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Review
. 2025 Jul 26;8(4):ooaf081.
doi: 10.1093/jamiaopen/ooaf081. eCollection 2025 Aug.

Advancing methodological development of artificial intelligence in patient-centered comparative clinical effectiveness research: Patient-Centered Outcomes Research Institute's unique contribution to research done differently

Jinghua Ou  1 , Erin Holve  1
Affiliations
Review

Advancing methodological development of artificial intelligence in patient-centered comparative clinical effectiveness research: Patient-Centered Outcomes Research Institute's unique contribution to research done differently

Jinghua Ou et al. JAMIA Open. .

Abstract

Background: Recent advancements of Artificial Intelligence (AI) are rapidly transforming clinical research. While this technology offers exciting opportunities, it amplifies existing concerns regarding the need for transparent methodology that fosters patient engagement, and introduces new challenges. PCORI's Improving Methods portfolio has invested in methodological research to enhance rigor and transparency via patient-centered approaches in AI.

Objective: This commentary outlines PCORI's approach to funding and promoting a portfolio of methodological research that aims to improve the conduct of patient-centered comparative clinical effectiveness research (CER), with a focus on AI methods. The paper highlights a growing portfolio of over 40 AI related projects, including a recent cohort leveraging large language models to augment research processes in CER.

Discussion: PCORI's current portfolio of methods projects in AI illustrate timely opportunities for the clinical research informatics community to develop and assess AI applications that will further advance a robust, interoperable and ethical infrastructure for patient-centered CER. PCORI's requirement for ongoing, meaningful engagement of patients throughout the research lifecycle provides a blueprint for patient-centered AI by developing and applying models and methods designed to create value for patients and other healthcare partners.

Keywords: artificial intelligence; clinical research informatics; comparative clinical effectiveness research; patient-centered research.

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Conflict of interest statement

None declared.

Figures

Figure 1.
Figure 1.
Key stages in research process utilizing AI/ML methods in CER, with focus areas addressed by Methodological projects funded by PCORI. This figure outlines 5 key stages in the research process where AI/ML methods are applied: data processing/information extraction, research consent, predictive modelling, model evaluation, and model interpretability.
See this image and copyright information in PMC

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