Bench to Bedside at AI Speed
Article excerpt
AI is accelerating the journey from laboratory discovery to patient treatment by identifying who qualifies for new therapies, matching people to clinical trials, and tracking outcomes across large populations. Dr. A.J. Blood, a cardiologist at Brigham and Women's Hospital, discusses how machine learning can sift through patient data to find candidates for experimental treatments, work that once took months now happening in weeks. The technology raises practical questions: Which patients benefit most? How do researchers scale these systems responsibly? Blood explores both the promise and the friction points as cardiology and other specialties adopt AI-driven matching systems.
How can AI determine who gets matched to new therapies, who is identified for clinical trials, and how patient tracking is scaled across large populations? Chip is joined by Dr. A.J. Blood, a practicing cardiologist at Brigham and Women's Hospital and the co-founder and Chief Executive Officer of AIwithCare, a startup company that delivers AI-enabled solutions for research, clinical operations, and patient care. They discuss the role of AI in identifying patients for clinical trials and new therapies, which is typically a critical bottleneck in drug development, as well as how to ensure clinical trials are representative. Also, Dr. Blood shares insights from his extensive research background and the tool, RECTIFIER (RAG-Enabled Clinical Trial Infrastructure for Inclusion Exclusion Review), designed to enhance patient recruitment for clinical trials by efficiently sifting through complex medical data.