Evidence mapPaperPMID 39323919Full record

ReviewBJUI compass2024

ChatGPT and generative AI in urology and surgery-A narrative review.

Shane Qin, Bodie Chislett, Joseph Ischia, Weranja Ranasinghe, Daswin de Silva, Jasamine Coles-Black, Dixon Woon, Damien Bolton

Erratum issuedAbstract readReview
In one paragraph

Review in BJUI compass, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Erratum.BJUI compass · 2024
    Article
  9. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Shane QinDepartment of Urology Austin Health Heidelberg Victoria Australia.ORCID https://orcid.org/0000-0002-5159-0448
Bodie ChislettDepartment of Urology Austin Health Heidelberg Victoria Australia.
Joseph IschiaDepartment of Urology Austin Health Heidelberg Victoria Australia.
Weranja RanasingheDepartment of Anatomy and Developmental Biology Monash University Melbourne Victoria Australia.ORCID https://orcid.org/0000-0002-4006-0388
Daswin de SilvaResearch Centre for Data Analytics and Cognition La Trobe University Melbourne Victoria Australia.
Jasamine Coles-BlackDepartment of Urology Austin Health Heidelberg Victoria Australia.ORCID https://orcid.org/0000-0002-8358-3779
Dixon WoonDepartment of Urology Austin Health Heidelberg Victoria Australia.
Damien BoltonDepartment of Urology Austin Health Heidelberg Victoria Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: ChatGPT (generative pre-trained transformer [GPT]), developed by OpenAI, is a type of generative artificial intelligence (AI) that has been widely utilised since its public release. It orchestrates an advanced conversational intelligence, producing sophisticated responses to questions. ChatGPT has been successfully demonstrated across several applications in healthcare, including patient management, academic research and clinical trials. We aim to evaluate the different ways ChatGPT has been utilised in urology and more broadly in surgery. Methods: We conducted a literature search of the PubMed and Embase electronic databases for the purpose of writing a narrative review and identified relevant articles on ChatGPT in surgery from the years 2000 to 2023. A PRISMA flow chart was created to highlight the article selection process. The search terms 'ChatGPT' and 'surgery' were intentionally kept broad given the nascency of the field. Studies unrelated to these terms were excluded. Duplicates were removed. Results: Multiple papers have been published about novel uses of ChatGPT in surgery, ranging from assisting in administrative tasks including answering frequently asked questions, surgical consent, writing operation reports, discharge summaries, grants, journal article drafts, reviewing journal articles and medical education. AI and machine learning has also been extensively researched in surgery with respect to patient diagnosis and predicting outcomes. There are also several limitations with the software including artificial hallucination, bias, out-of-date information and patient confidentiality. Conclusion: The potential of ChatGPT and related generative AI models are vast, heralding the beginning of a new era where AI may eventually become integrated seamlessly into surgical practice. Concerns with this new technology must not be disregarded in the urge to hasten progression, and potential risks impacting patients' interests must be considered. Appropriate regulation and governance of this technology will be key to optimising the benefits and addressing the intricate challenges of healthcare delivery and equity.

Indexed as

ChatGPTgenerative artificial intelligencemachine learningsurgeryurology

Identifiers

PMID39323919
PMCPMC11420103

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.