Artificial intelligence in anterior cruciate ligament tear diagnosis: a bibliometric analysis of the 50 most cited studies
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Introduction
Since the 2000s, artificial intelligence (AI) publications in medicine have surged, particularly in orthopaedics and radiology. A key area is the diagnosis of anterior cruciate ligament (ACL) tears, where AI enhances detection and treatment strategies. This study aims to perform a bibliometric analysis of AI in ACL tear diagnosis, identifying pivotal studies to guide future research and clinical priorities.
Materials and Methods
A bibliometric analysis was conducted using the Web of Science database. The top-50 articles were ranked by citation count and analyzed for basic characteristics and research focus. Trends in diagnostic advancements and AI model utilization were also assessed.
Results
The most cited articles, published between 2017 and 2024, peaked in 2021 (n = 13). Citation counts ranged from 7 to 401 (median: 8.5 ± 7.0). China (n = 14) and the United States (n = 13) emerged as the leading contributors. The vast majority (90%) of models were based on convolutional neural networks (CNNs), with 80% undergoing internal validation. Only 5% of the included models utilized a radiomic framework.
Conclusion
This bibliometric analysis examines the growing role of AI in ACL tear diagnosis, with a marked increase in research output from 2017 to 2024. Key barriers to the adoption of AI models include algorithmic bias, data privacy, explainability, cost-effectiveness, and interoperability. The underrepresentation of radiomic-based models, despite their diagnostic potential, highlights an avenue for future research. Advancing explainable AI, strengthening validation, and establishing standardized reporting guidelines will be essential to ensure clinical integration to improve patient outcomes.
Keywords
ACL - AI - anterior cruciate ligament - artificial intelligence
Data Availability Statement
All relevant data supporting the findings of this study can be accessed within the Supplementary Digital Content attached to the article.
Since the 2000s, artificial intelligence (AI) publications in medicine have surged, particularly in orthopaedics and radiology. A key area is the diagnosis of anterior cruciate ligament (ACL) tears, where AI enhances detection and treatment strategies. This study aims to perform a bibliometric analysis of AI in ACL tear diagnosis, identifying pivotal studies to guide future research and clinical priorities.
Materials and Methods
A bibliometric analysis was conducted using the Web of Science database. The top-50 articles were ranked by citation count and analyzed for basic characteristics and research focus. Trends in diagnostic advancements and AI model utilization were also assessed.
Results
The most cited articles, published between 2017 and 2024, peaked in 2021 (n = 13). Citation counts ranged from 7 to 401 (median: 8.5 ± 7.0). China (n = 14) and the United States (n = 13) emerged as the leading contributors. The vast majority (90%) of models were based on convolutional neural networks (CNNs), with 80% undergoing internal validation. Only 5% of the included models utilized a radiomic framework.
Conclusion
This bibliometric analysis examines the growing role of AI in ACL tear diagnosis, with a marked increase in research output from 2017 to 2024. Key barriers to the adoption of AI models include algorithmic bias, data privacy, explainability, cost-effectiveness, and interoperability. The underrepresentation of radiomic-based models, despite their diagnostic potential, highlights an avenue for future research. Advancing explainable AI, strengthening validation, and establishing standardized reporting guidelines will be essential to ensure clinical integration to improve patient outcomes.
Keywords
ACL - AI - anterior cruciate ligament - artificial intelligence
Data Availability Statement
All relevant data supporting the findings of this study can be accessed within the Supplementary Digital Content attached to the article.
Date Issued
2026-04-01
Date Acceptance
2025-08-01
Citation
Indian Journal of Radiology and Imaging, 2026, 36, pp.151-166
ISSN
0971-3026
Publisher
Thieme Medical and Scientific Publishers Pvt. Ltd.
Start Page
151
End Page
166
Journal / Book Title
Indian Journal of Radiology and Imaging
Volume
36
Issue
02
Copyright Statement
© 2025. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41867290
PII: IJRI-25-3-4554
Subjects
ACL
AI
anterior cruciate ligament
artificial intelligence
BARRIERS
Life Sciences & Biomedicine
MODELS
PERFORMANCE
RADIOLOGY
Radiology, Nuclear Medicine & Medical Imaging
RADIOMICS
Science & Technology
Publication Status
Published
Coverage Spatial
Germany
Date Publish Online
2025-08-20