A huge problem also comes down to the patient diversity of our clinical trials; if our dataset is mostly european, then AI will often fail to generalize treatments for non-european population. This requires clinical reform as well; I argue that getting rid of the "undue inducement" guideline which prevents us from fairly compensating patients would help produce more diverse patient populations: https://onethousandmeans.substack.com/p/equitable-medicine-requires-fair
> As a result, Phase III osteoporosis trials typically enroll 10,000–16,000 participants and follow them for three to five years. The sheer scale and duration of these trials push costs to between $500 million and $1 billion.
Is there a magic bullet (related to AI) which can dramatically reduce the cost of participant enrollment and monitoring? Even if a trial may take a long time (because of the biology and clinical endpoints), can we make it so cheap to monitor patients that it makes a difference for investors?
I don't think you are thinking of the same kind of AI that Amodei is thinking of. There is a very big difference between narrow AI systems for generating drug candidates, which we have today to some extent, and an AGI that's as smart or smarter than the best researchers, which is what Anthropic is trying to make.
An AGI (by definition) would not need any more data to design a new drug candidate than human researchers do, and it might create much faster ways of getting that data, like super accurate and efficient simulations, or individualized lab-grown organs to test on, or things that are so out-there that no one has even thought of them. If it can cure a condition in a system with feedback loops that are 100x faster than clinical trials, with 99% certainty that it'll transfer to real humans, then yeah, the trials might only take a couple years. If you run a trial on a new AGI-designed miracle drug for a chronic disease, and a few months later every patient is perfectly healthy in every way you can measure, you probably do not need a 15-year cycle of long-term studies to determine the efficacy and safety of the drug (and some biohacker would probably mass-produce it illicitly if you tried).
You could argue that AGI is still far away, or that better simulations or lab-grown organs in particular wouldn't actually be that helpful, or that it would take a while for regulation to catch up, and those may all be true to some extent. However, the fact that our current clinical trial system has slow feedback loops that current tech can't solve does not seem that relevant in the scenario that Amodei is imagining, of a century of scientific progress compressed into 5-10 years.
So true! I actually wrote some more about this topic in response to Dario's Machines of Loving Grace.
https://alexeigannon.substack.com/p/ai-wont-unlock-the-tech-tree?utm_campaign=post-expanded-share&utm_medium=post%20viewer
A huge problem also comes down to the patient diversity of our clinical trials; if our dataset is mostly european, then AI will often fail to generalize treatments for non-european population. This requires clinical reform as well; I argue that getting rid of the "undue inducement" guideline which prevents us from fairly compensating patients would help produce more diverse patient populations: https://onethousandmeans.substack.com/p/equitable-medicine-requires-fair
Thank you, I really enjoyed reading this article.
I have a question related to operational costs.
> As a result, Phase III osteoporosis trials typically enroll 10,000–16,000 participants and follow them for three to five years. The sheer scale and duration of these trials push costs to between $500 million and $1 billion.
Is there a magic bullet (related to AI) which can dramatically reduce the cost of participant enrollment and monitoring? Even if a trial may take a long time (because of the biology and clinical endpoints), can we make it so cheap to monitor patients that it makes a difference for investors?
Thank you! What is the question?
Pressed send too soon, sorry. Now my comment is edited with the question.
I don't think you are thinking of the same kind of AI that Amodei is thinking of. There is a very big difference between narrow AI systems for generating drug candidates, which we have today to some extent, and an AGI that's as smart or smarter than the best researchers, which is what Anthropic is trying to make.
An AGI (by definition) would not need any more data to design a new drug candidate than human researchers do, and it might create much faster ways of getting that data, like super accurate and efficient simulations, or individualized lab-grown organs to test on, or things that are so out-there that no one has even thought of them. If it can cure a condition in a system with feedback loops that are 100x faster than clinical trials, with 99% certainty that it'll transfer to real humans, then yeah, the trials might only take a couple years. If you run a trial on a new AGI-designed miracle drug for a chronic disease, and a few months later every patient is perfectly healthy in every way you can measure, you probably do not need a 15-year cycle of long-term studies to determine the efficacy and safety of the drug (and some biohacker would probably mass-produce it illicitly if you tried).
You could argue that AGI is still far away, or that better simulations or lab-grown organs in particular wouldn't actually be that helpful, or that it would take a while for regulation to catch up, and those may all be true to some extent. However, the fact that our current clinical trial system has slow feedback loops that current tech can't solve does not seem that relevant in the scenario that Amodei is imagining, of a century of scientific progress compressed into 5-10 years.