Evidence mapPaperPMID 40567249Full record

ReviewMedComm2025

Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.

Fei Han, Xiao Huang, Xin Wang, Yong-Feng Chen, Chuang Lu, Shasha Li, Lu Lu, Da-Wei Zhang

Abstract readReview
In one paragraph

Review in MedComm, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 2 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

36 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  11. Precision medicine in orthopaedics: A review of current technologies and future directions.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026
    Review
  12. Review
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  15. Is orthopaedics entering the age of generative AI?-A narrative review of current applications challenges and future directions.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026
    Review
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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

8 authors.

Fei HanDepartment of Orthopedics Xijing Hospital Air Force Medical University Xi'an China.ORCID https://orcid.org/0009-0009-8471-8987
Xiao HuangDepartment of Orthopedics Xijing Hospital Air Force Medical University Xi'an China.ORCID https://orcid.org/0009-0007-9277-9208
Xin WangDepartment of Orthopedics Xijing Hospital Air Force Medical University Xi'an China.
Yong-Feng ChenDepartment of Orthopedics Xijing Hospital Air Force Medical University Xi'an China.
Chuang LuDepartment of Orthopedics The 990th Hospital of the Joint Logistics Support Force Zhumadian China.
Shasha LiLintong Rehabilitation and Convalescent Centre of the Joint Logistics Support Force Xi'an China.
Lu LuDepartment of Orthopedics The 990th Hospital of the Joint Logistics Support Force Zhumadian China.
Da-Wei ZhangDepartment of Orthopedics Xijing Hospital Air Force Medical University Xi'an China.ORCID https://orcid.org/0009-0002-0711-1857

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) drives transformative changes in orthopedic surgery, steering it toward precision and personalization through intelligent applications in preoperative planning, intraoperative assistance, and postoperative rehabilitation/monitoring. Breakthroughs in deep learning, robotics, and multimodal data fusion have enabled AI to demonstrate significant advantages. Nonetheless, current applications face challenges such as limited real-time decision autonomy, fragmented medical data silos, standardization gaps restricting model generalization, and ethical/regulatory frameworks lagging behind technological advancements. Therefore, a critical analysis of the current status of AI and the acceleration of its clinical translation is urgently required. This study systematically reviews the core advancements, challenges, and future directions of AI in orthopedic surgery from technical, clinical, and ethical perspectives. It elaborates on the "perceptual-decisional-executional" intelligent closed loop formed by algorithmic innovation and hardware upgrades, summarizes AI applications across surgical continuum, analyzes ethical and regulatory challenges, and explores emerging trajectories. This review integrates the end-to-end applications of AI in orthopedics, illustrating its evolution. It introduces an "algorithm-hardware-ethics trinity" framework for technical translation, providing methodological guidance for interdisciplinary collaboration. Additionally, it evaluates the combined efficacy of diverse algorithms and devices through practical cases and details of future research frontiers, aiming to inform researchers of current landscapes and guide subsequent investigations.

Indexed as

artificial intelligencedeep learningmultimodal data fusionorthopedic surgeryrobot‐assisted surgery

Identifiers

PMID40567249
PMCPMC12188104

What Socratic 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 Socratic graph.