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An increase in protocol complexity is paving the way for machine learning models to optimize trial design.

Clinical Trial Failure: A Data-Driven Perspective on Risk Mitigation

Leveraging artificial intelligence-powered stochastic methods for clinical data review represents a significant leap beyond traditional approaches.

Revolutionizing Clinical Data Management: The Leap from Deterministic to AI-Powered Stochastic Methods

Understanding the benefits and concerns associated with implementing datasets into clinical trial workflows.

The Backbone of Innovation: High Quality Datasets

COVID-19 not only advanced scientific boundaries, but also transformed research methodologies and accelerated adaptive clinical trial design.

COVID-19 Five Years Later: The Impact on Drug Discovery 

Genomics has long been a primary focus in precision medicine, but other areas of research are increasingly being used to help attain a more complete picture of how an individual’s complex biology impacts their health profile.  

Integrated Multi-Omic Data: Powering Precision Medicine

In this video interview, Derek Ansel, vice president, therapeutic strategy lead, rare disease, Worldwide Clinical Trials, discusses how artificial intelligence can aid in bringing therapies to rare disease patients faster. 

Leading the Technology and Innovation Front Through Rare Disease Research

, Derek Ansel, vice president, therapeutic strategy lead, rare disease, Worldwide Clinical Trials, discussed the unique challenges in rare disease R&D, including competing priorities among researchers, difficulty in diagnosing and treating patients, and defining endpoints due to varying symptoms. Ansel also touched on the potential of technology and artificial intelligence to address some of these challenges and improve rare disease research.

ACT: Looking forward, what advancements or changes would you like to see in the rare disease space to make research more efficient and effective?

I think fixing all of the items that I've already mentioned would be of utmost importance. Again, increasing greater global collaboration across the various stakeholders, I think, will always be critical. Rare is challenging. Rare is difficult because of, in essence, what a rare disease is, but I think if we can lay the framework around that global collaboration and working together really helps the next disease community pick up where other leaders have laid the ground there. I'm really encouraged by the use of technology and of AI (artificial intelligence), I'm very much of a skeptic when it comes to that type of technology for various reasons, if you’re interested, happy to discuss more, but I think for a community largely that's been built by technology in its very essence, parents and caregivers have created drugs because of the challenges we see in rare disease, communities have been born because of the internet and the ability to connect directly with other patients as a necessity, because those communities otherwise wouldn't exist. Rare disease, I feel, really leads the technology and innovation front in rare disease research, so I'm really excited to see how they are going to be using AI, whether that's helping diagnose patients faster by looking at pictures of children with dysmorphic features to help get a diagnosis earlier in life, or by using AI, there are various companies out there that are linking the various approved drugs out there. Could they be used to support and treat and increase quality of life in patients who otherwise wouldn't have access to those drugs, and that's really being leveraged by AI, instead of having humans mine through those various datasets themselves in a very manual way, can leverage those technologies to connect to researchers and connect patients to drugs faster.

Videos

Addressing Clinical Trial Challenges

ACT: What advancements do you foresee for Medable AI and Medable Studio, and how might these innovations change the way clinical trials are conducted in the coming years?

We designed Medable Studio to scale clinical trial approaches across an entire company, enterprise, portfolio of work, and development asset. That’s one of the key ways we see growth happening. Up until now, we’ve conducted many pilot studies—companies would run a single study to test the approach. We’ve done a lot of those, and we’re now seeing the value they bring. We understand that operating trials this way offers significant benefits but scaling it across multiple trials has been a challenge. However, we’re now seeing companies adopt this approach more broadly.

This isn’t something limited to one or two trials or reserved for Phase IV studies—it’s a model that can be applied across all trials. It enables us to scale in ways that weren’t possible before. Traditionally, scaling innovation required significant human resources, almost like a tax or capital investment. Medable Studio eliminates that burden, making innovation more efficient.

AI further enhances these capabilities. We’ve already integrated AI into some of our tools. For example, building eCOA instruments—a process that once took weeks—now takes minutes, sometimes even seconds. That’s a significant advancement, but it only scratches the surface. Once an instrument is in the library, it never needs to be built again.

