Evidence map›Paper›PMID 41899512›Full record

ArticleCancers2026

Quality and Usability of Prostate Cancer Information Generated by Artificial Intelligence Chatbots: A Comparative Analysis.

Abdullah Al-Khanaty, Jordan Santucci, David Hennes, Niranjan Sathianathen, Carlos Delgado, Karan Sharma, Eoin Dinneen, Kieran Sandhu, David Chen, Renu Eapen and 9 more

Abstract read
In one paragraph

Article in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

19 authors.

Abdullah Al-KhanatyDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne 3052, Australia.ORCID 0000-0002-6234-3522
Jordan SantucciDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne 3052, Australia.
David HennesDepartment of Urology, Austin Health & Olivia Newton John Cancer Centre, Heidelberg 3084, Australia.
Niranjan SathianathenDepartment of Surgery, Austin Health, University of Melbourne, Melbourne 3052, Australia.
Carlos DelgadoDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne 3052, Australia.ORCID 0000-0003-0400-2647
Karan SharmaDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne 3052, Australia.
Eoin DinneenDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne 3052, Australia.
Kieran SandhuDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne 3052, Australia.
David ChenDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne 3052, Australia.
Renu EapenDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne 3052, Australia.
Daniel MoonDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne 3052, Australia.
Gregory JackDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne 3052, Australia.
Jeremy GoadDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne 3052, Australia.
Shankar SivaSir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne 3052, Australia.ORCID 0000-0003-2840-0658
Muhammad AliSir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne 3052, Australia.ORCID 0000-0002-3285-9165
Damien BoltonDepartment of Surgery, Austin Health, University of Melbourne, Melbourne 3052, Australia.
Nathan LawrentschukDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne 3052, Australia.
Declan G MurphyDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne 3052, Australia.
Marlon PereraDivision of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne 3052, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence chatbots are increasingly used by patients to obtain health information, including for prostate cancer. While these platforms offer accessible and conversational responses, concerns remain regarding the quality, usability, and clinical relevance of AI-generated content. This study comparatively evaluated patient-directed prostate cancer information generated by commonly used AI chatbots.

methodsStandardised prostate cancer-related prompts were developed using Google Trends and authoritative healthcare resources. Identical queries were submitted to five publicly accessible AI chatbots: ChatGPT 5.2, Google Gemini, Claude AI, Microsoft Copilot, and Perplexity. Responses were independently assessed by two blinded reviewers using the DISCERN instrument for information quality and the Patient Education Materials Assessment Tool for printable materials (PEMAT-P) for understandability and actionability. Inter-rater reliability was assessed using intraclass correlation coefficients (ICCs). Readability was evaluated using the Flesch-Kincaid Reading Ease score. Descriptive statistics were used for comparative and pooled analyses.

resultsOverall information quality was moderate, with a pooled median (interquartile range [IQR]) DISCERN score of 56.5 (53.0-61.0). Higher mean DISCERN scores were observed for ChatGPT 5.2 and Microsoft Copilot, whereas lower scores were observed for Claude and Perplexity. PEMAT-P understandability was consistently high across platforms, with a pooled median (IQR) score of 91.7% (83.3-91.7%). In contrast, PEMAT-P actionability was uniformly poor, with a pooled median (IQR) score of 0% (0-0%). Readability analysis demonstrated moderate complexity, with a pooled median (IQR) Flesch-Kincaid Reading Ease score of 50.4 (49.2-52.5) and a median word count of 666 (657-1022). Inter-rater reliability was good for PEMAT understandability (ICC 0.841) and moderate for DISCERN (ICC 0.712).

conclusionsAI chatbots provide highly understandable but only moderately high-quality patient-directed prostate cancer information, with a consistent lack of actionable guidance. Although variation in content quality was observed across platforms, significant limitations remain in evidence transparency and practical patient support. Future development should prioritise integration of evidence-based resources and actionable decision-support tools to enhance the role of AI chatbots in prostate cancer education.

Indexed as

artificial intelligencechatbotshealth information qualitypatient educationprostate cancer

Identifiers

PMID41899512
PMCPMC13024046

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.