Evidence map›Paper›PMID 38926606›Full record

ArticleProstate cancer and prostatic diseases2025

ChatGPT can help guide and empower patients after prostate cancer diagnosis.

Harry Collin, Kandice Keogh, Marnique Basto, Stacy Loeb, Matthew J Roberts

Abstract read
In one paragraph

Article in Prostate cancer and prostatic diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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  6. CostWorld journal of gastrointestinal oncology · 2025
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  9. Patient- and clinician-based evaluation of large language models for patient education in prostate cancer radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025
    Article
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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

5 authors.

Harry CollinDepartment of Urology, Royal Brisbane & Women's Hospital, Brisbane, QLD, Australia. hjacollin@gmail.com.ORCID http://orcid.org/0000-0002-5630-6047
Kandice KeoghDepartment of Urology, Royal Brisbane & Women's Hospital, Brisbane, QLD, Australia.ORCID http://orcid.org/0000-0001-7424-6976
Marnique BastoDepartment of Urology, Royal Brisbane & Women's Hospital, Brisbane, QLD, Australia.ORCID http://orcid.org/0000-0003-1286-3869
Stacy LoebDepartments of Urology and Population Health, New York University Langone Health, New York, NY, USA.ORCID http://orcid.org/0000-0003-3933-9207
Matthew J RobertsDepartment of Urology, Royal Brisbane & Women's Hospital, Brisbane, QLD, Australia.ORCID http://orcid.org/0000-0003-0552-7402

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND/

objectivesPatients often face uncertainty about what they should know after prostate cancer diagnosis. Web-based information is common but is at risk of being of poor quality or readability. SUBJECTS/

methodsWe used ChatGPT, a freely available Artificial intelligence (AI) platform, to generate enquiries about prostate cancer that a newly diagnosed patient might ask and compared to Google search trends. Then, we evaluated ChatGPT responses to these questions for clinical appropriateness and quality using standardised tools.

resultsChatGPT generates broad and representative questions, and provides understandable, clinically sound advice.

conclusionsAI can guide and empower patients after prostate cancer diagnosis through education. However, the limitations of the ChatGPT language-model must not be ignored and require further evaluation and optimisation in the healthcare field.

Indexed as

Artificial IntelligencePatient Education as TopicPatient ParticipationProstatic NeoplasmsGenerative Artificial IntelligenceHumansInternetMale

Identifiers

PMID38926606
PMCPMC12106063

What OpenQuestion holds

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LicenceCC BY
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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.