Evidence map›Paper›PMID 39318971›Full record

ReviewEuropean urology open science2024

ChatGPT as a Clinical Decision Maker for Urolithiasis: Compliance with the Current European Association of Urology Guidelines.

Ali Talyshinskii, Patrick Juliebø-Jones, B M Zeeshan Hameed, Nithesh Naik, Kinju Adhikari, Ulanbek Zhanbyrbekuly, Lazaros Tzelves, Bhaskar Kumar Somani

Abstract readReview
In one paragraph

Review 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 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 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

8 authors.

Ali TalyshinskiiDepartment of Urology, Astana Medical University, Astana, Kazakhstan.
Patrick Juliebø-JonesDepartment of Clinical Medicine, University of Bergen, Bergen, Norway.
B M Zeeshan HameedDepartment of Urology, Father Muller Medical College, Mangalore, Karnataka, India.
Nithesh NaikDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India.
Kinju AdhikariDepartment of Urology, HCG Cancer Centre of Bangalore, Bangalore, Karnataka, India.
Ulanbek ZhanbyrbekulyDepartment of Urology, Astana Medical University, Astana, Kazakhstan.
Lazaros TzelvesDepartment of Urology, University of Athens, Athens, Greece.
Bhaskar Kumar SomaniDepartment of Urology, University Hospital Southampton NHS Trust, Southampton, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and objective: Generative artificial intelligence models are among the most promising and widely used tools used in health care. This review investigates GPT-4 answers to decision-making questions regarding the diagnosis and treatment of urolithiasis across several clinical settings and their correspondence to the current European Association of Urology (EAU) guidelines. Methods: In March 2024, the GPT-4 model was asked 11 questions, containing a brief description of a patient with urolithiasis. All questions were grouped according to urolithiasis care step: diagnosis, urgent care, scheduled intervention, and metaphylaxis. When responses were received, compliance with the current EAU guidelines was assessed by experienced urologists. Key findings and limitations: Although all responses were provided with information that corresponded to EAU guidelines, six of the 11 answers were associated with missed guideline-provided parts, and incorrect data were given in eight of the 11 answers. GPT-4 is relatively safe in the initial diagnostic flow of patients suspected of having stones within the urinary tract and during treatment planning; however, its understanding of all the nuances of metaphylaxis leaves much to be desired and is far from the dogma given in the EAU guidelines. Moreover, GPT-4 knowledge of strategy and algorithm is not always aligned with the EAU guidelines. Conclusions and clinical implications: Despite the fact that from the perspective of patients with urolithiasis, GPT-4 is capable of answering their questions well, the specificity of questions from urologists is labor intensive for its current version, and necessitates the ability to interpret it correctly and further attempts to improve it. While some aspects of diagnostics are more accurate, these struggle with surgical planning and algorithms in line with the EAU guidelines. Patient summary: The generative artificial intelligence (AI) model GPT-4 is capable of answering urology-related questions, but lacks detailed responses. Although some aspects of the diagnostics are accurate, knowledge of surgical planning is not in line with the European Association of Urology guidelines. Future improvements should focus on efforts to enhance the accuracy, reliability, and clinical relevance of AI tools in urology.

Indexed as

Clinical decisionDiagnosisGenerative pretrained transformerTreatmentUrolithiasis

Identifiers

PMID39318971
PMCPMC11421362

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.