Emerging applications of AI/ML, automation, and digitization are helping sponsors cut clinical trial start-up times to as little as four weeks, reduce data errors, and enhance patient engagement—demonstrating how tech-enabled processes are reshaping trial efficiency and experience across the study lifecycle.
The clinical trial industry is quickly becoming more comfortable with using well-designed artificial intelligence/machine learning, automation and digitization to make clinical trials more efficient and to uncover deeper insights that improve patient outcomes. The growing collection of use cases is building evidence of the host of ways new technologies can help accelerate and optimize clinical trials. But while many are aware of common uses of these advances, such as for reviewing vast amounts of scientific literature, other applications may slip under the radar.
Below, we discuss four emerging uses of artificial intelligence/machine learning (AI/ML), automation and other technologies in clinical trials to accelerate study start-up and enhance patient experiences.
The reduction of “white space,” the typically lengthy transition periods between trial phases, is a key point of discussion for trial sponsors concerned about long development timelines and increased costs. AI/ML, and in particular generative and agentic AI, are transforming how sponsors approach trial design and planning to compress timelines.
When engaging in early, iterative planning, ideally up to 12 months before study start-up, sponsors are using AI-driven analytics to simulate multiple design scenarios, benchmark against historical data and assess the downstream impact of study design elements. This proactive approach is leading to more informed, data-driven decisions around eligibility criteria, likely patient and site burdens, and operational feasibility with an aim to minimize amendments and trial delays.
Design experts, AI/ML engineers, therapeutic experts and patient recruitment specialists are collaborating to connect disparate clinical research tools and data sources via for strategic analytical applications to gain initial insights early in the trial design process. Through further iterations on design ideas, the layers of insights can support evidence-driven decision-making, offering a chance to see how the changes may impact protocols. Early on, sponsors are able to gauge potential risks and trade-offs to consider, assessing how their trial design choices may impact downstream processes.
There is also potential for the experts noted above to rely on well-designed AI to synthesize massive libraries of data and draw relationships between defined entities to generate hundreds of potential scenarios. This helps to better understand how differing eligibility criteria and parameters, such as endpoints, study procedures and real-world treatment patterns, may help or hinder study execution. Comparing the “what ifs” of each of these scenarios, sponsors can weigh how each case may play out in real-world use to make trade-offs to improve likelihood of trial success.
As the R&D landscape becomes more complicated, sponsors are also looking for in-depth insights to support rapid detection of relevant trends and related decision-making. It will be essential for clinical research organization partners and technology providers to use expansive, global data and AI to provide evidence-driven guidance and build optimal country and site strategies that balance enrollment speed, timelines and cost. Through tailored tech-enabled approaches, sponsors will be able to explore a range of possibilities with real-time insights about the countries for consideration for the trial. It is possible to develop alternative models based on time and cost using generative AI, allowing sponsors opportunities to optimize country and site selection with evidence-driven decision-making.
Naturally, as the study progresses, sponsors aim to be forward-thinking in planning efforts. Relying on ongoing real-time predictive modelling, they can gauge where to course correct and also proactively develop new targets.
Accelerating study start-up is key for reducing white space. While electronic data capture (EDC) is a critical component of effective start-up strategies, we must take a broader look at advances study start-up to experience true acceleration.
Using tech-enabled processes and AI in the study start-up phase can reduce timelines to as little as four weeks.
Standards are foundational to ensuring consistency and quality across clinical trials. A robust end-to-end standards strategy, spanning data collection, Study Data Tabulation Model (SDTM), analysis datasets and associated metadata (ADaM), and results, drives higher compliance rates, minimizes customization and reduces rework. By embedding quality standards throughout the lifecycle, study teams can streamline processes and accelerate timelines with greater confidence.
A digital protocol plus standards and machine learning libraries opens a world of possibility in start-up processes. The protocol is the master specification of the trial, enabling AI to “read” the protocol and recommend design. For example, an AI algorithm that reads a digital protocol and generates the EDC design for both standard and protocol-specific forms ensures teams can work more productively and focus their time on holistic review of the protocol and data review across all sources.
