AI is a transformative force in clinical research, and its impact continues to grow. By applying data-driven insights throughout the clinical trial lifecycle, from optimizing study designs to implementing advanced simulation and predictive capabilities, AI can enhance clinical trial planning and execution, saving time, reducing costs, and delivering significantly better outcomes for patients.
Growing need for AI
Clinical trials are becoming increasingly decentralized, innovative in design, and increasingly complex due to a significant increase in the number of endpoints. Phase 3 clinical trials now collect an average of 3.6 million data points, an increase of more than seven times over the past 20 years. Traditional methods are not sufficient to keep up with this pace of change. Instead, strategically deploying AI tools can help companies overcome these complexities, increase efficiency, reduce costs, and accelerate drug development timelines.
Practical application of AI in clinical trials
Rather than choosing to use a single AI tool to assist with a specific task, we are now at a point where AI can be fully integrated into the entire clinical trial lifecycle to ensure intelligent, data-driven decision-making is embedded throughout the process.
Optimization of test design
AI is revolutionizing clinical trial design, significantly accelerating timelines even before the first patient is enrolled. AI models leverage historical data to rigorously test proposed inclusion/exclusion criteria and predict their impact on patient recruitment and retention. These models can also identify patient subgroups at high risk for dropout or adverse events and proactively address problems before they occur. This early integration of AI will not only increase efficiency across clinical programs, but also accelerate progress on the most promising molecules. In one example, an AI solution helped shorten oncology trial timelines by at least a year by using data to correlate early outcomes with long-term survival rates. Additionally, many AI users I work with report improvements in protocol design, site feasibility, and cohort identification.
Furthermore, AI ensures that study designs seamlessly align with regulatory expectations. Identify successful endpoints used by competitors and accepted by regulators, determine the most appropriate standard-of-care comparisons, and suggest innovative alternative endpoints. This significantly increases the drug’s chances of approval and reimbursement.
Reduce cost issues
AI has dramatically reduced the marginal cost of generating critical insights. A few years ago, extracting a single meaningful insight from a large data set might have taken a human expert months. Now, AI co-pilots with a deep understanding of both the data and historical context are enabling the same team to perform multiple analyzes in the same time frame with minimal additional cost. While human expertise remains paramount, AI greatly increases the value of that productivity.
Anticipate potential problems
Mid-study changes can add significant time and cost to clinical trials if handled incorrectly. Delays in clinical trials due to these issues cost companies approximately $40,000 per day and result in approximately $500,000 in lost revenue per study. However, advanced AI-powered “what-if” simulations accurately assess the potential impact of these changes on factors such as study enrollment and timelines, ensuring their implementation is strategic and maximally efficient.
In addition, AI provides real-time performance monitoring, benchmarks against predictions and similar past tests, and predicts potential safety events. This proactive warning allows for early identification and mitigation of problems, minimizing downstream impacts and empowering businesses to make the right decisions today to prevent problems tomorrow.
Human oversight of these tools will remain essential, as AI is intended to supplement human judgment rather than completely replace it. By identifying potential problems early on, investigation teams can spend more time on critical thinking and decision-making, rather than addressing problems after they arise.
The importance of good data
Data quality remains a major hurdle when applying AI models to clinical trials. A model is only as good as the data used to train it. Therefore, for AI models to be reliable, accurate, effective, and avoid bias, it is absolutely essential that they are reliable, accurate, and truly representative of the task at hand. This is a major concern for major regulators. For example, bias in the training dataset (i.e., lack of demographic representation of certain subgroups) can result in an AI model making recommendations that do not apply to certain populations or groups.
Having a large repository of historical trial data provides comprehensive information on successes and failures across different therapeutic areas, geographies, and study sizes, as well as patient-level information.
Through continuous and responsible innovation, AI has the transformative power to reduce future risks in clinical trials and across the healthcare ecosystem, permanently increasing efficiency while delivering safer and better patient experiences.