We’re also tackling translation, which has been a major bottleneck in running global trials. Eliminating that barrier will bring enormous benefits. Leveraging AI-powered translation tools will further streamline the process.

Beyond these efficiencies, AI is reshaping how clinical trials are conducted. It’s not just about collecting data through eCOA—it’s about passive data collection via sensors and wearables, enabling remote, large-scale monitoring. Additionally, integrating with EHR systems and tokenization vendors allows us to gather richer patient data and answer research questions in new ways. These innovations are fundamentally changing the clinical trial landscape.

: Key factors contributing to success include a strong customer value team with expertise in clinical research, not just technology. The team addresses product adoption, therapeutic area strategies, and patient engagement. A critical element for success is designing trials with patients’ daily lives in mind, ensuring that tasks are non-burdensome and easy to integrate into their routines. For example, in a study with patients suffering from extreme fatigue, notifications were adjusted to be more accessible by changing the timing to align better with patient habits.

Additionally, Medable has gained Canal approval for operating in France, overcoming previous barriers like stricter data protection regulations. This approval enhances confidence in using electronic consent (e-consent) across global trials, improving data security and participant benefits. Furthermore, advancements in AI and tools like Medical Studio are transforming trial operations. AI speeds up tasks like building eco instruments, reducing weeks of work to minutes, and offers solutions to challenges like language translation for global trials. With digital platforms, the company enables broader, scalable approaches for clinical trials across various therapeutic areas. This shift from pilot studies to large-scale implementations aims to enhance trial efficiency and cost-effectiveness across entire portfolios, rather than focusing on individual trials.

Lastly, regulatory bodies are adapting to decentralized trials, with the EMA and FDA releasing new guidelines that support the integration of digital solutions. Despite some regulatory discrepancies, such as the stricter Canal regulations in France, the industry is moving toward broader acceptance of decentralized trials, ensuring that digital tools are compliant with data security and privacy standards. This approach promises to reshape clinical trial methodologies, making them more efficient and accessible globally.

Andrew Mackinnon, Executive GM, Medable, discusses how AI-driven innovations are streamlining clinical trials through enabling scalable operations. 

Video Series

Addressing Clinical Trial Challenges Through AI and Scalable Solutions

In this video interview, Rachael Higgins, chief commercial officer, PicnicHealth, discusses industry’s next steps in being able to maximize the benefits of virtual models.

Executing with Clinical Technology

, Rachael Higgins, chief commercial officer, PicnicHealth, discussed the company’s recently released 

2025 Annual State of Observational Research Report

. She highlighted some of the key findings including the persistence of more traditional research methods as well as how patient retention and incomplete data are creating challenges for the industry.

ACT: Looking forward, what do you think are the next steps for industry in closing the gap between the need for technology innovation and actually being able to execute with it?

I think it all is going to come down to education and getting our customers comfortable with how new models actually work. It is embracing new technologies that are out there, perhaps it's doing a pilot or doing a softer launch of it, but that way to be able to see that. Number one, it's really not that scary. Number one, it's going to solve your data depth issue, it's going to solve your patient retention issue, it's going to solve your budget issues, just giving it a test trial, if you will. I think that that really is the only way that we're going to be able to do it. When I looked at the results of this report, I saw that 61% of them are already using some type of virtual model in AI, and to me, that's a huge opportunity, because, there's 40 something percent of them that haven't, but I think it's because they truly don't understand what they are, but being open to that, really understanding that there isn't just one way to do things, there are so many other ways to do that, to do what it is to get to your final outcome. Then also, I think, working with regulators, so that regulators become more and more comfortable with the ability to leverage AI and some of the other technologies to get to those endpoints as quickly as possible, to again bring medicines to patients, which is what we're looking to do.

In this video interview, Rachael Higgins, chief commercial officer, PicnicHealth, highlights how addressing these challenges can aid industry in moving more towards the future.

Addressing Data and Patient Retention Challenges

ACT: How can industry address some of the challenges holding it back from moving towards the future such as incomplete data and patient retention?

