The average Phase III study generates a staggering 3.4 million data points . That’s more data than a human team could process manually in a lifetime. And while data collection has become easier, extracting meaningful, actionable insights is often the most challenging part.
Fortunately, the advent of artificial intelligence (AI) has presented researchers with a multitude of ways to extract value from this information goldmine.
During a recent webinar, Laura Trotta, Vice President of Research at CluePoints, shared key strategies for using AI to support high-quality clinical trials.
Table of Contents
Traditional Challenges in Clinical Trial Oversight
How AI is Transforming in Clinical Trials
Core Applications of AI in Clinical Research
Centralized Monitoring
Centralized Statistical Monitoring (CSM)
Machine Learning (ML) & Deep Learning (DL)
How AI Enhances Key Clinical Trial Functions
Medical Coding
Risk Detection & Prioritization
Clinical Safety Signal Detection
The Future of AI in Clinical Research
Traditional Challenges in Clinical Trial Oversight
Historically, clinical teams have relied on manual processing and analysis methods, such as 100% source data verification (SDV) and manual data listings, to ensure trial quality. These approaches are time-consuming, prone to human error, and expensive.
For example, SDV corrects just 1.1% of electronic case report form (eCRF) entries, yet generates an enormous operational burden. The cost of a single query ranges from $28 to $225, and a Phase III study can create tens of thousands of them.
Given these diminishing returns, there’s an urgent need for innovative, scalable solutions. The inefficiencies of traditional methods make it harder to maintain quality while controlling costs, especially as studies grow in complexity.
How AI is Transforming Clinical Trials
AI offers powerful tools for improving data quality, trial efficiency, and oversight. By learning from large datasets, AI can uncover patterns and anomalies that traditional methods may miss, helping sponsors and contract research organizations (CROs) make faster, more informed decisions.
Traditional processing and analysis approaches are often inefficient, contributing to lengthy development timelines and driving up costs. It currently takes over a decade and costs about $1 billion to $2 billion to bring a new medicine to market. Despite the significant expenses associated with these processes, manual or automatic queries affect less than 1.7% of entries.5
AI fills this oversight gap, allowing teams to monitor quality at scale without added resource strain. It empowers teams to shift from reactive quality control to proactive quality assurance; catching problems early, often before they impact patient safety or trial integrity.
By integrating AI tools with risk-based quality management (RBQM) frameworks, clinical operations teams can maximize the value of collected data and accelerate timelines without compromising compliance.
Core Applications of AI in Clinical Research
Recent years have seen the emergence of advanced statistical techniques and machine learning methods designed to address critical clinical research challenges.
CENTRALIZED MONITORING (CM)
Centralized monitoring enables remote clinical and operational trial data oversight across all study sites. By monitoring in near-real-time, sponsors can quickly identify and address potential issues before they affect data quality.
AI enhances this process by recognizing subtle patterns, trends, or site-level anomalies faster than traditional methods. These insights allow teams to take corrective action earlier, which protects both the trial and its participants.
CENTRALIZED STATISTICAL MONITORING (CSM)
Centralized statistical monitoring (CSM) applies statistical algorithms to identify outliers and discrepancies in clinical trial data. These techniques support a more proactive data-driven form of risk-based quality management (RBQM).
By integrating AI, sponsors gain deeper insight into deviations, site variability, and emerging risks, ultimately accelerating timelines and reducing oversight burden. AI technologies can enhance efficiency at every stage of the drug development pathway, from data oversight to decision-making. With smarter anomaly detection, sponsors can improve site performance monitoring, reduce errors, and mitigate risks that might otherwise go unnoticed.
MACHINE LEARNING (ML) & DEEP LEARNING (DL)
Machine learning (ML) enables computers to learn from data without being explicitly programmed, while deep learning (DL) uses layered neural networks to analyze structured and unstructured data.
