By Alla Smyshlyaeva, Senior Director, Proposal Management & Alistair Neally, Director, ProposalsÂ
Last week, we made our annual pilgrimage to the SCOPE Summit to join the conversation about the trends impacting the clinical research landscape in 2025. Artificial intelligence (AI) continues to be the topic on everyone’s lips, and our data science team was happy to see AI for Clinical Trials added this year. It was interesting to observe the evolution of the AI conversation from “ChatGPT is awesome – have you tried it yet?” in 2023 to “AI will solve all the problems” in 2024, and the more sobering “AI is a tool in the toolbox – let’s see how we can use it best” this year.
Like everywhere in the world, technology is way ahead of humans in clinical trials as well, and it was great to see huge progress in transforming data into knowledge with new technology. Below, we’ve listed some of the innovations PSI found most exciting.
Digital Protocols
The promise of digital protocols is tremendous: faster decision-making, faster design time, and increased operational efficiency.
Companies have made great strides in merging digital protocols with AI to support clinical operations. PSI’s launch of INTELIA™ CORE™ is also imminent. It will produce automated schedules of assessments, Case Report Forms (CRFs), investigator grant templates, lab specifications, and cohort and study design mapping, to name a few highlights.
AI-driven Document Management
Informed Consent Form (ICF), Clinical Study Report (CSR), and protocol writing are the key focus. Still, it’s critical to create the architecture to manage all kinds of documents, not just clinical documents.
Enrollment and Cost Optimization
Although there are no industry-wide metrics on trial delays, it’s evident patient enrollment is still the industry’s code to crack. Lots of companies cite from 20% to 90% of studies being delayed, with 80% as one of the most frequently used metrics.
There are lots of technological attempts to solve this problem, including enrollment prediction at the study planning phase. Using predictive analytics and AI, we can model the future and help decrease the likelihood of delays and change orders. For example, VISIONAL™ uses a combination of machine learning for budget forecasting, business-weighted constraint-based algorithms for scenario modeling, and generative AI (GenAI) for protocol similarity search and enrollment rate predictions.
Use of AI and Other Technology to Reduce Costs and Increase Employee Motivation
One interesting example where the use of technology, including AI, is helping increase efficiency, reduce time, save money and improve focus is the approach to site monitoring and reporting. Everyone in the industry knows clinical operational costs are the most expensive part of a trial budget. By standardizing site monitoring visits and uploading the data so that site reports are generated faster using a tablet device, AI can then be deployed to review and analyze those multiple reports more quickly to identify anomalies and flag potential issues — or even help those visits that may end up susceptible to findings or potential for inspection.
Providing staff with the tools and a standard approach will positively impact motivation, reduce time, increase productivity, and lower costs. The key message from the conference is that AI is here, and we must all adopt it. As one attendee remarked, “AI will not replace scientists; however, scientists using AI will replace those who don’t.”
AI to Help Reduce Repetition in Biosimilar Trials
An interesting topic, particularly with the U.S. Food and Drug Administration (FDA) and other regulatory authorities keen on the adoption of AI, is the concept of reducing patient and site burden using AI-driven data to help demonstrate how a particular drug behaved in patients from previous trials. This knowledge could be used to help redesign biosimilar studies and reduce the number of patients needed in the control arm for future trials. Could future studies of this nature be re-engineered and become more efficient in time and cost?
Similarly, are we about to see AI-driven intelligence review imaging results to help speed up the review time, working with industry-leading vendors?
Site Selection in One Click
Site selection is another pain point, with 10-50% non-enrolling sites across the industry. The ultimate goal is site selection at the speed of thought:
- Automated pre-selection of sites to match the protocol
- Automated feasibility questionnaire to collect only the missing data
- Automated pre-selected site list
Our Site Knowledge platform will incorporate many of these capabilities. We look forward to the release in April this year.
Knowledge Graphs for Deeper Insights
It’s an understatement to say that clinical trial data is complex. We see a lot of potential in adding knowledge graphs for deeper insights, and it was great to see how other companies are also using natural language processing and knowledge graphs to make knowledge more accurate and immediate.
Data-driven Optimization of Inclusion/Exclusion Criteria
Several companies presented innovations in this area. Based on the historical and demographic data, we can predict which inclusion and exclusion criteria will be the bottlenecks in enrollment, simulate outcomes, and decide if they’re there just as a carry-over from previous protocols or an absolute necessity.
Different Vendors Attending in Future
With the adoption of AI in the clinical space, we are all waiting to see what will happen. We know it will be faster than before and suspect we will see a significant change in the types of vendors attending such conferences in the future, with more companies focused on delivering clinical trials more quickly with AI. Here at PSI, the same can be said; with the initial adoptions of products and activities incorporating AI and the number of initiatives in the works, our AI revolution is well underway. Interested in learning more? Get in touch with us today.
