Evidence map›Paper›PMID 38338185›Full record

ReviewHealthcare (Basel, Switzerland)2024

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives.

Luca Andriollo, Aurelio Picchi, Rudy Sangaletti, Loris Perticarini, Stefano Marco Paolo Rossi, Giandomenico Logroscino, Francesco Benazzo

Abstract readReview
In one paragraph

Review in Healthcare (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

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

  1. Pooled it
  2. Review
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  4. Machine learning model identifies tibial anatomical variables as potential risk factors for anterior cruciate ligament injury.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026
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  11. Article
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  16. AI-assisted direct anterior approachFrontiers in bioengineering and biotechnology · 2025
    Article
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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

7 authors.

Luca AndriolloRobotic Prosthetic Surgery Unit-Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy.ORCID 0009-0007-1586-2542
Aurelio PicchiUnit of Orthopedics, Department of Life, Health and Environmental Sciences, University of L'Aquila, 67100 L'Aquila, Italy.
Rudy SangalettiRobotic Prosthetic Surgery Unit-Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy.
Loris PerticariniRobotic Prosthetic Surgery Unit-Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy.
Stefano Marco Paolo RossiRobotic Prosthetic Surgery Unit-Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy.ORCID 0000-0003-4395-8457
Giandomenico LogroscinoUnit of Orthopedics, Department of Life, Health and Environmental Sciences, University of L'Aquila, 67100 L'Aquila, Italy.ORCID 0000-0003-1301-5343
Francesco BenazzoRobotic Prosthetic Surgery Unit-Sports Traumatology Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.

Indexed as

anterior cruciate ligamentartificial intelligencedeep learningknee injuriesmachine learningnavigation

Identifiers

PMID38338185
PMCPMC10855330

What OpenQuestion 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 OpenQuestion graph.