5 Ways AI Is Changing Clinical Research — And How To Embrace It
Clinical research is being shaped by AI, from trial design to patient reminders. AI is becoming infrastructure, reshaping workflows, speeding up processes, and automating mundane tasks. By staying current and mindful of AI use, we can augment skilled workers rather than replace them.
Where AI Is Making A Real Impact
-
Patient Recruitment and Enrollment AI can review millions of electronic health records (EHRs) using natural language processing (NLP) to identify eligible participants. Tools like Deep 6 AI and Inato have shown significant matching accuracy. However, real-world gains depend on clean EHRs and thoughtful inclusion criteria.
-
Protocol Design and Trial Planning Machine learning (ML) algorithms analyze past trials and real-world evidence to optimize study protocols. Platforms like Saama and nference help teams identify ideal endpoints and simulate trial outcomes, resulting in more likely successful protocols.
-
Site Selection and Feasibility AI models triangulate EHRs, provider density, and regional demographics to predict site performance. Tools like Power convert complex protocol criteria into plain language, improving enrollment and reducing trial delays.
-
Trial Monitoring and Data Quality AI supports risk-based monitoring by identifying anomalies in real-time, such as delayed data entries or missing visits. Some systems compare case report forms (CRFs) with source documentation to catch discrepancies early, improving data integrity.
-
Patient Engagement and Retention AI-powered decentralized trial solutions, like Medable, help patients stay on track with study activities, sending medication reminders and collecting patient-reported outcomes. This reduces site burden and improves participant experience, leading to better retention and compliance.
AI Limitations Are Real
AI tools can hallucinate, mirror human bias, and require thoughtful oversight. Research professionals must ensure AI usage complies with GCP, data privacy laws, and audit trail requirements. Automation can’t become a black box.
How Clinical Professionals Upskill In AI
To stay ahead, clinical professionals should:
- Build foundational knowledge about AI and its applications
- Explore AI tools firsthand, such as ChatGPT or Claude
- Examine their workflow to identify tasks that can be streamlined with AI
- Join or launch AI-related initiatives in their organization
- Stay informed through industry newsletters and thought leaders
Conclusion
AI is making processes faster, improving data quality, and reducing barriers for patient participation. Clinical leaders who upskill with AI will become force-multipliers, while others risk obsolescence. The ability to work with AI tools to solve fundamental problems will define the next generation of clinical research leadership.
