Evidence map›Paper›PMID 42662092›Full record

ReviewFrontiers in cellular and infection microbiology2026

Application of artificial intelligence in geriatric infection: recent advances and prospects.

Puyu Liang, Zhaoyi Tan, Beibei Liang, Yuxi Xu, Tianlin Wang, Zhijian Zhang, Yun Cai

Abstract readReview
In one paragraph

Review in Frontiers in cellular and infection microbiology, 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

7 authors.

Puyu Liang *The Phase I Clinical Trial Laboratory, the First Medical Center of the PLA General Hospital, Beijing, China.
Zhaoyi Tan *The Phase I Clinical Trial Laboratory, the First Medical Center of the PLA General Hospital, Beijing, China.
Beibei LiangThe Phase I Clinical Trial Laboratory, the First Medical Center of the PLA General Hospital, Beijing, China.
Yuxi XuThe Phase I Clinical Trial Laboratory, the First Medical Center of the PLA General Hospital, Beijing, China.
Tianlin WangThe Phase I Clinical Trial Laboratory, the First Medical Center of the PLA General Hospital, Beijing, China.
Zhijian ZhangDepartment of Pulmonary and Critical Care Medicine, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.
Yun CaiThe Phase I Clinical Trial Laboratory, the First Medical Center of the PLA General Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: As global population aging accelerates, infectious diseases in older adults have emerged as a growing public health burden with substantial clinical, economic, and societal implications. The rapid advancement of artificial intelligence (AI) offers a transformative opportunity to enhance the management of infections in this vulnerable population. Methods: This review delineates key determinants specific to geriatrics that influence infection susceptibility and atypical presentation, including age-related organ dysfunction, immunosenescence, inflammaging, chronic comorbidities, and psychosocial factors, and summarizes the recent advances in AI applications across the management for infectious diseases in older adults. Results: AI has made significant progress in the prevention, diagnosis, and treatment of infectious diseases in older adults: early detection of clinical deterioration, rapid and etiologically precise diagnosis, individualized therapeutic optimization, AI-facilitated antimicrobial stewardship, and accelerated discovery of novel antimicrobial strategies. Conclusion: AI shows great potential in helping prevent, diagnose, and treat infections in older adults, and represents a promising complement to traditional care approaches.

Indexed as

Artificial IntelligenceCommunicable DiseasesGeriatricsAgedAgingHumansartificial intelligenceclinical therapiesdiagnosisearly warninggeriatric infection

Identifiers

PMID42662092
PMCPMC13519598

What OpenQuestion holds

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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.