside manner and compassion are crucial. To ensure the model's readiness for deployment, Hippocratic AI conducts a unique Reinforcement Learning with Human Feedback process using healthcare professionals to train and validate the model. Hippocratic AI will not release the model until a large number of licensed healthcare professionals deem it safe. The tool is created for healthcare by healthcare professionals, including physicians, hospital administrators, payor experts, and AI researchers that have come from esteemed institutions like El Camino Health System, Johns Hopkins Hospital, Washington University in St. Louis, The University of Pennsylvania, Stanford, Google, and Nvidia. The AI tool has been incubated and funded by two of the best healthcare investors, with $50MM raised in a seed round. Hippocratic AI is committed to creating an objective evaluation system for developing a compassionate and caring healthcare-focused LLM and releasing the first of many bedside manner benchmarks in the coming months.

Hippocratic AI is an artificial intelligence company based in Palo Alto, founded in 2022. The company focuses on developing safety-oriented generative AI solutions for the healthcare sector. Its mission is to enhance healthcare accessibility, equity, and outcomes through responsible AI deployment. Hippocratic AI has raised a total of $278 million in funding, including a significant Series B round.

The company offers several innovative products, including the Polaris LLM Constellation, which features a multi-model architecture for healthcare tasks. This includes a primary conversational agent for real-time patient interactions and specialized agents for tasks like nutrition guidance. Additionally, Hippocratic AI provides a staffing marketplace that helps health systems deploy AI agents for low-risk tasks, addressing staffing shortages in various healthcare roles. Their AI Agent App Store allows clinicians to create custom AI agents tailored to patient care and operational needs.

Hippocratic AI collaborates with health systems such as WellSpan Health and Universal Health Services, and is supported by notable investors including Kleiner Perkins and Nvidia. The company emphasizes a safety-first approach, ensuring rigorous testing and clinician oversight in its applications.

Pros
Pre-trained on evidence-based content Outperforms GPT-4 in healthcare Aims to improve healthcare access Promotes healthcare equity and outcomes Safe language learning models Unique reinforcement learning process Validated by licensed healthcare professionals Created by healthcare professionals From esteemed healthcare institutions Funded by prominent investors Raises bedside manner benchmarks Focuses on compassion in healthcare $50MM raised in seed round Team with variety of experts Exclusive focus on healthcare Impressive performance on exams High margin of outperformance Investment in legally acquiring content First safety-focused LLM Addresses thousands of healthcare applications State-of-the-art (SOTA) model Model's deployment safety guaranteed Committed to creating evaluation system Emphasis on emotional well-being Objectivity in performance measures Open with results and benchmarks

Cons
Limited to healthcare applications Restricted pre-training data Requires healthcare professional validation Deployment delay possible Niche user group Delayed bedside manner benchmarks No evident scalability No language options indicated Lack of transparency in RLHF-HP process Bound by healthcare legalities