GPT3 and Large Language Models - an inflection point for AI

Sailu Challapalli, our Chief Product Officer, spoke at a recent Harvard Business School Healthcare panel. The event brought together different healthcare and AI experts to discuss large language models and their impact. 

Missed the event? You can watch a recording here.

Here are some key takeaways from the conversation:

1. So much depends on the use case! What does your organization want to do? What do you want to automate? What do you want to scale?‍
2. Large Language Models can support healthcare organizations. LLMs have the power to abate physician burnout by summarizing relevant patient information. Clinicians can also use LLMs to answer questions or retrieve information about medical terminology and  concepts. For example, one can ask: what is NSCLC or what are common treatments for NSCLC? Large Language Models can also be used to generate standard summaries and outputs, such an ER discharge report.‍
3. Large Language Models have their limitations. LLMs cannot support complex reasoning, data curation, or real-world evidence generation. They have engineering limitations and they are hard to update based on new knowledge, like new treatments and standards of care. They also don’t tie their results back to the source evidence. The biggest weakness of LLMs is that they are based on language not logic: the answers change based on how questions are asked and often contain bias. How do you stabilize it?

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A panel of experts agree: if your healthcare organization is not using AI you’re losing out on value. However, as you can see from the talking points above, there are nuances to understanding how to train and apply LLMs and there are many use cases where LLMs alone are likely not sufficient.

The first step to bringing AI to your healthcare organization is understanding your own needs and use cases.

Large Language Models are only one component of Mendel’s combined approach to AI data processing. Mendel uses both symbolic AI and machine learning to support reasoning since enabling logic is at the center of all our work. Consequently, Mendel’s resulting AI processing pipeline looks at individual patient records to understand individual patient journeys and outcomes. 

Want to learn more about Mendel’s approach and if it can help your organization? Contact us at hello@mendel.ai.