ML and DL power a wide range of applications in clinical trials, including:
- Predictive analytics for patient safety
- Text mining of unstructured reports
- Pattern recognition in operational and clinical datasets
These tools are particularly powerful when applied to large-scale, complex datasets generated throughout a trial. They also support scalability, enabling the same intelligent systems to be applied across multiple studies, therapeutic areas, or even entire pipelines.
How AI Enhances Key Clinical Trial Functions
At CluePoints, we are actively developing ML and DL tools designed to improve trial quality and efficiency. Our ultimate goal is to bring safer, more effective treatments to patients faster. These tools can support rapid, accurate medical coding, improve risk detection and prioritization, and identify clinical safety signals.
MEDICAL CODING
Previously, mapping adverse events and concomitant medications to MedDRA or WHODrug dictionaries was a manual, time-consuming process.
CluePoints’ deep learning-based medical coding model, already more than 90% accurate, now guides researchers to the correct term in seconds. This drastically reduces coding errors and saves time across the study lifecycle.
In addition to speeding up the coding process, this model increases site consistency and reduces discrepancies during regulatory review.
RISK DETECTION & PRIORITIZATION
RBQM platforms raise “risk signals” when anomalies are detected in trial data. These signals are documented and tracked using free text fields, often filled by different users investigating the issue.
With insights from over 1,200 monitored clinical trials, CluePoints is training a model to identify signals more likely to represent real, actionable issues. This approach helps teams prioritize signal reviews and properly conduct follow-up investigations.
By triaging risk signals based on likelihood and severity, AI helps study teams focus their attention on the most critical issues first: saving time and improving decision-making.
CLINICAL SAFETY SIGNAL DETECTION
CluePoints is exploring how deep learning can support clinical safety signal detection. These models are designed to behave like human reviewers, capable of forming medical hypotheses and identifying subtle patterns across the dataset.
If successful, this approach could transform pharmacovigilance by providing earlier warnings and deeper insight into potential safety concerns.
The ability to analyze cumulative safety data over time with AI opens new doors for identifying trends that human reviewers may not spot until much later. This means better protection for patients and more robust data for regulators.
The Future of AI in Clinical Research
At CluePoints, we’re excited by the potential of AI and other emerging technologies to improve clinical trial design, oversight, and outcomes.
Clinical trials generate vast volumes of data, and as the stewards of that information, it’s our responsibility to use it wisely.
As data volumes continue to grow, AI will be indispensable to delivering faster, safer, more efficient clinical trials, empowering teams to focus on outcomes that truly matter.
By continuing to invest in AI development, we’re helping the industry evolve toward a future where every trial is more intelligent, efficient, and patient-focused.
Want to learn more?
Explore CluePoints’ AI-powered solutions for clinical trial management and clinical trial analytics, or contact us to learn how we can support your next clinical trial.
REFERENCES
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2 Wouters, O. J., McKee, M., & Luyten, J. (2020). Estimated research and development investment needed to bring a new medicine to market, 2009-2018. Jama, 323(9), 844-853.
3 Stokman, P. G., Ensign, L., Langeneckhardt, D., Mörsch, M., Nuyens, K.,Herrera, D., Hochgräber, G., Cassan, V., Beineke, P., Kwock, R Voortman, A., Vogelgesang, S., Boussetta, S. & Bitzer, B.(2021). Risk-based Quality Management in CDM An inquiry into the value of generalized query-based data cleaning. Journal of the Society for Clinical Data Management,1.
4 Mohs, R. C., & Greig, N. H. (2017). Drug discovery and development: Role of basic biological research. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 3(4), 651-657.
5 Congressional Budget Office (2021). Research and Development in the Pharmaceutical Industry. https://www.cbo.gov/publication/57025.
6 Sheetz, N., Wilson, B., Benedict, J., Huffman, E., Lawton, A., Travers, M., Nadolny, P., Young, S., Given, K., & Florin, L. (2014). Evaluating Source Data Verification as a Quality Control Measure in Clinical Trials. Therapeutic Innovation & Regulatory Science, 48(6), 671–680.
