 Thank you everyone for being here today. As you mentioned, my name is Rachel Pang. I work at Anthropic. I lead our healthcare life sciences industry. So I've been working a lot this last year and a half, and hopefully to inspire all of you to kind of take it back to your organizations on some ideas on how you can start leveraging Claude models via Bedrock. So just to start off with some of our founding beliefs at Anthropic, the language models, as I'm sure you all have seen, the language models are going to continue to evolve at a very exponential rate. Just even looking at quad 2.1 last year compared to 3.5, there's been an extremely big jump. I'll just say for myself, it's been much more fun selling 3.5 than 2.1. And these advances, they will have the potential to disrupt the labor economy. And it's really important and something that we believe in our DNA at Anthropic, that this technology needs to be deployed responsibly. So just an introduction of who Anthropic is. You would like to learn more about us. We have a booth over there, but we really believe in responsible AI. We created this concept called constitutional AI. And that's something we really believe in. And. We created this concept called constitutional AI. And that's something we really believe in. And I think that resonates a lot, especially with customers within industries such as healthcare life sciences. And although it's still a very, let's say, new phase, we're still very new in the AI space, AI is already starting to have massive impact in organizations. So for example, especially in support, we're seeing 20% to 35% faster response times for support teams. For coding, it's been pretty incredible. Claude is extremely good at coding tasks. 30% to 50% faster content creation for sales and marketing, and 20% to 50% faster task completions for back office work. But that's not what you guys want to talk about today, I'm sure. So I want to tell you a little bit more about Cloud 3.5 Sonnet before we get into the specific HCLS use cases. But the most recent Cloud 3.5 Sonnet, it strikes the ideal balance between the market-leading intelligence, speed, and cost. It's available now on Bedrock. But what's been really interesting is in the last few weeks, we've announced some extremely cool features, such as computer use. And I'm going to share that a little bit here. So what is computer use? I think it's better just to explain it in a story. So let's say I have to plan my daughter's birthday party. I tell Claude, hey, I want you to book a jump house and then I want you to book, I want you to order me a pizza for all the kids and I need you to order me a bunch of stuff on Amazon for the party favors. With computer use, I give this instruction to Claude, and it actually does this task for me through the computer use, so through a bunch of APIs. This is now in public beta. I really encourage all of you to check it out. We know at Anthropic it's not perfect yet, but this is just to show that we are continuing to innovate on some really amazing features. And today, I actually talked to a customer, and they were extremely excited because they immediately thought, wow, I could do this for all of the different forms that I have to do within life sciences. Just imagine how much you can simplify some of your workflows within the life sciences, just imagine how much you can simplify some of your workflows within the life sciences space. So again, some of the example use cases you can see here. We launched this back a few weeks ago in October. It's currently in public beta, so not in bedrock, but it will soon be available. Unfortunately, we can't do the demo here. Some technical difficulties, but we have a really amazing demo on our website, so I encourage you to all check it out. Cool. So just a little bit more information about what we've been seeing in terms of trends in the last year and a half across HCLS. So, Novo Nordisk, for example, this was a public case study. They were able to use Claude partnering with MongoDB to reduce clinical creation documents from 12 weeks to 10 minutes. This actually prevented millions of dollars in opportunity costs for them. With Pfizer, they've been using Claude for drug discovery, specifically on drawing on scientific content. And then also within patient triaging, we're seeing a lot of customers using this as a patient interface. So they're using it to obviously message with patients and creating a more seamless patient experience. And diving a little bit deeper on the Novo Nordisk use case. So Novo Nordisk, something that they were seeing a lot of challenges with was with their clinical study report, which I'm sure all of you can understand and relate to. It typically took them about 12 weeks to compile, and this really delayed them in getting new treatments to patients. So what they did is that they developed something called NovoScribe. And it's a generative AI solution that used Bedrock, Langchain, and MongoDB to automate and streamline the CSR creation process. And again, what this did is it changed the process to go from 12 weeks to 10 minutes. And it also increased the quality of the CSR outputs with fewer resources. So this is just one of the examples. And what's really amazing, I would say, with these types of use cases is that you can easily translate these to your organizations, right? I mean, the processes are very similar, and these are things that you can easily translate these to your organizations, right? I mean the processes are very similar and these are things that you can start to do them yourself within your companies as well. Just to share a little bit more as well and what we're seeing across the industry, in terms of use cases that I've been seeing and talking to customers, we see a lot within, I would say, the clinical trial side. A lot of experimentation happening on the R&D side and a lot on the commercial side. I think now we're seeing companies wanting to experiment more in R&D, but it's still, I would say, early infancy stages on seeing the value of where generative AI will be. But in terms of just being able to cut costs, to help your scientists to minimize time and things like that, the clinical trial side of things is where we're seeing about 60% of our use cases. So what does this mean, I guess, from you to take away for today? So I'm not sure. I guess raise your hands. Who's used Claude in the audience? That's amazing. OK, great. So as you know, it's available on Bedrock. You can access all of our models. We try to keep model parity with Bedrock and as well as first party. So I think what could be a really great starting point for all of you is to look at the prompting suite. You can use our meta-prompter that's available on our website. There's a ton of documentation there. There's also a lot of resources on Bedrock as well. We also are focusing on contextual retrieval, so something called prompt caching. So the idea is that we're going to continue to make our context windows longer and longer. Right now I think we're at 500,000 for specific enterprise customers and we see a world where it's going to get into the millions, but prompt caching is a way for it essentially to do fine-tuning, kind of like fine tuning, where you can actually have the model actually read the prompts and not have to do actual fine tuning. You'll see more agentic use cases coming out. And then, of course, this comes up a lot. But how do we understand the model? Anthropic has started to do this. A lot of interpretability work, and we will continue to invest resources in this space. I kind of flew by that a little bit quickly, so if there are any questions from the audience, I'm happy to take them.
AWS re:Invent 2024 - Claude's role in streamlining drug development (HLS213)
In this lightning talk, the Anthropic team shares how leading life sciences companies are using generative AI to revolutionize drug development workflows. Learn how AI is addressing the “cold start” problem in creating critical documents, transforming it into a “quick start” solution. Also explore real-world applications in rapid protocol creation for clinical trials, efficient generation of safety narratives, and streamlined production of clinical safety reports.

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