Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
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
0
Zitationen
2
Autoren
2025
Jahr
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.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.578 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.470 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.984 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.814 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.