Hippocratic AI

Hippocratic AI, a pioneering healthcare startup, is using AWS support and infrastructure to introduce innovative patient care through AI technology. Co-founder and CEO Munjal Shah discusses how the company’s large language model (LLM) powers AI agents capable of natural, real-time conversations with patients—managing chronic conditions, ensuring medication adherence, and providing crucial follow-up care— while addressing the global shortage of healthcare workers. Subscribe to AWS: https://go.aws/subscribe Sign up for AWS: https://go.aws/signup AWS free tier: https://go.aws/free Explore more: https://go.aws/more Contact AWS: https://go.aws/contact Next steps: Explore on AWS in Analyst Research: https://go.aws/reports Discover, deploy, and manage software that runs on AWS: https://go.aws/marketplace Join the AWS Partner Network: https://go.aws/partners Learn more on how Amazon builds and operates software: https://go.aws/library Do you have technical AWS questions? Ask the community of experts on AWS re:Post: https://go.aws/3lPaoPb Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—use AWS to be more agile, lower costs, and innovate faster. #AWS #AmazonWebServices #CloudComputing #AWSStartup #ArtificialIntelligence #Healthcare

Asked how many in the room had ever received a proactive call from their healthcare provider, almost no hands went up. Vivek Muppalla treats that as the signature of scarcity: too few clinicians and too few hours, so the system triages and only the sickest get called. Hippocratic AI is past 200 million clinical conversations across more than 60 health systems, which is what changes the arithmetic. The tradeoff they refused is the familiar one: models accurate enough for this work can take tens of seconds to answer, and models fast enough for a phone call are not safe enough to make one. So the stack was built end to end. Polaris runs 31 models on every conversation, one holding the thread and 30 specialists covering labs, medications and scheduling, executed in parallel with each specialist first making a fast check on whether it has anything to say at all. A single model would be a single point of failure. Their speech recognition is a decoder only audio system fed the conversation so far and the domain context alongside the audio, so a drug name resolves against a finite list rather than an unbounded one, and prosody survives the projection so it hears the how as well as the what. Single word answers get a second scoring pass, because a heard as no is catastrophic. On evaluation he does the arithmetic that makes 99% unacceptable: at 10,000 calls a day that is 100 people sent to the wrong appointment, and catching a 1% failure rate takes roughly 450 tests. Speaker info: - https://x.com/vim1up - https://www.linkedin.com/in/vivekmuppalla/ - https://hippocraticai.com/ Timestamps: 0:00 - Who has ever been called by their provider 1:05 - Triage as a math problem, and what flips it 1:54 - The oath every employee takes 2:44 - A call with a patient, start to escalation 5:12 - Why a generic voice stack does not work 6:04 - Building vertically to get speed and intelligence 7:44 - Latency and intelligence as a compounding flywheel 8:36 - Polaris, and running 31 models at once 9:29 - What speech recognition gets wrong in the real world 10:27 - Feeding context and domain knowledge into the audio model 11:19 - A finite list of medications instead of an infinite one 12:10 - Rescoring single word answers 13:51 - How a specialist decides to speak up 14:43 - Verifiers for tool calls 15:34 - Quantization, speculative decoding, cache 16:25 - Why 99% is a bad number here 17:14 - The tests needed to catch a 1% failure 18:03 - Grading on the scale used for humans 18:55 - Building a benchmark for empathy