I think for companies, the life science partners that we do have, I think they truly need to not only say that they're open to things like virtual trials, but actually to be able to embrace them and actually take that leap to be able to do more of that, because that really is the only way to truly do things like getting access to the universal patient record by having patients consent directly, allowing you to get access to their medical record. It is a way that's no burden, or less burden to the patient, and then also give something back to the patient. Things like allowing patients to get access to their medical records and to be able to share their medical records, to be involved in the research, things like that are really going to certainly help them be able to scale up in the ways that they need to be able to do. When you think about all of our customers who are out there and the challenges that they have, I think they're all solvable. I think it's just getting them comfortable in the fact that the old way doesn't have to be the only way, right? There are certainly many ways to skin a cat, and I think these new ways certainly have proven to be effective. I think, again, it's just taking that leap. And then certainly, when you think about where the future is actually going to, the R&D costs are rising tremendously year over year. You see it in the news constantly with our life science partners having to make very significant cuts with not only their staff, thinking about their pipeline, what it is they're going to do, so they have to do more with less, and again, leveraging some of these types of models, virtual opportunities, leveraging direct connection to the patients, leveraging AI, that's going to be able to address their problems, which is going to allow them to scale in the future, which is really what everybody wants, which, again, at the end of the day, is going to be able to bring medicines to patients, which is really what we all want.

In this video interview, Rachael Higgins, chief commercial officer, PicnicHealth, discusses findings from the report and the identification of current challenges being faced by the industry.

Implementing Virtual Models in Clinical Trials: Results from PicnicHealth’s Annual State of Observational Research Report

ACT: Could you tell our audience a bit about the 

 and highlight some of its most noteworthy findings?

The research report that PicnicHealth ran basically shows that the industry is definitely at a turning point. The stats show that 80% of the leaders that we interviewed are either using or they're open to virtual models. I think the exact number was 78% and I think this is happening because they are number one, getting pressures from the regulators, payers, and patients for number one, real-world data. They also are leveraging AI and tech-enabled solutions to be able to deliver this in a more systematic, efficient way. If I look at all the stats that were out there, I think one of the major stats that came out is data completeness, and this is the industry's biggest hurdle, 64% of them said that incomplete data is their top challenge and if you think about traditional methods, like a site-based study or a traditional chart review, it just isn't able to deliver that data completeness or capture the entire patient record, if you will, which is why they're actually looking for other options for that. Another area, which again, is no surprise, is patient retention, the loss to follow up with any trial, is one of the biggest issues for life science partners, and they are looking for ways to actually solve that, so having a direct connection to the patient is one of the ways that can increase keeping patients engaged, which then, in turn, would, obviously, enhance the retention. Another thing that we highlighted was 60% of leaders actually flagged that long-term follow up studies was a top priority for 2025 and we know that there are so many pressures and demands on life sciences with all of the cost-cutting measures that are happening, so they're looking for innovative ways to do that. Then last, but certainly not least is AI. If you think about some of the releases from JP Morgan, if you think about some of the questions that folks had answers with our survey, they are having a lot of pressure to leverage AI, not because it's the fun thing to do, but really because it is an innovative, efficient way to really be able to hone in on what it is that you're looking for and then get those answers really quickly. I think 61% of them are either using it or open to actually being able to do that.

In this video interview, Rachael Higgins, chief commercial officer, PicnicHealth, discusses findings from the report and the industry's willingness to adopt technology.

In this video interview, Cameron Breze, product manager, Inovalon, discusses the need for accurate information in creating useful clinical data.

The Role of Quality Data in Utilizing Machine Learning Models

, Cameron Breze, product manager, Inovalon, discussed challenges in patient recruitment for clinical trials. Traditional recruitment methods, such as manual outreach, are now less efficient due to accessibility issues. Breze highlighted technology as a way to address recruitment challenges, which can be leveraged to automate and streamline processes, reducing labor intensity and errors.

ACT: With so many data sources now in the mix in clinical research, how important is the role of quality data in identifying patient populations?

