Evidence map›Paper›PMID 32257027›Full record

ReviewIndian journal of orthopaedics2020

Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics.

Murali Poduval, Avik Ghose, Sanjeev Manchanda, Vaibhav Bagaria, Aniruddha Sinha

Abstract readReview
In one paragraph

Review in Indian journal of orthopaedics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

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

  1. Pooled it
  2. [Application and prospect of machine learning in orthopaedic trauma].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2023
    Pooled it
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  17. Machine learning in orthopaedic surgery.World journal of orthopedics · 2021
    Review
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  20. [Digital OR].Der Unfallchirurg · 2020
    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

5 authors.

Murali Poduval1Tata Consultancy Services, Unit 129/130, SDF V, SEEPZ, Andheri East, Mumbai, 400093 India.
Avik Ghose2TCS Research and Innovation, Tata Consultancy Services, Kolkata, 700160 India.
Sanjeev Manchanda3TCS Research and Innovation, Tata Consultancy Services, Unit 129/130, SEEPZ, Andheri East, Mumbai, 400096 India.
Vaibhav BagariaReliance HN Foundation Hospital Mumbai, Mumbai, India.
Aniruddha Sinha2TCS Research and Innovation, Tata Consultancy Services, Kolkata, 700160 India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Orthopaedics as a surgical discipline requires a combination of good clinical acumen, good surgical skill, a reasonable physical strength and most of all, good understanding of technology. The last few decades have seen rapid adoption of new technologies into orthopaedic practice, power tools, new implants, CAD-CAM design, 3-D printing, additive manufacturing just to name a few. The new disruption in orthopaedics in the current time and era is undoubtedly the advent of artificial intelligence and robotics. As these technologies take root and innovative applications continue to be incorporated into the main-stream orthopedics, as we know it today, it is imperative to look at and understand the basics of artificial intelligence and what work is being done in the field today. This article takes the form of a loosely structured narrative review and will introduce the reader to key concepts in the field of artificial intelligence as well as some of the directions in application of the same in orthopaedics. Some of the recent work has been summarised and we present our viewpoint at the conclusion as to why we must consider artificial intelligence as a disrupting positive influence on orthopaedic surgery.

Indexed as

Artificial intelligenceMachine learningOrthopaedic surgery

Identifiers

PMID32257027
PMCPMC7096590

What OpenQuestion holds

Textmetadata
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.