Today’s trials only collect about 30% of the data in EDC due to increased data collection in labs, electronic clinical outcome assessments (eCOA), wearables, and eSource platforms. This means a comprehensive data review strategy that includes all data sources and all reviews is critical to success. Using AI to read the digital protocol and recommend a comprehensive data review strategy along with automated programming and validation leads to even greater acceleration beyond EDC.
As clinical trials evolve toward more patient-centric data collection technologies, the focus is shifting from reactive data cleaning to proactive, real-time compliance monitoring and risk prediction. AI plays a pivotal role in this transformation by streamlining data flows, identifying potential issues before they arise, and reducing operational white space across the trial lifecycle.
Patient engagement and retention is a persistent challenge that requires strategic attention early in trial planning. Whether it is complex protocols, frequent site visits, logistical hurdles or simply feeling disconnected from the trial’s purpose and personal relevance, there is a multitude of reasons participants may lose interest or drop out.
Gamification in clinical research, or incorporating game-design elements into patients’ experiences, is becoming a valuable strategy to ensure patients remain actively engaged and motivated throughout their trial participation. When thoughtfully designed, it can help patients feel more in control, more informed and more invested in their role in the study.
Gamified features can be tailored to individual preferences and embedded within the trial’s digital platforms. Examples include:
One of the strengths of gamification is its adaptability. Multimedia tools can be customized to suit different therapeutic areas, age groups, cultural backgrounds, and levels of digital literacy. For example, pediatric trials may benefit from colorful, animated interfaces, while adult-focused studies might use more sophisticated progress dashboards and educational content. Sponsors and trial designers can also adjust the complexity and tone of gamified elements to align with the emotional and cognitive needs of specific patient populations. This flexibility ensures that gamification enhances—not distracts from—the core goals of the study.
By making participation more interactive, personalized and rewarding, gamification can transform the trial journey from a passive obligation into an engaging and more meaningful experience—thus, increasing adherence to study protocols and improving the quality of data collected.
Site-based clinical laboratory testing has been slower to adopt electronic data capture than other trial activities. Many sites still rely on paper-based submissions to provide patients’ personal information and sample data to central labs. This results in delayed query resolution, which trickles into the broader trial timeline to adversely impact downstream activities, such as with holds on lab reports, prolonged data reconciliation during cleaning and delayed submissions. Trial sponsors are recognizing the need to update technology for site-based lab testing to improve the quality of workflows and data capture and analysis, and for better real-time oversight.
Use cases indicate a reduction in query rates from 25% when using paper to 1% with electronic requisitions. Also, a single integrated lab application that holds all necessary data allows sites to spend less time identifying paper sources and reviewing paper manuals and to eliminate manually reconciling paper records and multiple systems. Site staff can then dedicate more time to their top priority, providing quality care to patients.
As trial sponsors adopt real-time EDC, they need to understand that it is essential to avoid piling tech-related operational burdens on sites and creating hesitation to use new tools. During trial planning, sponsors, study teams and contract research organization partners need to educate site teams about how daily operations will be streamlined and provide real-time support options to assure that patient visits will be completed when technical issues occur.
Additionally, paper requisitions typically include pre-built kits that may contain unnecessary supplies, such as single-use plastics or tubes. With EDC, it is possible to build kit-agnostic or bulk-kit-type options to help reduce consumption of single-use plastics and enhance sustainable clinical lab practices.
Electronic requisition also allows a bird’s-eye view of inventory management systems and sample tracking to better gauge supply needs and reduce waste.
Looking ahead, the continued evolution of tech-enabled clinical solutions, including AI/ML and automation, promises to further transform clinical trials to be more agile, operationally efficient and patient-centric without compromising quality. However, for these innovations to be effective in real-world settings, human expertise and guidance is essential so that the nuances of therapeutic focus, patient populations, sites, regulations, etc., are accounted for to tailor-fit tech solutions into trial design. How future clinical research can be transformed lies in the synergy between advanced technology and human insight as it unlocks new possibilities for innovation and impact.
Gavin Hershaw, Director, Customer Experience; Natalia Kotchie, Senior Vice President, Applied Data Science Center; Jessica Taylor Jones, Director, Head of Digital Patient Engagement – PRE, Patient and Site Centric Solutions; and Sabrina Steffen, Vice President, Head of Innovation & Data Strategy, Data Sciences, Safety & Medical; all with IQVIA
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