How AI Could Revolutionize Clinical Trials and Break Pharma’s Cost Crisis
Artificial Intelligence & Machine Learning News
Clinical trials are now the most expensive and time-consuming part of drug development, consuming up to 60 to 70 percent of total costs. Bringing a new drug to market can take anywhere from seven to fifteen years, and fewer than 12 percent of drugs that enter human testing ever receive approval. This has created a bottleneck that threatens not just pharmaceutical profitability, but global innovation in healthcare. The need for smarter, faster, and more efficient clinical trials has never been more urgent.
Erik Terjesen, Managing Director at Silicon Foundry, believes the solution lies not in discovering new molecules faster but in reimagining how drugs are tested. His work centers on helping pharmaceutical leaders identify and partner with AI-driven startups that are redefining the clinical trial process—from virtual patient simulations to adaptive trial design and real-time oversight.
The True Bottleneck: Clinical Trials, Not Discovery
For years, AI headlines in life sciences have focused on drug discovery. Companies like Insilico Medicine and BenevolentAI have shown how machine learning can identify promising compounds faster than traditional chemistry ever could. But once a potential drug candidate reaches human testing, innovation slows dramatically.
“Discovery is sexy, but trials are where time and money are lost,” says Terjesen. “AI can help transform that by redesigning how we collect, analyze, and act on patient data in real time.”
Clinical trials face challenges that go beyond scientific uncertainty. Recruitment, data collection, compliance, and patient retention often cause costly delays. With AI, these pain points can be addressed head-on. Machine learning can identify ideal trial sites, predict patient drop-off, and even flag data anomalies before they compromise study validity.
Enter the Digital Twin: Virtual Patients as the New Frontier
One of the most promising innovations is the rise of digital twins—virtual models of patients built from real-world data. These AI-generated simulations mimic a patient’s physiology, genetics, and medical history, allowing researchers to test hypotheses without exposing real patients to risk.
Digital twins can help model drug interactions, predict side effects, and optimize dosing strategies long before a trial begins. “Imagine being able to run thousands of trial simulations before enrolling your first patient,” says Terjesen. “That’s the power of AI-driven digital modeling. It can drastically reduce the number of physical participants needed, shorten timelines, and lower costs.”
The potential impact is enormous. A study from Deloitte estimates that integrating AI into clinical trials could save the industry tens of billions of dollars annually. Beyond cost, these tools can improve safety and equity by including virtual models of patients from underrepresented populations—something traditional trials often struggle to achieve.
Smarter Trial Design and Real-Time Oversight
AI isn’t just helping with virtual simulations; it’s transforming how trials are designed and monitored. Adaptive AI systems can analyze live data from ongoing studies and suggest adjustments to enrollment criteria, dosage levels, or trial endpoints. This real-time responsiveness turns static, rigid protocols into dynamic, learning systems.
“Most clinical trials today still operate on designs that were written months or even years ago,” Terjesen notes. “AI can make them adaptive—so when early data shows a pattern, you can pivot immediately rather than waiting for a formal interim analysis.”
Such capabilities not only save time but can also improve patient safety. If AI detects unexpected side effects in one subgroup, the system can flag the risk before it escalates. Similarly, predictive analytics can identify which patients are most likely to respond to a drug, allowing for more targeted and successful trials.
Breaking Through Data and Regulatory Barriers
Despite its promise, AI faces significant hurdles in the clinical trial ecosystem. One major challenge is data fragmentation. Medical data remains scattered across hospitals, insurers, and research institutions, often locked behind incompatible systems or privacy regulations. Integrating this data into AI models requires massive coordination and compliance effort.
Then there’s the regulatory landscape. Agencies like the FDA are cautiously optimistic about AI, but validation remains a complex process. Models must demonstrate transparency, explainability, and consistent performance before they can influence official trial decisions.
