Munjal Shah Launches Hippocratic AI to Apply Large Language Models to Improve Healthcare Outcomes

Serial entrepreneur Munjal Shah has launched a new startup called Hippocratic AI that aims to leverage recent advances in artificial intelligence, specifically large language models (LLMs), to provide better chronic care and patient navigation services. Munjal Shah believes these AI systems can help bridge the gap between the growing number of patients with chronic health conditions and the limited number of healthcare professionals available to provide care.

LLMs like ChatGPT have demonstrated an impressive ability to understand context, summarize large amounts of information, and generate high-quality written responses. Munjal Shah wants to apply these capabilities to healthcare communication. As he points out, there are only a few hundred thousand chronic care nurses in the U.S., but over 68 million Americans with multiple chronic conditions. Hippocratic AI’s LLM could act as an AI-powered virtual nurse for each patient, providing reminders, guidance, and coordination support.

Munjal Shah stresses that the goal is not to replace human nurses but to use AI to “super staff” the healthcare system to levels that have never been possible before due to practical constraints. By leveraging LLMs trained in medical information, Hippocratic AI aims to improve healthcare access and, ultimately, outcomes for the patients who need it most.

Crucially, Hippocratic AI will focus exclusively on non-diagnostic applications of its LLM technology. Munjal Shah recognizes that even advanced AI still makes mistakes, and errors cannot be tolerated when providing diagnoses or treatment recommendations. Instead, the LLM will be used for safer applications like sending reminders, answering routine questions, coordinating care, explaining billing details, and more.

To ensure accuracy, Hippocratic AI trains its model on a large medical dataset spanning peer-reviewed research, clinical guidelines, insurance policies, and other authoritative sources. The system is also carefully evaluated by human medical experts specializing in the types of care communication the AI aims to provide. This helps validate that the LLM meets the necessary standards before being deployed to patients.

In the article, Munjal Shah provides examples of areas where Hippocratic AI could make a meaningful difference immediately. Calling patients about expected test results takes time that nurses often don’t have to spare. But an AI assistant could easily make these routine calls instead. Similarly, billing and insurance details often must be clarified for medical staff and patients. An LLM specifically trained in insurance plans could generate clear explanations of benefits and charges for each individual.

By offloading these repetitive, non-urgent tasks to AI, human healthcare professionals could redirect their precious time to the work only they have the skills and empathy to do – diagnosing conditions, developing treatment plans, having sensitive conversations, and building trusted relationships with patients. This is the future Munjal Shah envisions with Hippocratic AI – not replacing nurses but maximizing their impact.

With chronic conditions on the rise and nurses leaving the field in droves due to stress and burnout, there is growing urgency around improving healthcare capacity and access. Munjal Shah believes AI can play a pivotal role by multiplying the number of care team members available to each patient. While promising, success will depend on Hippocratic AI’s ability to deploy LLMs safely and accurately and only for appropriate use cases where AI augmentation makes sense. Munjal Shah’s entrepreneurial track record and focus on responsible AI give optimism. However, the proof will be in the patient outcomes as Hippocratic AI moves from concept to real-world implementation.