Evidence map›Paper›PMID 40486793›Full record

ArticleJSES international2025

Artificial intelligence in shoulder arthroplasty: how smart is it?

Hyun Gon Kim, Su Cheol Kim, Jong Hun Park, Jae Soo Kim, Dae Yeung Kim, Jae Chul Yoo

Abstract read
In one paragraph

Article in JSES international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
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

6 authors.

Hyun Gon KimDepartment of Orthopedic Surgery, Korea University Ansan Hospital, Ansan, Republic of Korea.
Su Cheol KimDepartment of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Jong Hun ParkDepartment of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Jae Soo KimDepartment of Orthopedic Surgery, Chung-ang Univeristy College of Medicine, Chung-ang University Gwangmyeong Hospital, Gwangmyeong, Republic of Korea.
Dae Yeung KimDepartment of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Jae Chul YooDepartment of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The application of artificial intelligence (AI) is growing rapidly in many fields, and the medical field is no exception. The amount of information we collect before, during, and after surgery is growing exponentially, making the opportunity for AI applications to be integrated into our daily practice more and more apparent. Methods: A literature search was conducted in January 2024 using PubMed (MEDLINE), SCOPUS, and EMBASE databases. A critical analysis of the relevant literature on AI technology in shoulder arthroplasty was conducted. Results: In the field of shoulder arthroplasty, recent literature reports the predictive value of AI models in predicting length of hospital stay and health-care costs, predicting clinical outcomes and complications, and identifying implants. By leveraging machine learning before, during, and after surgery, surgeons can make clinical decisions, predict possible problems, estimate resources needed and clinical outcomes, and ultimately personalize care for each patient. Conclusion: AI technology is becoming more and more advanced, especially in the medical field. By leveraging machine learning before, during, and after surgery, surgeons can make clinical decisions, predict possible problems, estimate resources needed and clinical outcomes, and ultimately personalize treatment for each patient. Because this technology is still in its infancy, there are several limitations to bringing it into the real-world clinical setting. However, it is advancing at a rapid pace, and therefore as a shoulder surgeon, you need to understand and be interested in AI technology.

Indexed as

Artificial intelligenceClinical decisionDeep learningMachine learningPredictionShoulder arthroplasty

Identifiers

PMID40486793
PMCPMC12144956

What OpenQuestion holds

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Registered trials

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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.