Summary of Polaris: a Safety-focused Llm Constellation Architecture For Healthcare, by Subhabrata Mukherjee et al.

Polaris: A Safety-focused LLM Constellation Architecture for Healthcare

by Subhabrata Mukherjee, Paul Gamble, Markel Sanz Ausin, Neel Kant, Kriti Aggarwal, Neha Manjunath, Debajyoti Datta, Zhengliang Liu, Jiayuan Ding, Sophia Busacca, Cezanne Bianco, Swapnil Sharma, Rae Lasko, Michelle Voisard, Sanchay Harneja, Darya Filippova, Gerry Meixiong, Kevin Cha, Amir Youssefi, Meyhaa Buvanesh, Howard Weingram, Sebastian Bierman-Lytle, Harpreet Singh Mangat, Kim Parikh, Saad Godil, Alex Miller

First submitted to arxiv on: 20 Mar 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary Polaris is a novel language model constellation designed for real-time patient-AI healthcare conversations. Unlike previous works, Polaris focuses on long multi-turn voice conversations and addresses challenges like safety, hallucinations, and rapport building. The system consists of multiple multibillion-parameter LLMs acting as cooperative agents to drive engaging conversations and perform healthcare tasks. A sophisticated training protocol iteratively co-trains the agents to optimize for diverse objectives. Polaris is trained on proprietary data, clinical care plans, regulatory documents, medical manuals, and simulated patient-nurse conversations. The system is designed to speak like medical professionals and demonstrates unique capabilities like empathy and bedside manner. Clinicians evaluated Polaris, with over 1100 nurses and 130 physicians rating the system highly across dimensions like medical safety, clinical readiness, conversational quality, and bedside manner. Additionally, Polaris outperformed GPT-4 and LLaMA-2 70B in task-based evaluations of individual specialist support agents.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Polaris is a new way for artificial intelligence (AI) to have conversations with patients in healthcare settings. Unlike other AI systems that just answer questions, Polaris helps have long talks like humans do. It’s made up of many smaller AI models working together to make sure the conversation stays safe and helpful. To train Polaris, researchers used lots of medical information and made it talk like a real doctor or nurse. They even had people play the role of patients and rated how well Polaris did in different areas. The results showed that Polaris is very good at having these conversations and can even do better than some other AI systems.