Regard Launches Chatbot + Clinical Workflow LLM, Powered by OpenAI’s ChatGPT-4

What You Should Know:

  • Regard, a software company developing an artificial intelligence (AI) co-pilot for clinicians to help diagnose medical conditions, announced today that it has expanded its relationship with OpenAI, an AI research and development company.
  • Through this relationship, Regard will release new core product functionalities and a new chatbot, Max, that is built on OpenAI’s large language model (LLM), GPT-4.

AI-Driven Research and Development in Healthcare

Investing in healthcare-focused Generative AI is crucial now, given its potential to revolutionize the entire healthcare ecosystem. While the industry is exploring impactful applications, innovative companies like Regard are leading the way in piloting new functionalities, testing cost and outcome impacts and sharing insights with the broader market, making this partnership and work cutting-edge.

While many companies discuss the possibilities of utilizing GPT-4 and LLMs in a healthcare context, Regard is one of the few actually operationalizing the technology and testing its capabilities. Regard’s new pilot LLM functionality will include:

  1. Automatically drafting portions of the clinical note fully backed by patient data, emulating the role of a Medical Resident
  2. Equipping clinicians with intelligent autocomplete functionality to save documentation time by searching through the data seamlessly
  3. Checking documentation against clinical guidelines for more accurate auditing

“It is an exciting time for AI in healthcare and Regard is proud to be a pioneer in developing AI solutions for our health system customers,” said Eli Ben-Joseph CEO and co-founder of Regard. “AI is not going to replace anyone in the near future, but the teams using AI will. We want to help our health system customers accelerate their ability to use AI to build the future of medicine. We are optimistic and excited about the impact of our new functionality leveraging GPT-4, but also are taking an iterative approach to ensure that we can test, learn and share our findings with the Regard community and peers in the industry.”