AI Research
Generative AI Transforms Clinical Study Report Development
This presentation was created based on an
Generative artificial intelligence (AI) has absolutely exploded onto the scene. It’s become a top strategic priority in the life sciences, and honestly, for good reason. It holds this incredible promise to dramatically speed up drug development and really shake up the regulatory processes we’ve known for years. Let’s dive into this.
Right at the heart of this whole transformation is one single critical document, the clinical study report, or CSR. These reports, they’re the absolute backbone of regulatory submissions, and generative AI is set to completely change the game for how they’re created, cutting down on manual work, and we hope, shortening those timelines. But this leads to a really fascinating and kind of frustrating puzzle. With all the tech we have now, with all the advances we’ve made, why has the time it takes to produce these crucial reports pretty much stayed the same? For over a decade, it’s a question that really gets to the core of the problem here. Back in 2013 a CSR took somewhere between 8-15 weeks to get done. Now you think that would have improved, right? Well, a recent survey shows that today, that ranges between 6-15 weeks. We’ve seen a decade of innovation with almost no change in the final output. Something is clearly broken.
This brings us right to the main point. The real bottleneck isn’t the technology we’re using, it’s the process itself. If you just automate a flawed, inefficient process, you don’t actually fix it, you just get faster at creating content that’s still going to need weeks, maybe even months, of painstaking manual review. This really highlights a massive risk. If you take an AI and you train it on a bloated, inefficient process, what’s it going to learn? It’s just going to learn to replicate those same bad habits, so you won’t get true efficiency. You’ll just get faster at being inefficient, and that’s not the goal at all, is it?
To really get this right, organizations have to focus on two fundamental pillars before they even think about deploying AI at scale. First up is data readiness. Think of that as the solid foundation you’re building on. And second is content readiness. That’s the framework that shapes what the AI actually creates.
Let’s dig into that first pillar, data readiness. You can think of this as the high-quality fuel that any successful AI project absolutely has to run on. Here’s where we hit our first major snag. While our patient level data often follows really clear industry standards like Study Data Tabulation and Analysis Data models, the summary level data, the stuff that’s essential for building these CSRs is often just all over the place. It can be wildly inconsistent from study to study across different therapeutic areas, even within the same company, and all that inconsistency, it just forces a bunch of extra complicated steps before any AI can be reliably put to work.
Once you’ve got that data foundation solid, we can move on to the second pillar, which is just as critical, getting the content itself prepped and ready for automation. This is basically like cleaning the house before you let the robots in to help. Optimizing your content really boils down to three key tasks. First, you’ve got to clearly define the scope of each document, and then come the two big ones. You have to systematically get rid of all that subjective, opinion-based content, and then you have to hunt down and eliminate redundant, repeated information.
Let’s just break down the difference here. Objective content is all about the facts. It describes pre specified outcomes. This is what preserves scientific integrity and lets you compare apples to apples across different studies. Subjective content, on the other hand, that reflects opinions, interpretations, and it introduces potential bias and creates all these inconsistencies that regulators then have to spend time sorting through. For AI to work, and frankly, for good science, the focus has to be on objective, fact-based reporting.
Now let’s talk about redundancy. This is truly the silent killer of efficiency when you’re creating documents. Think about it, every single time information is repeated, you’re creating a risk for transcription errors, and you’re adding to the massive burden of manual review and all those painful accuracy checks. It’s just waste, plain and simple. This really highlights a fundamental shift we need in our thinking. The old way was to just reuse content; rewriting protocol details into the CSR or copying and pasting numbers from a table right into the text. The new, much smarter way is to refer. Instead of rewriting everything, just link to the protocol appendix. Instead of repeating data, just refer the reviewer directly to the validated tables and figures. It’s cleaner, it’s safer, and it’s so much faster.
