AI Is Transforming ACL Tear Diagnosis, but Most Models Lack External Validation: Study
A recent study published in the Indian Journal of Radiology and Imaging in February 2026 reveals a transformative surge in artificial intelligence (AI) research for anterior cruciate ligament (ACL) diagnosis, noting that while convolutional neural networks (CNNs) dominate 90% of diagnostic models, a staggering 80% still rely on internal validation, signaling a vital need for broader external testing to bridge the gap into routine clinical practice.
Artificial intelligence research in medicine has surged 36-fold since the early 2000s; however, a critical clinical gap remained in understanding the longitudinal evolution of these technologies in orthopaedics, leading Saran S. Gill and colleagues from Imperial College London to conduct the bibliometric analysis of the 50 most cited papers to identify pivotal diagnostic trends for anterior cruciate ligament (ACL) tears.
Therefore, the bibliometric study utilized the Web of Science database to rank and analyze the 50 most-cited peer-reviewed articles published between 2017 and 2024, employing two independent reviewers to screen for clinical relevance and using analytical tools to evaluate model characteristics, validation methods, and institutional contributions. The analysis focused on human population studies regarding AI-driven ACL diagnosis while excluding retracted papers and those outside the topical scope to ensure data integrity.
Key Clinical Findings of the Study Includes:
Publication Peak: The study identified that research output peaked in 2021 with 13 top-cited articles, though this figure gradually declined to 4 studies by 2024.
Geographical Contributions: China and the United States emerged as global leaders with 14 and 13 publications, respectively, contrasting with the lower representation of 10 studies each from major European contributors.
Architectural Dominance: Convolutional neural networks were employed in 90% of cases, while only 5% of studies utilized radiomic-based approaches despite their superior precision potential.
Validation Disparities: The study highlighted a significant gap where 80% of models underwent internal validation, yet only 15% were subjected to multi-institutional magnetic resonance imaging (MRI) testing.
Research Priorities: The analysis noted that while 40 studies proposed original models, 10 were reviews, emphasizing a growing focus on model performance assessment and clinical validation.
The results suggest that while AI is fundamentally reshaping orthopedic diagnostics with a cumulative 1,222 citations across top works, addressing key barriers like algorithmic bias, data privacy, and the current lack of explainability in outputs is essential for successful healthcare integration.
Thus, the study concludes that establishing standardized reporting guidelines and advancing explainable AI will be vital for translating these technological advancements into routine practice to enhance clinical outcomes.
Although the reliance on a single database may have excluded some relevant literature, future efforts should prioritize large-scale external validation and the integration of diverse, multi-continental datasets to refine the generalizability and clinical utility of these diagnostic tools.
Reference
Gill SS, Prashar A, Kamath AG, Shinwari H, Sugand K, Gupte CM. Artificial Intelligence in Anterior Cruciate Ligament Tear Diagnosis: A Bibliometric Analysis of the 50 Most Cited Studies. Indian J Radiol Imaging 2026;36:151–166.