Quality data is something that is top of mind for everyone in the in the data technology space. Many people like to use the age-old adage of garbage in garbage out when it comes to machine learning models and a lot of the data modeling that's available where if you're feeding it with inaccurate information, you're going to get a result that really can't be transposed and moved and translated into anything that's really applicable and that ends up being a waste of institutional resources and not the best way to serve patients. I can share a little anecdote as well on that. I published a research paper with the MIMIC for critical care database, where we looked at resuscitation, so fluid and IV administration in the hospital with organ failure, and found out through lots of computational modeling that essentially, we had a great record of the fluids that were put in, but not a great record of the fluids put out, so the clinical staff were not recording that, not because they were missing it or they were making some error, but in the scheme of making that particular research impactful, if you don't have that information, you can't make designations on that person's fluid status, and seeing that in aggregate across hundreds of thousands of patients, you get to see what a potential impact that could have had, had that policy or procedure been updated or changed, so I think a lot of those decisions help inform the way that site staff are trained and data is collected over time. Beyond the basics of setting up a system that is going to collect quality data and allow the researchers to interact with it, there's a lot of data vetting that has to go through, so relying on subject matter experts and folks with experience in those particular areas, it's an interesting intersection, where sometimes the person who may be best able to speak to the quality of the data might come from a tech background or a healthcare background where you wouldn't expect that to be the case, typically, so combining interests and having that overlap and Venn diagram and cross pollination of ideas is something that inspires me to work every day and have that overlap and really collaborative effort with a lot of people that I work with and a lot of my other peers in the industry.

Real-World Data Agentic AI 

ACT: Could you share real-world use cases where this technology has significantly accelerated clinical development, improved data integrity, or optimized regulatory compliance?

I think that right now, Agentic AI is a marketing buzz. Last year, there were a lot of new developments, and I do believe that we have the reliable tools and platforms right now to build Agentic AI solutions for the industry. I can call out a couple of use cases that we are very excited to be working with our client sponsors on.

The first one is related to protocol adherence and compliance. For instance, any protocol deviations in terms of missed patient visits, or if it's outside the window, causes a cascading set of delays. We believe that by building a specific agent, the protocol we call it the protocol adherence agent, we are able to address this in real time basis and resolve this before it becomes a huge issue.

The other example is that we’re using it in monitoring overall efficacy of a particular drug and looking at any kind of safety concerns as well. So, we do believe that there is a number of other examples, but these are some of the things that we're working on.

Edetek R&D cloud ecosystem addresses key challenges in clinical data management and analytics by creating a seamless digital data pipeline capable of collecting and transforming data from any source, format, or type. The platform integrates third-party software with event orchestration, ensuring security, collaboration, and scalability.

Agentic AI enhances these solutions by eliminating workflow bottlenecks and improving efficiency beyond traditional AI. While human oversight remains essential, agentic AI automates repetitive tasks and optimizes decision-making in real time. At the SCOPE Summit, Edetek highlighted its use of agentic AI in critical areas such as protocol adherence, where AI agents detect and resolve deviations in real time to prevent cascading delays. Another key application is monitoring drug efficacy and safety, ensuring regulatory compliance while accelerating clinical development.

Interoperability remains a major hurdle in clinical research, and Edetek’s cloud ecosystem addresses this challenge on three levels. First, its semantic integration approach eliminates costly implementations by offering pre-built connectors to over 100 data sources. Second, event-driven system orchestration facilitates seamless collaboration between different platforms. Finally, its capability orchestration model allows stakeholders to customize their cloud environment, enabling a flexible, scalable R&D infrastructure. These innovations position Edetek’s ecosystem as a transformative force in clinical data management, ensuring data integrity, compliance, and operational efficiency.

In an interview with Applied Clinical Trials Associate Editor Don Tracy, Shakthi Kumar, chief strategy & business officer, Edetek, provides examples of how Agentic AI can accelerate clinical development. 

Latest Conference Coverage

SCOPE Summit 2025: Shakthi Kumar Talks Real-World Data Use in Clinical Development

In this video interview, Dominique Demolle, CEO of Cognivia, talks artificial intelligence/machine learning and its potential in gathering patient data. 

