How do we find the right patients for the right clinical trials? In this episode, James Green (CEO, Cognome) and Dr. Parsa Mirhaji (Albert Einstein College of Medicine) discuss the complexities of clinical trial matching and how AI-driven learning health systems can transform patient recruitment. They explore: ✅ The role of Agentic AI in understanding trial criteria ✅ Challenges of data silos, redundancy, and quality in hospitals ✅ oTESSA, an AI-powered tool enhancing trial matching with justification & transparency ✅ How soft criteria can improve trial eligibility over time ✅ The impact of reinforcement learning in making trial matching more effective From oncology to breast cancer, this conversation dives deep into how AI, domain knowledge, and institutional context shape the future of clinical research. Tags: #ClinicalTrialMatching, #AIinHealthcare, #LearningHealthSystem, #AgenticAI, #ClinicalTrialRecruitment, #OncologyTrials, #BreastCancerResearch, #HealthcareData, #PatientEligibility, #ReinforcementLearning, #AITransparency, #TESSA, #MedicalAI, #HealthcareInnovation, #ClinicalResearch, #AIforGood, #PatientMatching, #DataSilos, #AIinMedicine, #HealthTech Chapters: Why Clinical Trial Matching is So Complex The Role of AI in Identifying the Right Patients Understanding Inclusion & Exclusion Criteria with AI Tackling Data Silos, Redundancy & Quality Issues Transparency, Justification & Eliminating AI Hallucination Soft vs. Hard Criteria: Preparing Patients for Future Trials The Future of AI in Healthcare & Just-in-Time Matching Closing Thoughts & Next Steps for Clinical Trial AI