Evidence map›Paper›PMID 39421706›Full record

ArticleContemporary oncology (Poznan, Poland)2024

Performance of ChatGPT in providing patient information about upper tract urothelial carcinoma.

Jan Łaszkiewicz, Wojciech Krajewski, Wojciech Tomczak, Joanna Chorbińska, Łukasz Nowak, Adam Chełmoński, Piotr Krajewski, Aleksandra Sójka, Bartosz Małkiewicz, Tomasz Szydełko

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PubMed Full text at PMC Publisher
In one paragraph

Article in Contemporary oncology (Poznan, Poland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Observational
  4. Review
  5. 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

10 authors.

Jan ŁaszkiewiczUniversity Center of Excellence in Urology, Wrocław Medical University, Wrocław, Poland.
Wojciech KrajewskiDepartment of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wrocław Medical University, Wrocław, Poland.
Wojciech TomczakUniversity Center of Excellence in Urology, Wrocław Medical University, Wrocław, Poland.
Joanna ChorbińskaDepartment of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wrocław Medical University, Wrocław, Poland.
Łukasz NowakDepartment of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wrocław Medical University, Wrocław, Poland.
Adam ChełmońskiUniversity Center of Excellence in Urology, Wrocław Medical University, Wrocław, Poland.
Piotr KrajewskiDepartment of Dermatology, Venereology and Allergology, Wroclaw Medical University, Wrocław, Poland.
Aleksandra SójkaUniversity Center of Excellence in Urology, Wrocław Medical University, Wrocław, Poland.
Bartosz MałkiewiczDepartment of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wrocław Medical University, Wrocław, Poland.
Tomasz SzydełkoDepartment of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wrocław Medical University, Wrocław, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The aim was to evaluate ChatGPT generated responses to patient-important questions regarding upper tract urothelial carcinoma (UTUC). Material and methods: Fifteen common inquiries asked by patients regarding UTUC were assigned to 4 categories: general information; symptoms and diagnosis; treatment; and prognosis. These questions were entered into ChatGPT and its responses were recorded. In every answer 5 criteria (adequate length, comprehensible language, precision in addressing the question, compliance with European Association of Urology guidelines and safety of the response for the patient) were assessed by the urologists using a numerical scale of 1-5 (a score of 5 being the best). Results: Sixteen questionnaires were included. A score of five was assigned 336 times (28.0%); 4 - 527 times, (43.9%); 3 - 268 times (22.3%); 2 - 53 ti- mes (4.4%); and 1 - 16 times (1.3%). The average overall score was 3.93. Responses to each question received average scores within the range 3.34-4.18. Answers regarding "general information" were graded the highest - mean score 4.14. Artificial intelligence scored the lowest in the "treatment" category - mean score 3.68. A mean score of 4.02 was given for the safety of the response. However, a few urologists considered several answers as unsafe for the patient, by grading them 1 or 2 in this criterion. Conclusions: ChatGPT does not provide fully adequate information on UTUC, and inquiries regarding treatment can be misleading for the patients. In particular cases, patients might receive potentially unsafe answers. However, ChatGPT can be used with caution to provide basic information regarding epidemiology and risk factors of UTUC.

Indexed as

AIartificial intelligenceChatGPTupper tract urothelial carcinomaurologic oncologyUTUC

Identifiers

PMID39421706
PMCPMC11480910

What OpenQuestion holds

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LicenceCC BY-NC-SA
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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.