Artificial Intelligence in Anterior Cruciate Ligament Tear Diagnosis: A Bibliometric Analysis of the 50 Most Cited Studies

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.

^* Joint first authors.

Publication History

Article published online:
20 August 2025

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