Evidence map›Paper›PMID 40150708›Full record

ReviewBioengineering (Basel, Switzerland)2025

Artificial Intelligence-Driven Wireless Sensing for Health Management.

Merih Deniz Toruner, Victoria Shi, John Sollee, Wen-Chi Hsu, Guangdi Yu, Yu-Wei Dai, Christian Merlo, Karthik Suresh, Zhicheng Jiao, Xuyu Wang and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Bioengineering (Basel, Switzerland), 2025. 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

12 authors.

Merih Deniz TorunerThe Warren Alpert Medical School, Brown University, Providence, RI 02903, USA.ORCID 0000-0001-7447-8654
Victoria ShiSchool of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
John SolleeThe Warren Alpert Medical School, Brown University, Providence, RI 02903, USA.
Wen-Chi HsuDepartment of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.ORCID 0000-0002-8303-4382
Guangdi YuDepartment of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.
Yu-Wei DaiDepartment of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.
Christian MerloSchool of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Karthik SureshSchool of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Zhicheng JiaoDepartment of Diagnostic Radiology, Warren Alpert Medical School of Brown University, Providence, RI 02903, USA.
Xuyu WangSchool of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA.
Shiwen MaoDepartment of Electrical and Computer Engineering, Auburn University, Auburn, AL 36849, USA.ORCID 0000-0002-7052-0007
Harrison BaiDepartment of Radiology and Radiological Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.

Funding

U.S. National Science Foundation IIS-2306789U.S. National Science Foundation IIS-2306790U.S. National Science Foundation IIS-2306791U.S. National Science Foundation IIS-2306792
6 · The paper itself

Abstract

(1) Background: With technological advancements, the integration of wireless sensing and artificial intelligence (AI) has significant potential for real-time monitoring and intervention. Wireless sensing devices have been applied to various medical areas for early diagnosis, monitoring, and treatment response. This review focuses on the latest advancements in wireless, AI-incorporated methods applied to clinical medicine. (2) Methods: We conducted a comprehensive search in PubMed, IEEEXplore, Embase, and Scopus for articles that describe AI-incorporated wireless sensing devices for clinical applications. We analyzed the strengths and limitations within their respective medical domains, highlighting the value of wireless sensing in precision medicine, and synthesized the literature to provide areas for future work. (3) Results: We identified 10,691 articles and selected 34 that met our inclusion criteria, focusing on real-world validation of wireless sensing. The findings indicate that these technologies demonstrate significant potential in improving diagnosis, treatment monitoring, and disease prevention. Notably, the use of acoustic signals, channel state information, and radar emerged as leading techniques, showing promising results in detecting physiological changes without invasive procedures. (4) Conclusions: This review highlights the role of wireless sensing in clinical care and suggests a growing trend towards integrating these technologies into routine healthcare, particularly patient monitoring and diagnostic support.

Indexed as

artificial intelligenceearly diagnosishealthcare monitoringwireless sensing

Identifiers

PMID40150708
PMCPMC11939480

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.