Evidence map›Paper›PMID 41781638›Full record

ReviewDrugs & aging2026

Embracing the Digital Revolution: How Artificial Intelligence is Transforming Clinical Trials in Older Participants.

Yaru Wang, Miao Miao, Qingqing Wang, Yuyin Yin, Haijuan Zhao, Shuang Zhao, Han Yang, Xin Wang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Drugs & aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yaru WangDepartment of Clinical Trial Research Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Miao MiaoDepartment of Clinical Trial Research Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Qingqing WangDepartment of Clinical Trial Research Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Yuyin YinDepartment of Clinical Trial Research Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Haijuan ZhaoDepartment of Clinical Trial Research Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Shuang ZhaoDepartment of Clinical Trial Research Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Han YangDepartment of Clinical Trial Research Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China.
Xin WangDepartment of Clinical Trial Research Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China. xinwang134@126.com.

Funding

National High Level Hospital Clinical Research Funding BJ-2023-088National High Level Hospital Clinical Research Funding BJ-2025-243National Natural Science Foundation of China 82504178
6 · The paper itself

Abstract

Artificial intelligence (AI) is revolutionizing clinical trials in geriatric populations by addressing the unique challenges of aging-related diseases and patient heterogeneity. Advanced AI techniques, including machine learning, deep learning, and AI-driven digital health platforms, enable intelligent patient stratification, risk prediction, and real-time monitoring tailored to older adults. AI facilitates the design and execution of decentralized clinical trials, improving accessibility and compliance among older participants. Moreover, AI-powered digital twins and predictive models enhance safety assessments and treatment personalization, optimizing therapeutic outcomes. By integrating multi-omics data, electronic health records, and wearable device outputs, AI enables precise and dynamic decision-making throughout the trial lifecycle. This approach not only increases trial efficiency and accuracy but also supports ethical, patient-centered research practices. This review explores the transformative role of AI in geriatric clinical trials, outlining key advancements, practical challenges, and strategic directions for establishing AI as a catalyst for precision medicine in aging populations.

Indexed as

Artificial IntelligenceClinical Trials as TopicAgedAgingDigital HealthHumansPrecision Medicine

Identifiers

PMID41781638

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.