Evidence map›Paper›PMID 32777930›Full record

ReviewAnnals of the Royal College of Surgeons of England2020

Artificial Intelligence in plastic surgery: What is it? Where are we now? What is on the horizon?

D C Murphy, D B Saleh

Registry-linked trialAbstract readReview
In one paragraph

Review in Annals of the Royal College of Surgeons of England, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07523815 (Assessment of the Accuracy of Artificial Intelligence Assisted Workflow Versus Conventional Workflow in Computer Guided Implant Placement), which is not on this map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing 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.

NCT07523815 phase4recruitingnot on this mapstarted 2026, after this paper: background citation

Assessment of the Accuracy of Artificial Intelligence Assisted Workflow Versus Conventional Workflow in Computer Guided Implant Placement: A Randomized Controlled Clinical Trial.

TypeinterventionalSponsorSandra Moussa Botros El TarziRan2026 to 2026Enrolled20ConditionsPartially Edentulous MandibleArmsAI assisted implant placement, Conventional computer guided implant placement
3 · Its place in the literature

Who cites it

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

  1. Pooled it
  2. Large language model applications in facial plastic and reconstructive surgery: a systematic review of applications, performance, and ethical considerations.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
    Review
  3. Review
  4. Article
  5. Ethical Considerations and Equipoise in Cancer Surgery.Indian journal of surgical oncology · 2024
    Review
  6. Article
  7. Review
  8. Review
  9. Article
  10. ChatGPT in Plastic and Reconstructive Surgery.Indian journal of plastic surgery : official publication of the Association of Plastic Surgeons of India · 2023
    Review
  11. Review
  12. Review
  13. Article
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

2 authors.

D C MurphyNewcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
D B SalehNewcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAn increasing quantity of data is required to guide precision medicine and advance future healthcare practices, but current analytical methods often become overwhelmed. Artificial intelligence (AI) provides a promising solution. Plastic surgery is an innovative surgical specialty expected to implement AI into current and future practices. It is important for all plastic surgeons to understand how AI may affect current and future practice, and to recognise its potential limitations.

methodsPeer-reviewed published literature and online content were comprehensively reviewed. We report current applications of AI in plastic surgery and possible future applications based on published literature and continuing scientific studies, and detail its potential limitations and ethical considerations.

findingsCurrent machine learning models using convolutional neural networks can evaluate breast mammography and differentiate benign and malignant tumours as accurately as specialist doctors, and motion sensor surgical instruments can collate real-time data to advise intraoperative technical adjustments. Centralised big data portals are expected to collate large datasets to accelerate understanding of disease pathogeneses and best practices. Information obtained using computer vision could guide intraoperative surgical decisions in unprecedented detail and semi-autonomous surgical systems guided by AI algorithms may enable improved surgical outcomes in low- and middle-income countries. Surgeons must collaborate with computer scientists to ensure that AI algorithms inform clinically relevant health objectives and are interpretable. Ethical concerns such as systematic biases causing non-representative conclusions for under-represented patient groups, patient confidentiality and the limitations of AI based on the quality of data input suggests that AI will accompany the plastic surgeon, rather than replace them.

Indexed as

Artificial IntelligenceImage Interpretation, Computer-AssistedPlastic Surgery ProceduresBig DataBreastBreast NeoplasmsFemaleHumansMammographyArtificial intelligenceBig dataMachine learningPlastic surgeryTechnology

Identifiers

PMID32777930
PMCPMC7538735

What OpenQuestion holds

Textmetadata
Read underepoch 390

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 OpenQuestion graph.