We live in a rapidly evolving digital era, driven by an ongoing wave of groundbreaking technological innovations. Clinical research is no exception. In recent years, the industry has increasingly embraced technology to manage trials with greater efficiency, precision, and creativity. While many advancements have contributed to this transformation, two stand out as key drivers of change: Artificial Intelligence (AI) and Machine Learning (ML).
In this piece, one of our ML Engineers, Nicolas Huet, explores the world of AI and ML: what these technologies truly mean, how they function, and how they complement each other to shape the future of clinical research.
Table of Contents
What’s the Difference Between AI & ML?
  Artificial Intelligence (AI)
  Machine Learning (ML)
What Does ML Look Like in Practice?
How Does CluePoints’ Risk-Based Quality Management (RBQM) Platform Leverage ML?
  Use Case: Detecting Risk Signals That Represent Actual Study Issues
How Is ML Shaping the Future of Clinical Trials?
What’s the Difference Between Artificial Intelligence (AI) & Machine Learning (ML)?
Artificial Intelligence (AI)
Artificial Intelligence is the overarching goal: to develop systems or algorithms capable of mimicking human behavior in specific contexts. AI essentially aims to replicate human-like decision-making, perception, and reasoning.
This broad objective includes subfields such as Computer Vision, which enables machines to interpret and understand visual inputs like images and videos, and Natural Language Processing (NLP), which allows systems to understand, analyze, and generate human language.
Importantly, the concept of AI doesn’t prescribe a specific method or approach. It defines what we want to achieve, not how to achieve it.
Machine Learning (ML)
ML is one of the primary methods we use to achieve AI. It involves training algorithms on data tailored to a specific task, allowing the system to learn patterns, make predictions, or perform actions based on that data—without being explicitly programmed for every scenario.
A well-known early example of AI is Deep Blue, the IBM system that defeated world chess champion Garry Kasparov in 1997. Deep Blue used a tree search algorithm1 to evaluate millions of potential moves—an approach rooted in rule-based programming, not ML.2
Today, however, most state-of-the-art game-playing systems, like those developed by DeepMind for Go and other games, rely heavily on ML techniques. Looking ahead, it’s possible that new approaches may eventually surpass ML, but for now, ML remains the dominant force powering advancements in AI.
What Does Machine Learning (ML) Look Like in Practice?
ML involves algorithms that can identify meaningful patterns and correlations within data and then use those patterns to make decisions or predictions about new, unseen data. The process of discovering these patterns is known as the training phase or learning phase.
To train an ML model, you must provide a dataset carefully curated for your specific task. For instance, if your goal is to build an algorithm that can recognize images of cats, you would train the model using a large and diverse set of images—some containing cats and others not. These images should feature cats of different breeds, in various poses, lighting conditions, and backgrounds. This diversity helps the algorithm learn the distinctive features of a cat—such as whiskers, paws, tails, and ear shapes—so that it can generalize well to new images it has never seen before.
ML encompasses a wide range of techniques, from simpler methods like linear regression to more advanced architectures such as:
- Transformer models are used in natural language processing and other complex tasks.3
- Generative Adversarial Networks (GANs) often generate synthetic data like images or audio.4
- Convolutional Neural Networks (CNNs) are especially effective for image recognition tasks like the cat example above.
Choosing the right ML method depends entirely on the task at hand. Some algorithms are better suited for image classification, while others excel at language translation, time series forecasting, or recommendation systems.5
How Does CluePoints’ Risk-Based Quality Management (RBQM) Platform Leverage Machine Learning (ML)?
CluePoints integrates ML across its RBQM platform to enhance automation, streamline user experience, and surface meaningful insights from complex clinical trial data. ML is currently applied in two primary areas:
- User Experience Management: ML enables automation of key tasks within the CluePoints platform—such as grouping risk signals and configuring centralized monitoring setups. By reducing the need for manual intervention, these ML-powered features improve platform usability, speed up workflows, and allow users to focus on higher-value activities.
- Knowledge Retrieval: One of ML’s greatest strengths is its ability to extract patterns and insights from large, unstructured datasets. At CluePoints, ML algorithms analyze data from past studies to uncover valuable learnings. These insights are then surfaced to platform users—Sponsors and CROs—empowering them to more effectively plan, manage, and document their studies with historical context and evidence-based recommendations.
