ReviewThe Indian journal of radiology & imaging2026
Artificial Intelligence in Anterior Cruciate Ligament Tear Diagnosis: A Bibliometric Analysis of the 50 Most Cited Studies.
Review in The Indian journal of radiology & imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled 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.
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.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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 ( 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.