SCOPE 2025 Highlights: AI, Digital Protocols & Other Innovations We’re Excited About
By Alla Smyshlyaeva, Senior Director, Proposal Management & Alistair Neally, Director, ProposalsÂ
Last week, we made our annual pilgrimage to the SCOPE Summit to join the conversation about the trends impacting the clinical research landscape in 2025. Artificial intelligence (AI) continues to be the topic on everyone’s lips, and our data science team was happy to see AI for Clinical Trials added this year. It was interesting to observe the evolution of the AI conversation from “ChatGPT is awesome – have you tried it yet?” in 2023 to “AI will solve all the problems” in 2024, and the more sobering “AI is a tool in the toolbox – let’s see how we can use it best” this year.
Like everywhere in the world, technology is way ahead of humans in clinical trials as well, and it was great to see huge progress in transforming data into knowledge with new technology. Below, we’ve listed some of the innovations PSI found most exciting.
Digital Protocols
The promise of digital protocols is tremendous: faster decision-making, faster design time, and increased operational efficiency.
Companies have made great strides in merging digital protocols with AI to support clinical operations. PSI’s launch of INTELIA™ CORE™ is also imminent. It will produce automated schedules of assessments, Case Report Forms (CRFs), investigator grant templates, lab specifications, and cohort and study design mapping, to name a few highlights.
AI-driven Document Management
Informed Consent Form (ICF), Clinical Study Report (CSR), and protocol writing are the key focus. Still, it’s critical to create the architecture to manage all kinds of documents, not just clinical documents.
Enrollment and Cost Optimization
Although there are no industry-wide metrics on trial delays, it’s evident patient enrollment is still the industry’s code to crack. Lots of companies cite from 20% to 90% of studies being delayed, with 80% as one of the most frequently used metrics.
There are lots of technological attempts to solve this problem, including enrollment prediction at the study planning phase. Using predictive analytics and AI, we can model the future and help decrease the likelihood of delays and change orders. For example, VISIONAL™ uses a combination of machine learning for budget forecasting, business-weighted constraint-based algorithms for scenario modeling, and generative AI (GenAI) for protocol similarity search and enrollment rate predictions.
Use of AI and Other Technology to Reduce Costs and Increase Employee Motivation
One interesting example where the use of technology, including AI, is helping increase efficiency, reduce time, save money and improve focus is the approach to site monitoring and reporting. Everyone in the industry knows clinical operational costs are the most expensive part of a trial budget. By standardizing site monitoring visits and uploading the data so that site reports are generated faster using a tablet device, AI can then be deployed to review and analyze those multiple reports more quickly to identify anomalies and flag potential issues — or even help those visits that may end up susceptible to findings or potential for inspection.
Providing staff with the tools and a standard approach will positively impact motivation, reduce time, increase productivity, and lower costs. The key message from the conference is that AI is here, and we must all adopt it. As one attendee remarked, “AI will not replace scientists; however, scientists using AI will replace those who don’t.”
AI to Help Reduce Repetition in Biosimilar Trials
An interesting topic, particularly with the U.S. Food and Drug Administration (FDA) and other regulatory authorities keen on the adoption of AI, is the concept of reducing patient and site burden using AI-driven data to help demonstrate how a particular drug behaved in patients from previous trials. This knowledge could be used to help redesign biosimilar studies and reduce the number of patients needed in the control arm for future trials. Could future studies of this nature be re-engineered and become more efficient in time and cost?
Similarly, are we about to see AI-driven intelligence review imaging results to help speed up the review time, working with industry-leading vendors?
Site Selection in One Click
Site selection is another pain point, with 10-50% non-enrolling sites across the industry. The ultimate goal is site selection at the speed of thought:
Our Site Knowledge platform will incorporate many of these capabilities. We look forward to the release in April this year.
Knowledge Graphs for Deeper Insights
It’s an understatement to say that clinical trial data is complex. We see a lot of potential in adding knowledge graphs for deeper insights, and it was great to see how other companies are also using natural language processing and knowledge graphs to make knowledge more accurate and immediate.
Data-driven Optimization of Inclusion/Exclusion Criteria
Several companies presented innovations in this area. Based on the historical and demographic data, we can predict which inclusion and exclusion criteria will be the bottlenecks in enrollment, simulate outcomes, and decide if they’re there just as a carry-over from previous protocols or an absolute necessity.
Different Vendors Attending in Future
With the adoption of AI in the clinical space, we are all waiting to see what will happen. We know it will be faster than before and suspect we will see a significant change in the types of vendors attending such conferences in the future, with more companies focused on delivering clinical trials more quickly with AI. Here at PSI, the same can be said; with the initial adoptions of products and activities incorporating AI and the number of initiatives in the works, our AI revolution is well underway. Interested in learning more? Get in touch with us today.
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