Given the volume, variability, and complexity of data processed through the CluePoints platform, ML presents numerous opportunities to deliver added value. The team continues to explore and implement new ML-driven capabilities to support smarter, faster, and more accurate quality management.
Use Case: Detecting Risk Signals That Represent Actual Study Issues
One ongoing project launched this year demonstrates a powerful application of ML in signal classification.
In the CluePoints platform, users can create risk signals whenever a potential issue is suspected during a clinical study. These signals guide monitoring activities and investigations, which are then documented by users in free-text form.
To enhance this process, CluePoints developed and trained a deep learning model capable of analyzing these free-text findings. The model identifies which signals likely represent true study issues requiring corrective or preventive action. This model has the potential to:
- Automatically flag high-priority signals for follow-up
- Prioritize review efforts based on the likelihood of actual issues
- Improve the effectiveness and consistency of documentation and decision-making
As development continues, this tool could significantly strengthen risk-based monitoring by making the platform more proactive and intelligent in how it handles study data.
How Is Machine Learning (ML) Shaping the Future of Clinical Trials?
Predicting the future in a field as complex and rapidly evolving as clinical research is never simple. However, recent breakthroughs in healthcare technology,6 particularly in natural language processing (NLP),7 for mining unstructured healthcare data, make it increasingly clear that ML will play a growing role in shaping the future of clinical trials.
One immediate area of impact is data management. Clinical trials generate enormous volumes of data, which still require manual processing.8 ML can help automate many of these labor-intensive tasks, such as data entry validation, anomaly detection, and consistency checks, which remain costly and time-consuming when performed by hand. By introducing intelligent automation, ML can significantly reduce operational costs, improve data quality, and accelerate timelines for sponsors and CROs.
However, several critical challenges must be addressed to realize the full potential of ML in real-world clinical applications.9
- Model Robustness in Production: ML models that perform well in controlled research settings may behave unpredictably in live clinical environments. Ensuring reliability and performance at scale is a key hurdle.
- Privacy Protection: Safeguarding sensitive patient data is paramount. Applying ML while adhering to stringent data privacy regulations (e.g., HIPAA, GDPR) is a complex but essential task.
- Model Interpretability: In regulated industries like clinical research, it’s not enough for a model to be accurate—it must also be explainable. Decision-makers need transparency to trust and validate the model’s outputs.
- Ethical Considerations: From biased training data to unintended consequences, ethical concerns around fairness, accountability, and patient safety remain central to any ML deployment in healthcare.
These challenges are not unique to clinical trials, but their implications are amplified in this domain due to the life-critical nature of the work and the regulatory scrutiny involved. Still, the path forward is promising. As ML technology matures and the healthcare ecosystem adapts, its role in driving innovation, efficiency, and precision in clinical trials will only continue to grow.
Want to learn more about how AI can elevate your clinical research? Discover how CluePoints’ RBQM harnesses the power of advanced analytics and AI to improve data quality, accelerate timelines, and enhance oversight. Explore our RBQM solutions or connect with our team to see how we can support your next study.
Click here to download our Ultimate Guide to RBQM to explore the fundamentals, benefits, and real-world applications of this transformative approach.
REFERENCES
1. M. Campbell, et al. (2002), Deep Blue, Artificial Intelligence 134 57–83
2. D. Silver, et al. (2018), A general reinforcement learning algorithm that masters chess, shogi, and go through self-play, Science. 362 (6419) 1140–1144
3. I. Polosukhin, et al. (2017), Attention Is All You Need, arXiv:1706.03762
4. I. J. Goodfellow, et al. (2014), Generative adversarial nets, In Proceedings of NIPS 2672–2680
5. A. Krizhevsky, et al. (2012), Imagenet classification with deep convolutional neural networks, In NIPS 1097–1105
6. P. Shah, et al. (2019), Artificial intelligence and machine learning in clinical development: A translational perspective. NPJ Digit Med 2, 69
7. T. Brown, et al. (2020), Language Models are Few-Shot Learners, arXiv:2005.14165
8. Society for Clinical Data Management (2020), The Evolution of Clinical Data Management to Clinical Data Science, Part 2.
9. M. Brundage, et al. (2020), Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims, arXiv:2004.07213
