How Nvidia Powered Hippocratic AI Solves Global Healthcare Staff Shortage with AI Nurses for $9 per hour

High-powered chipmaker Nvidia has teamed up with artificial intelligence healthcare company Hippocratic AI to develop generative AI healthcare agents that not only outperform human nurses on video calls but cost a lot less per hour. 

Hippocratic’s healthcare-focused large language model (LLM) is better able to form a human connection with patients through super-low latency conversational reactions.

Hippocratic agents have already been tested by more than 1,000 registered nurses and 100 licensed physicians in the U.S., and dozens of healthcare providers are trying out the bots internally for non-diagnostic tasks. 

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“With generative AI, patient interactions can be seamless, personalized, and conversational—but to have the desired impact, the speed of inference has to be incredibly fast. With the latest advances in LLM inference, speech synthesis, and voice recognition software, NVIDIA’s technology stack is critical to achieving this speed and fluidity. We’re working with NVIDIA to continue refining our technology and amplify the impact of our work of mitigating staffing shortages while enhancing access, equity, and patient outcomes,” said Munjal Shah, Co-founder and CEO of Hippocratic AI, in a press statement.

How Hippocratic AI Is Solving Healthcare Staffing Crisis with Polaris

According to a study by the Department of Health and Human Services, an astonishing 16.7% of hospitals anticipated a critical staffing shortage in 2023. Furthermore, the U.S. Bureau of Labor Statistics estimates a requirement of over 200,000 nurses each year until 2031. 

To achieve critical staffing shortages, Hippocratic AI built Polaris, a novel constellation architecture with multiple specialized healthcare LLMs working in unison. Hippocratic AI found this architecture allowed for accurate medical reasoning, fact-checking, and the avoidance of hallucinations while maintaining a natural conversation with patients. 

Hippocratic says its Constellation model outperformed real nurses 79% to 63% in identifying a medication’s impact on lab values; 88% to 45% in identifying condition-specific disallowed over-the-counter medications; 96% to 93% in correctly comparing a lab value to a reference range; and 81% to 57% in detecting toxic dosages of over-the-counter drugs.

Also Read: Nvidia CEO Predicts AI to Surpass Human-Level Tests Within Five Years

Hippocratic AI presented a comparative evaluation between U.S. Licensed nurses and AI nurses on manners (e.g., empathy, trust, rapport), medical safety, medical knowledge, patient education, clinical readiness, and overall conversation quality.

Overall, AI nurses were rated strikingly close to human nurse performance and were even found to outperform them on some key dimensions