scalability

Grand Rounds August 7, 2026: Adaptive AI for CV Adjudication: Adjudication Across Different Definitions in the ODYSSEY OUTCOMES and EUCLID Trials (Sreek Vemulapalli, MD; Ricardo Henao, PhD)

Posted on by Sophia Ramirez

Speakers

Sreek Vemulapalli, MD
Associate Professor of Medicine with Tenure
Director, CV Imaging, DUHS
Duke Clinical Research Institute
Duke-Margolis Institute for Health Policy

Ricardo Henao, PhD
Associate Professor
Department of Biostatistics & Bioinformatics
Department of Electrical and Computer Engineering
Department of Surgery
Associate Director of Clinical Trials AI, Duke Clinical Research Institute (DCRI)
Duke University

Keywords

Adjudication; AI; Clinical Event; Clinical Event Committees; Scalability

Key Points

  • The promise of clinical endpoint or event adjudication is standardization and the improvement of endpoint credibility. However, a human-centered review model utilizing Clinical Endpoint Committees (CECs) represents a major scalability constraint due to cost pressure, operational drag, and between-group variability.
  • AI could reduce the burden on CECs by separating confident events from cases requiring human review. Previous trials testing this concept have derived separate models for each event type and have no demonstrated ability to adapt to new definitions or endpoints across trials. The research team sought to develop and train an AI algorithm to perform clinical endpoint adjudication that can extend to new endpoint and trials. They then compared that algorithm’s performance to Chat GPT 4.0 and hybrid (AI + human) adjudication.
  • The research team found that a single AI algorithm trained on a single trial can, after brief adaptation, identify the same events with a new definition and identify new events. This was validated in the EUCLID trial, when the algorithm alone or as a hybrid strategy reproduced the EUCLID primary endpoint and effect size. While further prospective studies are needed, the potential impact of AI-enabled automated adjudication of outcomes on developing evidence is substantial.

Discussion Themes

The role of clinical adjudicators could shift toward auditing the AI, similar to the evolution of EKG reading, where experts focus on the 10% of cases where the algorithm is most likely to be wrong or uncertain.

The primary barriers to implementing AI systems that comb through electronic health records for trial data are governance, data security, and access rights, rather than a lack of technological capability.

While demonstrated here in cardiovascular trials, this methodology is theoretically applicable to other complex domains. To gain regulatory acceptance, the presenters suggested that prospective validation across multiple clinical research groups and a wide variety of endpoints will be necessary.

Grand Rounds March 21, 2025: Generative Artificial Intelligence in Clinical Trials: A Driver of Efficiency and Democratization of Care (Alexander J. “AJ” Blood, MD, MSc)

Posted on by Sophia Ramirez

Speaker

Alexander J. “AJ” Blood, MD, MSc
Associate Director, Accelerator for Clinical Transformation Research Group
Instructor of Medicine at Harvard Medical School
Cardiologist and Intensivist
Brigham and Women’s Hospital

Keywords

Artificial Intelligence; Cost; Large Language Models; Enrollment; Eligibility; Recruitment

Key Points

  • The Accelerator for Clinical Transformation (ACT) is a research group that seeks to use emerging technology to try and expand access to healthcare and improve quality and quantity of healthcare delivery. They focus on team-based models and scalable applications.
  • It’s becoming more expensive and time-consuming to move a drug from the clinical trial stage to approval. Patient recruitment is the leading driver of costs in clinical trials, and 55% of trials that fail to complete cite low accrual rate as the reason for study termination. There’s pressure from industry to conduct clinical trials in a way that is faster, cheaper, and better for both the patients and the research environment.
  • ACT conducted a pilot study in which they embedded a Large Language Model (LLM) tool called RECTIFIER into an active clinical trial of patients with heart failure. RECTIFIER is an AI-powered, comprehensive software application able to ask and answer questions about unstructured clinical data. In a pilot study, RECTIFIER determined patient eligibility with higher accuracy and specificity than study staff, indicating its potential to streamline screening.
  • LLMs are the engines that power the software. There are two key challenges that need to be taken into consideration to use these tools effectively: 1) there’s a content window – a limit to the amount of Electronic Health Record (EHR) data you can pull in; and 2) Using LLMs is expensive.
  • Following up on the pilot study results, ACT conducted a prospective randomized controlled trial: The Manual Versus AI-Assisted Clinical Trial Screening Using LLMs (MAPS-LLM) trial. MAPS-LLM compared two methods for analyzing a randomized pool of potentially eligible participants: manual review by study staff, and RECITIFIER-augmented review by study staff. Their primary endpoint was eligibility determination.
  • They found that AI-assisted patient screening using the RECTIFIER system significantly improved eligibility determination and enrollment compared with manual screening in a heart failure clinical trial.
  • ACT concluded that implementing AI-assisted tools like RECTIFIER can enhance clinical trial efficiency, reduce resource utilization, and promote equitable recruitment, potentially leading to faster trial completion and earlier patient access to novel therapies. Generative AI is likely to play a significant role in the future of clinical trials.

Discussion Themes

Study staff in the MAPS-LLM intervention arm were able to direct more time and effort towards contacting patients and managing patients with the time they would have spent reviewing charts and manually screening the EHR.

The rate of eligibility between the two arms was equivalent; the difference was, the AI-augmented group was able to assess twice as many potentially eligible patients.

While this tool can do a lot of analytical work, a human element will be essential to utilizing it effectively and to bringing “human intelligence” to participant enrollment.

The ACT team has started to pilot this technology in other disease areas, including cardiology more broadly, endocrinology, oncology, and gastroenterology.