Evidence map›Paper›PMID 40150752›Full record

ReviewBioengineering (Basel, Switzerland)2025

Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis.

Li Yan, Qing Li, Kang Fu, Xiaodong Zhou, Kai Zhang

Abstract readReview
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. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–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

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  10. AI-Enhanced POCUS in Emergency Care.Diagnostics (Basel, Switzerland) · 2026
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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

5 authors.

Li YanInstitute of Medical Research, Northwestern Polytechnical University, Xi'an 710072, China.ORCID 0000-0001-9079-8826
Qing LiUltrasound Diagnosis & Treatment Center, Xi'an International Medical Center Hospital, Xi'an 710100, China.
Kang FuInstitute of Medical Research, Northwestern Polytechnical University, Xi'an 710072, China.
Xiaodong ZhouUltrasound Diagnosis & Treatment Center, Xi'an International Medical Center Hospital, Xi'an 710100, China.
Kai ZhangDepartment of Dermatology and Aesthetic Plastic Surgery, Xi'an No. 3 Hospital, The Affiliated Hospital of Northwest University, Xi'an 718000, China.

Funding

Key Research and Development Project of Shaanxi Province 2023-YBSF-121
6 · The paper itself

Abstract

The integration of artificial intelligence (AI) into ultrasound medicine has revolutionized medical imaging, enhancing diagnostic accuracy and clinical workflows. This review focuses on the applications, challenges, and future directions of AI technologies, particularly machine learning (ML) and its subset, deep learning (DL), in ultrasound diagnostics. By leveraging advanced algorithms such as convolutional neural networks (CNNs), AI has significantly improved image acquisition, quality assessment, and objective disease diagnosis. AI-driven solutions now facilitate automated image analysis, intelligent diagnostic assistance, and medical education, enabling precise lesion detection across various organs while reducing physician workload. AI's error detection capabilities further enhance diagnostic accuracy. Looking ahead, the integration of AI with ultrasound is expected to deepen, promoting trends in standardization, personalized treatment, and intelligent healthcare, particularly in underserved areas. Despite its potential, comprehensive assessments of AI's diagnostic accuracy and ethical implications remain limited, necessitating rigorous evaluations to ensure effectiveness in clinical practice. This review provides a systematic evaluation of AI technologies in ultrasound medicine, highlighting their transformative potential to improve global healthcare outcomes.

Indexed as

artificial intelligence (AI)deep learning (DL)diagnosismachine learning (ML)medicinetherapyultrasound

Identifiers

PMID40150752
PMCPMC11939760

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.