What happens when a retina specialist with a background in computer science takes on the inefficiencies of clinical research?In this insightful conversation, Dr. Mark Barakat of Retina Macula Institute joins Tilda CEO Ram Yalamanchili to explore how AI is transforming the day-to-day reality of clinical trial execution.They discuss the growing operational burdens on site staff, the silent cost of turnover, and the bottlenecks that limit research capacity. Dr. Barakat shares his firsthand experience adopting AI in a high-volume ophthalmology research site—including what’s working, what’s not, and why he believes AI will become a core collaborator, not a threat.From automating data entry and managing re-consent workflows to long-term visions of AI-assisted imaging and protocol compliance, this episode offers a grounded, site-level perspective on AI’s real potential in trial operations.⸻What you’ll learn:- How AI is reducing operational burden for clinical research coordinators and site staff- Why research sites struggle with staff burnout, turnover, and training and how AI can help- The hidden inefficiencies in clinical trial workflows (EDC, source-to-CRF, informed consent)- Real-world examples of AI improving regulatory compliance and data quality at ophthalmology sites- How sites are using AI to manage high trial volume without increasing headcount- Why adoption of AI in clinical research is slow—and what’s changing now- The role of AI in imaging analysis for retina trials, including OCT segmentation and atrophy tracking- How AI can level the playing field across high- and low-performing clinical trial sites- Practical considerations for bringing AI into day-to-day research operations- What sponsors need to know about site enablement, trial scalability, and AI’s role in quality assurance

In a recent conversation, Dr. Houman Hemmati (ophthalmologist, clinical trialist, and biotech innovator) didn’t hold back in describing the systemic inefficiencies he sees every day. From sponsor overreach to CRO micromanagement to administrative overload at research sites, the entire model is built to sustain activity, not outcomes.

He believes that AI can eliminate a lot of the operational drag that wastes time and wears people down. And he predicted a lot of the AI-centric changes that have been announced by the FDA recently.

ICON Eyecare experiences 66% data quality improvement and 90% increase in efficiency.

Dr. James Fox of ICON Eyecare (Grand Junction, CO) discusses what it really takes to scale clinical research today. He walks through the burnout his team faced, the turning point that led to adopting AI teammates, and the measurable improvements in data quality, team capacity, and growth. He unpacks the staffing paradox in research, the trust that's at stake, and what it means for the future when AI and humans work side-by-side. His site, running 30 trials with a lean team, is a case study in what happens when AI meets the real-world pressure of clinical operations.