The clinical trials track explores how AI is reshaping every phase of clinical development, from patient recruitment and site selection to adaptive study design, data analytics, and post‑market surveillance. While AI is already showing promise in automating candidate identification and streamlining data capture, its impact remains limited by siloed systems and traditional workflows.
As trial complexity grows and regulatory demands tighten, the industry must build cohesive, AI‑driven infrastructures that connect recruitment platforms, EDC, real‑world data, and safety monitoring. You’ll learn how advanced analytics, digital cohorts, and continuous pharmacovigilance can create closed‑loop trial ecosystems that recruit the right patients faster, detect signals in real time, and maintain compliance throughout the product lifecycle.
Optimize Patient Recruitment, Retention and Trial Design
Leverage AI to mine electronic health records, digital biomarkers, and social determinants of health to better target and engage eligible participants, reducing screen fail rates. Enhance trial design with predictive modeling and synthetic control arms to refine inclusion criteria and power calculations, ultimately accelerating protocol development and site activation.
Enable Adaptive, Decentralized and Patient-Centric Trials
Utilize AI-driven digital cohorts and remote monitoring tools to facilitate virtual visits, dynamic dosing, and personalized trial experiences. Integrate wearables and EDC systems to enable real-time monitoring of patient data, improving flexibility, adherence, and patient satisfaction throughout the trial.
Enhance Real-Time Analytics and Lifecycle Monitoring
Deploy machine learning dashboards that synthesize EDC, lab, and wearable data to identify safety signals, protocol deviations, and data quality concerns in real time. Post-approval, use AI to mine social media, claims databases, and patient registries to detect adverse events, track long-term safety, and support robust post-market surveillance.
Agenda Highlights
- End‑to‑End Trial Infrastructure: Unifying recruitment, EDC, RTSM, and safety databases into a single platform for seamless data flow and analytics.
- AI‑Driven Patient Matching: Leveraging NLP and predictive algorithms to identify high‑potential sites and patients from unstructured medical records.
- Predictive Analytics & Digital Cohorts: Simulating enrollment scenarios and virtual control arms to de‑risk study design and optimize sample size.
- Real‑Time Data Monitoring & Dashboards: Integrating diverse data streams including, wearables, labs and ePROs, into ML‑powered dashboards for continuous oversight.
- Continuous Pharmacovigilance: Applying NLP and signal‑detection algorithms to post‑market data for proactive safety management and regulatory reporting.