AI in Clinical Trials: From Promise to Practice

The clinical trial landscape is undergoing a profound transformation, with artificial intelligence (AI) at its core. No longer a futuristic concept, AI has become a practical and applied force, reshaping every phase of clinical research. This article explores a selection of AI-driven technologies already in active use and how they are redefining drug and device development.

One of the most prominent commercial tools is the platform developed by Israeli HealthTech company QuantHealth , which helps design precise, data-driven clinical protocols. Their system simulates trial outcomes, predicts primary endpoint results, and flags risks of poor design or under-recruitment – all based on a vast dataset of real patient information. According to the company, their model achieves approximately 85% accuracy and has reduced planning time by up to six months. Biotech firms and investors have already adopted the platform.

Patient recruitment a known bottleneck in clinical trials, has also benefited from operational AI tools. U.S. based companies such as Deep 6 AI , Leal Health , and Antidote deploy advanced algorithms to mine electronic health records, identifying eligible participants with impressive speed and accuracy. For example, Deep 6 AI reports that in an oncology trial, its system identified 36 suitable patients within 45 minutes – compared to only 30 patients found manually after screening over 5,000 records across two months.

In another groundbreaking application, Unlearn.AI is redefining control groups using “digital twins” – computational models that simulate how a patient’s disease would progress without treatment. These twins evolve in parallel with real participants, allowing partial or full virtual control arms that reduce double recruitment and enhance patient experience. According to the company, this approach can reduce control group size by ~33% and shave off four months of recruitment in Phase 3 trials, without compromising statistical power.

One more transformative shift in clinical research today is the rise of Decentralized Clinical Trials (DCTs), where data is collected outside traditional sites – often directly from patients’ homes, using wearable devices and AI-powered platforms.

A standout example is the solution developed by Biofourmis, powered by its proprietary Biovitals™ Analytics Engine. In combination with wearable sensors provided by Ametris, the system continuously analyzes vital signs using AI trained on real-world data from thousands of patients. This approach represents more than a technical upgrade – it marks a shift from reactive care to predictive, proactive patient safety. According to Biofourmis, implementation of its platform has led to a 70% reduction in 30-day hospital readmissions, clinical deterioration detected 21 hours earlier, and up to a 38% reduction in cost of care.

Looking ahead, several partially implemented technologies are poised to become standard. For instance, anomaly detection systems powered by AI, which alert study teams to data deviations in real time, are already being piloted with pharma partners. Similarly, AI integration within EDC and CTMS platforms is entering commercialization. Medidata and Veeva have begun offering predictive tools and smart operational assistance, with significant expansion expected in the next two years.

On the regulatory front, the FDA is actively promoting the integration of AI within digital health products and is regularly updating its guidance for AI-driven medical devices. Requirements include transparency, documentation, and traceability of decision-making processes. Even AI solutions operating behind the scenes such as patient-matching engines, risk prediction, and trial success modeling must meet high standards of data quality (GxP), privacy, and cybersecurity, even if they don’t require formal approval. In conclusion, AI in clinical research is no longer theoretical. It is an expanding set of real-world tools that deliver measurable value. A full index of current AI technologies in clinical trials is available upon request.