Using AI/ML to Improve Patient Adherence

, Dominique Demolle, CEO of Cognivia, discussed challenges in improving patient adherence in clinical trials. She highlighted the reliability of adherence tracking methods, such as pill counts and ingestible sensors, and the need for tailored patient engagement strategies. Some potential solutions to these challenges include the use of artificial intelligence and designing more patient-centric studies.

ACT: Is there potential for the use of artificial intelligence/machine learning (AI/ML) to improve adherence? If so, how?

 I would say definitely, yes. When we start thinking about the adherence, the magical trick would be to be able to predict if a patient is going to be or not adherent when entering the clinical trial or at risk of dropping out or not being adherent. Of course, to achieve that, you need in your recipe two sets of ingredients. The first is to understand the drivers of the non-adherence, and the second one, because it's a multi factorial source of elements, you need to integrate that information and understand the importance of all those different elements, and then you come to machine learning and AI, so yes, definitely it is a way to move forward to better adherence in clinical trials, but you have to capture from the patient themselves. What are the triggers? And there are multiple dimensions, some traits of personality, but health literacy, for example, think about health literacy, know that we have to implement a diversity action plan. Of course, patients coming from a minority might be less educated regarding to medical support, or what is a clinical trial? Why it is important to take drugs at such a such point of time. So, per se, it's all a concept that you have to integrate in your AI model, and then all the other factors, like the motivations, the stress, some patients are stressed about adverse events and will disengage very early, so the idea would be to succeed, to integrate that information using machine learning, using the modeling, and predict a risk for a patient to not be adherent, and then you have that to engage your patient with a more tailored patient engagement strategy. This is how I view AI and how it could improve the retention, this is all what we are looking for. Patients are difficult to recruit. We don't want to screen them out. This would also cost a lot, this would not reflect real world, so we want them to enter the trial, and we want them to keep them in the trial until the end. It's really a win-win for the sponsor, the patients, the investigator, and the stakeholder.

In an interview with ACT senior editor Andy Studna at SCOPE Summit, Rajneesh Patil, vice president, digital innovation, IQVIA, highlights the impact of artificial intelligence/machine in improving outcomes and maintaining safety with its implementation.

SCOPE Summit 2025: AI/ML’s Impact in Patient Care

ACT: At the end of the day, it has to matter how these artificial intelligence/machine learning (AI/ML)-driven solutions are aiding in improving patient health outcomes. Where are you seeing a tangible impact in patient care?

For years now, I think we've been talking about patient centricity. As an industry, as companies, I think there is a level of digital health technology evolution that's happening today in terms of direct to patient in terms of variable sensors, devices, connected devices that can help change the face of how data collection is done in clinical trials today, so that's one area where the patient journey impact of how we even bring a patient into a trial. Are we doing disease detection algorithms better so that we can identify disease burden early in the game, so we can connect to patients the clinical trial options as a care option, which is a big shift that we need to do in the industry. I think AI is helping that conversation, but also in general, the user experience for patients who are participating in trials. We've started in the industry now as common terminology and when a protocol is written measuring patient burden, how do you do that? With your past experience, with the machine learning algorithms of all the data, how long does it take for a patient, for each single site to visit on a trial? I was on a study more recently, where we were designing the protocol, helping the customer, and also learning from them. There was a visit, for example, which was eight hours long, and these are sick patients, so how do we reduce the burden of that visit? By introducing some of the direct to patient elements, home healthcare, nursing into the mix. Telemedicine has been around for a while. COVID helped it peak, so there are those technologies which enable, through AI and intelligence, we can improve the way trial participants are coming into the clinical trials, but also their engagement through the trial period, so that's one area I think we are very focused on improving patient journeys.

ACT: As trial sponsors, CROs and other industry stakeholders work to closely examine the potential of AI/ML for clinical trial success while maintaining patient and data safety and risk mitigation, how much will individual companies and groups need to break down information silos and discuss both benefits and concerns of expanding AI/ML use in clinical R&D?

