Evidence mapPaperPMID 37486359Full record

ArticleInternational urogynecology journal2023

The role of artificial intelligence in the future of urogynecology.

Yair Daykan, Barry A O'Reilly

Registry-linked trialAbstract read
PubMed Publisher
In one paragraph

Article in International urogynecology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06481436 (Use of Artificial Intelligence by Patients for Understanding of Diagnosis of Urogynecologic Conditions and Its Role in Treatment Decisions), which is not on this 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.

NCT06481436 naactive not recruitingnot on this mapstarted 2024, after this paper: background citation

Use of Artificial Intelligence by Patients for Understanding of Diagnosis of Urogynecologic Conditions and Its Role in Treatment Decisions

TypeinterventionalSponsorHartford HospitalRan2024 to 2025Enrolled125ConditionsUterovaginal Prolapse, Urinary Incontinence, Lower Urinary Tract SymptomsArmsUse of ChatGPT
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

5 · Who and what money

Authors and funding

2 authors.

Yair DaykanDepartment of Urogynaecology, Cork University Maternity Hospital, Cork, Ireland. yair.dykan@gmail.com.ORCID 0000-0002-0447-2414
Barry A O'ReillyDepartment of Urogynaecology, Cork University Maternity Hospital, Cork, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) in medicine is a rapidly growing field aimed at using machine learning models to improve health outcomes and patient experiences. Many new platforms have become accessible and therefore it seems inevitable that we consider how to implement them in our day-to-day practice. Currently, the specialty of urogynecology faces new challenges as the population grows, life expectancy increases, and quality of life expectation is much improved. As AI has a lot of potential to promote the discipline of urogynecology, we aim to explore its abilities and possible use in the future. Challenges and risks are associated with using AI, and a responsible use of such resources is required.

Indexed as

Artificial IntelligenceMedicineForecastingHumansMachine LearningQuality of LifeArtificial intelligenceObstetrics and gynecologyPersonalized medicineReviewUrogynecology

Identifiers

What Socratic 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 Socratic graph.