What does this all lead to? When you get this right for the CSR, it creates this amazing positive ripple effect. It’s not just about improving one document. It’s about transforming the entire content ecosystem for a whole regulatory submission. In this ideal ecosystem, every single part of the common technical document, the common technical document, has a crystal-clear purpose. The CSRs, they serve as the objective data foundation, just the facts. The module two summaries then use that data to weave the narrative story, and finally, the module two overviews provide that crucial analysis and interpretation. There’s no overlap, no redundancy, just a clean, logical flow of information.
The benefits here are huge, and they go both ways. For the authoring teams, this approach saves an incredible amount of time by just getting rid of duplication, and for the regulatory reviewers, their experience is dramatically better. They get a submission that’s clearer, more logical, and so much easier to navigate. It’s a total win-win.
All of this brings us to the real value of AI in this space, and it’s so much more profound than just making things go a little faster. At the end of the day, the real story here isn’t just about speed. The greatest value of generative AI is that it’s a catalyst. It’s forcing the entire industry to stop and fundamentally rethink and redesign these outdated processes, making them leaner, more consistent, and ultimately a lot more valuable for everybody involved.
The path forward is pretty clear. To really unlock what AI can do, professionals in clinical operations and regulatory affairs need to standardize their data, streamline their content, fully embrace objective reporting, and build a culture that actually values efficiency over those old, outdated habits. Generative AI isn’t coming, it’s already here, so the real question for every organization isn’t if you’re going to adopt it, it’s whether your foundational processes are actually ready for the transformation that’s about to happen.
AI Research
Advarra launches AI- and data-backed study design solution to improve operational efficiency in clinical trials
Advarra, the market leader in regulatory reviews and a leading provider of clinical research technology, today announced the launch of its Study Design solution, which uses AI- and data-driven insights to help life sciences companies design protocols for greater operational efficiency in the real world.
Study Design solution evaluates a protocol’s feasibility by comparing it to similar trials using Braid™, Advarra’s newly launched data and AI engine. Braid is powered by a uniquely rich set of digitized protocol-related documents and operational data from over 30,000 historical studies conducted by 3,500 sponsors. Drawing on Advarra’s institutional review board (IRB) and clinical trial systems, this dataset spans diverse trial types and therapeutic areas, provides granular detail on schedules of assessment, and tracks longitudinal study modifications, giving sponsors deeper insights than solutions based only on in-house or public datasets.
“Too often, clinical trial protocols are developed without the benefit of robust comparative intelligence, leading to inefficient designs and operations,” said Laura Russell, senior vice president, head of data and AI product development at Advarra. “By drawing on the industry’s largest and richest operational dataset, Advarra’s Study Design solution delivers deeper insights into the feasibility of a protocol’s design. It helps sponsors better anticipate downstream operational challenges, make more informed decisions to simplify trial designs, and accelerate protocol development timelines.”
Advarra’s Study Design solution can be used to optimize a protocol prior to final submission or for retrospective analyses. The solution provides insights on design factors that drive operational feasibility, such as the impact of eligibility criteria, burdensomeness of the schedule of assessment on sites and participants, and reasons for amendments. Study teams receive custom benchmarking that allows for operational risk assessments through tailored data visualizations and consultations with Advarra’s data and study design experts. Technical teams can work directly within Advarra’s secure, self-service insights workspace to explore operational data for the purpose of powering internal analyses, models, and business intelligence tools.
“Early pilots have already demonstrated measurable impact,” added Russell. “In one engagement, benchmarking a sponsor’s protocol against comparable studies revealed twice as many exclusion criteria and 60 percent more site visits than industry benchmarks. With these insights, the sponsor saw a path to streamline future trial designs by removing unnecessary criteria, clustering procedures, and adopting hybrid visit models, ultimately reducing site burden and making participation easier for patients.”
Study Design solution is the first in a series of offerings by Advarra that will be powered by Braid. Future applications will extend insights beyond protocol design to improve study startup, enhance collaboration, and better support sites.
To learn more about Study Design solution or to request a consultation, visit advarra.com/study-design.