There's so many layers to that question. We all realize the how loaded that question is, so let me deal with it in three parts. One is exactly like you said, the data silos and functional silos are fine. They were born because of a reason. They were all created to serve one process achieve, one outcome, but as we're seeing, the need for interoperability and integration, meaning data entered in one system, why is it not populating in all the other systems on a study? Basic question, some of the other industries are probably a little more evolved we see. Fintech is an example. Retail is an example. There's a huge opportunity there for us to break down those silos from a data consumption perspective. Breaking down the silos is a good concept, but why and what is the objective driving it will help us get there quicker, smarter, and kind of cheaper, so a lot of technology companies are helping us solve that. The second layer to that is, I think the operational workflow layer, again, the way we build processes in our industry, there's a lot of handoffs. There's a lot of friction when handoffs happen. They're not designed to have friction, but friction happens when there are handoffs, so how do we seamlessly bring some of the workflows together? That's what is the true power of integrating some of the processes and that, again, will help break down silos between, let's say, startup and feasibility. There's a bunch of risks identified there. Does it get carried through to the conduct phase? Does it get carried through the closeout phase? That continuum is something we can use to break down the silos. The third one is something we are exploring very, very dearly to my heart, around the regulatory influence on how some of these models, the delivery models, work and we had the opportunity to talk to a regulatory agency in September, October, on the use of AI in clinical development and their feedback was, I was not surprised, but I was sharing with a lot of my industry colleagues, who, typically, we have the tendency to think regulatory constraints might bog us down, but at the other hand, this interaction helped us actually appreciate that regulatory is actually waiting for us with our sponsors to go to them with new models, show them, of course, the guardrails within which we are operating. What are the risks and how are we mitigating them? FDA released this paper two weeks ago on guidance for AI usage in clinical. There is a seven-step process there on how you can build responsible AI, so using those as catalysts for operationalizing some of the AI capabilities. We are looking to further break down these types of silos, interoperability, integrations, more value creation is waiting at the other end of that, but it will take some effort in the initial days to be able to actually break down the silos, get some of these teams together, get some of our technologies talking to each other, under the back of which we can then harness it with AI. AI by itself will not create the value. The value will come from the data and the processes we have laid out to help us harness that power.

In an interview with ACT senior editor Andy Studna at SCOPE Summit, Rajneesh Patil, vice president, digital innovation, IQVIA, highlights the impact of artificial intelligence/machine learning in improving outcomes and maintaining safety with its implementation.

In an interview with ACT senior editor Andy Studna at SCOPE Summit, Patil, vice president, digital innovation, IQVIA, discusses how artificial intelligence/machine learning can help in areas such as feasibility, site selection, and patient recruitment.

SCOPE Summit 2025: Rajneesh Patil of IQVIA Highlights the Use of AI/ML in Improving Overall Cycle Times and Clinical Development

ACT: Can you share a few examples of how artificial intelligence/machine learning (AI/ML) can mitigate risks that have traditionally impacted clinical trial success?

Sure, while there are quite a few areas which can help improve the overall cycle time and clinical development, we are seeing some specific areas around trial feasibility, so site selection, patient selection, types of algorithms, which are AI driven, are helping us improve on metrics of patient recruitment rates, non-enrolling sites is another key metric, so a lot of our sponsors are looking to solve for these problems. Those are a couple areas. In the pharmacovigilance space which relates to patient safety, monitoring, etc., there is a huge opportunity, and we are seeing some value there in terms of automating some of those workflows in pharmacovigilance. Signal detection is one. These models are great at detecting safety signals if we can prompt them adequately and following that even the workflow automation of some of these next best action types of capabilities is another area where we're seeing significant improvements. There's also something to be said about the clinical operations efficiency overall. We do have, as an industry, looking at productivity and efficiency as goals, so there are several use cases within clinical operations, starting from monitoring at the sites. The administrative burden of document management, for example, is another area we can use AI to do compliance management, do escalation management in terms of working with sites, etc. The clinical operations world, where we see monitoring document compliance, and other administrative processes, those could get a solid benefit from the AI type of use cases.

Artificial Intelligence/Machine Learning

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