Around the <scp>EQUATOR</scp> With Clin‐ <scp>STAR</scp> : <scp>AI</scp> ‐Based Randomized Controlled Trial Challenges and Opportunities in Aging Research

The CONSORT 2010 statement is a guideline that provides an evidence-based checklist of minimum reporting standards for randomized trials. With the rapid growth of Artificial Intelligence (AI) based interventions in the past 10 years, the CONSORT-AI extension was created in 2020 to provide guidelines for AI-based randomized controlled trials (RCT). The Clin-STAR "Around the EQUATOR" series features existing reported standards while also highlighting the inherent complexities of research involving research of older participants. In this work, we propose that when designing AI-based RCTs involving older adults, researchers adopt a conceptual framework (CONSORT-AI-5Ms) designed around the 5Ms (Mind, Mobility, Medications, Matters most, and Multi-complexity) of Age-Friendly Healthcare Systems. Employing the 5Ms in this context, we provide a detailed rationale and include specific examples of challenges and potential solutions to maximize the impact and value of AI RCTs in an older adult population. By combining the original intent of CONSORT-AI with the 5Ms framework, CONSORT-AI-5Ms provides a patient-centered and equitable perspective to consider when designing AI-based RCTs to address the diverse needs and challenges associated with geriatric care.

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Publication Details

Journal
Journal of the American Geriatrics Society
Published
2025-02-05
DOI
https://doi.org/10.1111/jgs.19362
Citations
1
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.65

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article

Around the EQUATOR With Clin‐ STAR : AI ‐Based Randomized Controlled Trial Challenges and Opportunities in Aging Research

Bob Yang, Vijaytha Muralidharan, Deborah M. Kado, Steven Hsesheng Lin et al.
1 citations
Journal of the American Geriatrics Society
Artificial Intelligence in Healthcare and Education
0.65
article

Around the EQUATOR With Clin‐ STAR : AI ‐Based Randomized Controlled Trial Challenges and Opportunities in Aging Research

Bob Yang, Vijaytha Muralidharan, Deborah M. Kado, Steven Hsesheng Lin, Caroline Park
article en
1 citations

Abstract

The CONSORT 2010 statement is a guideline that provides an evidence-based checklist of minimum reporting standards for randomized trials. With the rapid growth of Artificial Intelligence (AI) based interventions in the past 10 years, the CONSORT-AI extension was created in 2020 to provide guidelines for AI-based randomized controlled trials (RCT). The Clin-STAR "Around the EQUATOR" series features existing reported standards while also highlighting the inherent complexities of research involving research of older participants. In this work, we propose that when designing AI-based RCTs involving older adults, researchers adopt a conceptual framework (CONSORT-AI-5Ms) designed around the 5Ms (Mind, Mobility, Medications, Matters most, and Multi-complexity) of Age-Friendly Healthcare Systems. Employing the 5Ms in this context, we provide a detailed rationale and include specific examples of challenges and potential solutions to maximize the impact and value of AI RCTs in an older adult population. By combining the original intent of CONSORT-AI with the 5Ms framework, CONSORT-AI-5Ms provides a patient-centered and equitable perspective to consider when designing AI-based RCTs to address the diverse needs and challenges associated with geriatric care.

Journal of the American Geriatrics SocietyVol. 73(5)
Keck Hospital of USC (US), Aneurin Bevan University Health Board (GB), Geriatric Research Education and Clinical Center (US), Stanford Medicine (US)
National Institutes of Health
Openalex Percentile: Top 27%
Artificial Intelligence in Healthcare and Education
0.65
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Around the <scp>EQUATOR</scp> With Clin‐ <scp>STAR</scp> : <scp>AI</scp> ‐Based Randomized Controlled Trial Challenges and Opportunities in Aging Research — Bob Yang, Vijaytha Muralidharan, et al. · Journal of the American Geriatrics Society (2025) | TGRS Research Map | TGRS