Evidence map›Paper›PMID 39554303›Full record

ArticleEuropean urology open science2024

Capabilities of ChatGPT-3.5 as a Urological Triage System.

Christopher Hirtsiefer, Tim Nestler, Johanna Eckrich, Henrieke Beverungen, Carolin Siech, Cem Aksoy, Marianne Leitsmann, Martin Baunacke, Annemarie Uhlig

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Article in European urology open science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

9 authors.

Christopher HirtsieferKlinik und Poliklinik für Urologie, Universitätsklinikum Carl Gustav Carus Dresden, Dresden, Germany.
Tim NestlerKlinik für Urologie, Bundeswehrzentralrankenhaus Koblenz, Koblenz, Germany.
Johanna EckrichKlinik und Poliklinik für Urologie und Kinderurologie, Universitätsklinikum Bonn, Germany.
Henrieke BeverungenSt. Elisabeth Krankenhaus Leipzig, Leipzig, Germany.
Carolin SiechGoethe University Frankfurt, University Hospital, Frankfurt am Main, Germany.
Cem AksoyKlinik für Urologie, Universitätsklinikum Gießen und Marburg, Marburg, Germany.
Marianne LeitsmannUniversitätsklinik für Urologie, Medizinische Universität Graz, Graz, Austria.
Martin BaunackeKlinik und Poliklinik für Urologie, Universitätsklinikum Carl Gustav Carus Dresden, Dresden, Germany.
Annemarie UhligKlinik für Urologie, Universitätsmedizin Göttingen, Göttingen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and objective: Patients struggle to classify symptoms, which hinders timely medical presentation. With 35-75% of patients seeking information online before consulting a health care professional, generative language-based artificial intelligence (AI), exemplified by ChatGPT-3.5 (GPT-3.5) from OpenAI, has emerged as an important source. The aim of our study was to evaluate the role of GPT-3.5 in triaging acute urological conditions to address a gap in current research. Methods: We assessed GPT-3.5 performance in providing urological differential diagnoses (DD) and recommending a course of action (CoA). Six acute urological pathologies were identified for evaluation. Lay descriptions, sourced from patient forums, formed the basis for 472 queries that were independently entered by nine urologists. We evaluated the output in terms of compliance with the European Association of Urology (EAU) guidelines, the quality of the patient information using the validated DISCERN questionnaire, and a linguistic analysis. Key findings and limitations: The median GPT-3.5 ratings were 4/5 for DD and CoA, and 3/5 for overall information quality. English outputs received higher median ratings than German outputs for DD (4.27 vs 3.95; Conclusion and clinical implications: Our results highlights the potential of GPT-3.5 as a triage system for offering individualized, empathetic advice mostly aligned with the EAU guidelines, outscoring other online information. Relevant shortcomings in terms of information quality, especially for risk assessment, need to be addressed to enhance the reliability. Broader transparency and quality improvements are needed before integration into, primarily English-speaking, patient care. Patient summary: We looked at the performance of ChatGPT-3.5 for patients seeking urology advice. We entered more than 400 German and English inputs and assessed the possible diagnoses suggested by this artificial intelligence tool. ChatGPT-3.5 scored well in providing a complete list of possible diagnoses and recommending a course of action mostly in line with current guidelines. The quality of the information was good overall, but missing and unclear sources for the information can be a problem.

Indexed as

Artificial intelligenceChatGPTInternet useTriageUrological emergency

Identifiers

PMID39554303
PMCPMC11567918

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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.