About Advarra
Advarra breaks the silos that impede clinical research, aligning patients, sites, sponsors, and CROs in a connected ecosystem to accelerate trials. Advarra is number one in research review services, a leader in site and sponsor technology, and is trusted by the top 50 global biopharma sponsors, top 20 CROs, and 50,000 site investigators worldwide. Advarra solutions enable collaboration, transparency, and speed to optimize trial operations, ensure patient safety and engagement, and reimagine clinical research while improving compliance. For more information, visit advarra.com.
Palantir has outperformed Nvidia so far this year, but investors shouldn’t ignore the chipmaker’s valuation.
Artificial intelligence (AI) investing is a remarkably broad field, as there are numerous ways to profit from this trend. Two of the most popular are Nvidia (NVDA -1.55%) and Palantir (PLTR -0.58%), which represent two different sides of AI investing.
Nvidia is on the hardware side, while Palantir produces AI software. These are two lucrative fields to invest in, but is there a clear-cut winner? Let’s find out.
Image source: Getty Images.
Palantir’s business model is more sustainable
Nvidia manufactures graphics processing units (GPUs), which have become the preferred computing hardware for processing AI workloads. While Nvidia has made a ton of money selling GPUs, it’s not done yet. Nvidia expects the big four AI hyperscalers to spend around $600 billion in data center capital expenditures this year, but projects that global data center capital expenditures will increase to $3 trillion to $4 trillion by 2030. That’s a major spending boom, and Nvidia will reap a substantial amount of money from that rise.
However, Nvidia isn’t completely safe. Its GPUs could fall out of style with AI hyperscalers as they develop in-house AI processing chips that could steal some of Nvidia’s market share. Furthermore, if demand for computing equipment diminishes, Nvidia’s revenue streams could fall. That’s why a subscription model like Palantir is a better business over the long term.
Palantir develops AI software that can be described as “data in, insights out.” By using AI to process a ton of information rapidly, Palantir can provide real-time insights for what those with decision-making authority should do. Furthermore, it also gives developers the power to deploy AI agents, which can act autonomously within a business.
Palantir sells its software to commercial clients and government entities, and has gathered a sizable customer base, although that figure is rapidly expanding. As the AI boom continues, these customers will likely stick with Palantir because it’s incredibly difficult to move away from the software once it has been deployed. This means that after the AI spending boom is complete, Palantir will still be able to generate continuous revenue from its software subscriptions.
This gives Palantir a business advantage.
Nvidia is growing faster
Although Palantir’s revenue growth is accelerating, it’s still slower than Nvidia’s.
NVDA Revenue (Quarterly YoY Growth) data by YCharts
This may invert sometime in the near future, but for now, Nvidia has the growth edge.
One item that could reaccelerate Nvidia’s growth is the return of its business in China. Nvidia is currently working on obtaining its export license for H20 chips. Once that is returned, the company could see a massive demand from another country that requires significant AI computing power. Even without a massive chunk of sales, Nvidia is still growing faster than Palantir, giving it the advantage here.
Nvidia is far cheaper than Palantir
With both companies growing at a similar rate, it would be logical to expect that they should trade within a similar valuation range. However, that’s not the case. Whether you analyze the stocks from a forward price-to-earnings (P/E) or price-to-sales (P/S) basis, Palantir’s stock is unbelievably expensive.
NVDA PE Ratio (Forward) data by YCharts
From a P/S basis, Palantir is about 5 times more expensive than Nvidia. From a forward P/E basis, it’s about 6.5 times more expensive.
With these two growing at the same rate, this massive premium for Palantir’s stock doesn’t make a ton of sense. It will take years, or even a decade, at Palantir’s growth rate to bring its valuation down to a reasonable level; yet, Nvidia is already trading at that price point.
I think this gives Nvidia an unassailable advantage for investors, and I think it’s the far better buy right now, primarily due to valuation, as Palantir’s price has gotten out of control.
Keithen Drury has positions in Nvidia. The Motley Fool has positions in and recommends Nvidia and Palantir Technologies. The Motley Fool has a disclosure policy.
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