Gene Hunting with o1-pro: Reasoning about Rare Diseases with ChatGPT Pro Grantee Dr. Catherine Brownstein
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What is the main topic discussed in this episode?
Rare diseases are quite common actually. Uh there's more people with rare diseases in the United States States than there are natural blondes. We need to sequence the whole world in order to understand what is actually disease causing and what is just background variation. Logging into the Harvard Library, getting that paper, skimming the abstracts, it's not at all what I want. Then going back and being able to ask for a summary and be like, Oh yeah, this sounds good has changed my life. It's all cutting down on this mundane, time consuming, really tedious part of the job. And getting back to the fun part, which is gene discovery. There's gonna be this whole generation of geneticists that aren't gonna know how things were done before all this was available because it's gonna be a huge game changer and time saver.
Hello and welcome back to the cognitive revolution. Today I'm speaking with doctor Catherine Brownstein, MPH, PhD, and assistant professor at Boston Children's Hospital and Harvard Medical School, whose research focuses on identifying the genetic causes of previously unexplained rare and orphan diseases, and who was recently awarded a ChatGPT Pro Grant from OpenAI. You may be surprised to learn, as I was, that so-called rare diseases are not necessarily all that rare. Any disease affecting fewer than one in two thousand people, or fewer than two hundred thousand people in the United States, is classified as a rare disease. And often families spend painfully frustrating years bouncing around the medical system in search of an accurate diagnosis before ultimately reaching Dr.
Brownstein's elite team at Boston Children's. Of course, considering the radical cost reduction that we've seen in genetic sequencing in recent years, which with nearly a ten thousand X improvement in affordability, is one of the very few cost curves ever to rival that of large language models, there's been an ongoing revolution in this space, even before the current AI moment. In 2007, a genome sequence cost upwards of $1 million. In that era, it was used only in the most challenging cases and was often a difference maker. Today, it's just a couple hundred dollars and has become commonplace for individual patients. But that creates new challenges for specialists like Catherine, who now have to comb through a vast and still exponentially growing literature to find candidate diagnoses for their most challenging cases.
This new wealth of information, which as you'll hear, could be growing even faster still with improved regulations and incentives, makes information processing capacity relatively scarce and valuable. And you can probably guess where this is going, a great target for the latest generation of reasoning models. This conversation then is above all a window into how frontier large language models are starting to become useful in highly specialized fields. Dr. Bronstein is pioneering the application of AI to rare disease research in real time. She's using AI to triage potentially relevant research, and in some cases to connect the dots between subtle clues. She's working directly with OpenAI to develop use cases and provide feedback.
And considering that every case represents a real person with a life-altering or even life-threatening condition, she's constantly working to find the right balance between enthusiasm for AI's capabilities and a healthy to be skepticism for any specific AI output. As you'll hear, she's still figuring out where AIs can be the most valuable, how best to use them, and how much to trust them. That such an established expert is bringing what amounts to a beginner's mindset to such high-stakes cases may be surprising to some, but really I don't think it should be. Even the most AI obsessed folks like me have only managed to log a few thousand hours with large language models, and nearly all of that was with earlier and less powerful models.
So for the current frontier, we're all still figuring this out together. And there's currently an unprecedented opportunity for people with deep experience in specific niche domains to become the leaders in applying AI to their particular fields.
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Chapters
5 chapters
1
What is the main topic discussed in this episode?
0:00–8:06
2
What defines a rare disease and why are they actually common?
8:06–12:31
3
How has the cost of genome sequencing changed and what impact does that have on diagnosis?
12:31–28:43
4
What role do AI‑powered pipelines play in triaging genetic data?
28:43–42:45
5
How do patients typically reach the Manton Center for Orphan Disease Research?
42:45–1:27:08
Speakers
3 identifiedMore from "The Cognitive Revolution"
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