Evidence map›Paper›PMID 41941254›Full record

ArticleCanadian Urological Association journal = Journal de l'Association des urologues du Canada2026

Optimizing ChatGPT Applying prompt engineering to frequently asked questions in urologic oncology.

Mark N Alshak, Michelle I Higgins, Craig Cronin, William S Azar, Joseph G Cheaib, Max Kates, Sunil H Patel

Abstract read
In one paragraph

Article in Canadian Urological Association journal = Journal de l'Association des urologues du Canada, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Mark N AlshakThe James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Michelle I HigginsThe James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Craig CroninThe James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
William S AzarThe James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Joseph G CheaibThe James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Max KatesThe James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Sunil H PatelThe James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPatients rely on online searches for patient education materials (PEMs). PEMs are recommended to be written at or below a sixth-grade reading level but are regularly written at a college reading level. Using prompt engineering, we assess the information, misinformation, and readability of ChatGPT responses to urologic oncology questions.

methodsForty-five questions relating to prostate, bladder, and kidney cancer were presented to ChatGPT (version 4o, OpenAI). Quality of health information was assessed using DISCERN (1 [low] to 5 [high]). Understandability and actionability were assessed using PEMAT-P (0 [low] to 100% [high]). Misinformation was scored from 1 [no misinformation] to 5 [high misinformation]. Grade and reading level were calculated using the Flesch-Kincaid scale [5 (easy) to 16 (difficult), and 100-90 (5th grade level) to 10-0 (professional level), respectively]. Prompt engineering was then applied to responses and evaluated.

resultsChatGPT answers are highly accurate but at too advanced a reading level and lacked explanations of benefits, risks, visual aids, actionability, and citations. Using prompt engineering, DISCERN (3.42-4.47, p<0.0001), PEMAT-P understandability (88.4-95.5%, p<0.0001), actionability (25.6-84.2%, p<0.0001), grade reading level (10.5-5.3, p<0.0001), and reading level (42 [college level] to 71.7 [7th grade], p<0.0001), all significantly improved. Misinformation did not change significantly.

conclusionsUsing prompt engineering, ChatGPT provides highly accurate and understandable PEMs at a patient-appropriate reading level and provides concrete resources for patient action. Urologists should understand prompt engineering and be involved in the development of artificial chatbots to optimize results.

Identifiers

PMID41941254
PMCPMC13423254

What OpenQuestion holds

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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.