Evidence map›Paper›PMID 39141904›Full record

ArticleJournal of medical Internet research2024

Evaluating the Efficacy of ChatGPT as a Patient Education Tool in Prostate Cancer: Multimetric Assessment.

Damien Gibson, Stuart Jackson, Ramesh Shanmugasundaram, Ishith Seth, Adrian Siu, Nariman Ahmadi, Jonathan Kam, Nicholas Mehan, Ruban Thanigasalam, Nicola Jeffery and 2 more

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed, 1 pooled it
–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

69 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. The use of generative AI tools among individuals with cancer and their caregivers: a cross-sectional study of awareness, attitudes, and experiences.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
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  20. Artificial intelligence in cancer survivorship: evaluating ChatGPT's performance in rehabilitation-related queries.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
    Article

9 more citing papers are in PubMed but not listed here.

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

12 authors.

Damien Gibson *Department of Urology, Saint George Hospital, Kogarah, Australia.ORCID 0000-0003-0398-1265
Stuart Jackson *Faculty of Medicine, University of Sydney, Sydney, Australia.ORCID 0000-0002-2459-8399
Ramesh ShanmugasundaramDepartment of Urology, Saint George Hospital, Kogarah, Australia.ORCID 0000-0003-3869-4119
Ishith SethDepartment of Surgery, Peninsula Health, Victoria, Australia.ORCID 0000-0001-5444-8925
Adrian SiuSurgical Outcomes Research Centre, Sydney, Australia.ORCID 0000-0001-8151-9805
Nariman AhmadiDepartment of Urology, Chris O'Brien Lifehouse, Sydney, Australia.ORCID 0000-0001-8727-5952
Jonathan KamNepean Urology Research Group, Nepean Hospital, Sydney, Australia.ORCID 0000-0001-6354-9664
Nicholas MehanNepean Urology Research Group, Nepean Hospital, Sydney, Australia.ORCID 0009-0009-5585-7339
Ruban ThanigasalamDepartment of Urology, Chris O'Brien Lifehouse, Sydney, Australia.ORCID 0000-0002-6438-8732
Nicola JefferyDepartment of Urology, Chris O'Brien Lifehouse, Sydney, Australia.ORCID 0000-0003-0777-6883
Manish I PatelFaculty of Medicine, University of Sydney, Sydney, Australia.ORCID 0000-0003-1409-9171
Scott LeslieSurgical Outcomes Research Centre, Sydney, Australia.ORCID 0000-0001-9848-969X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) chatbots, such as ChatGPT, have made significant progress. These chatbots, particularly popular among health care professionals and patients, are transforming patient education and disease experience with personalized information. Accurate, timely patient education is crucial for informed decision-making, especially regarding prostate-specific antigen screening and treatment options. However, the accuracy and reliability of AI chatbots' medical information must be rigorously evaluated. Studies testing ChatGPT's knowledge of prostate cancer are emerging, but there is a need for ongoing evaluation to ensure the quality and safety of information provided to patients.

objectiveThis study aims to evaluate the quality, accuracy, and readability of ChatGPT-4's responses to common prostate cancer questions posed by patients.

methodsOverall, 8 questions were formulated with an inductive approach based on information topics in peer-reviewed literature and Google Trends data. Adapted versions of the Patient Education Materials Assessment Tool for AI (PEMAT-AI), Global Quality Score, and DISCERN-AI tools were used by 4 independent reviewers to assess the quality of the AI responses. The 8 AI outputs were judged by 7 expert urologists, using an assessment framework developed to assess accuracy, safety, appropriateness, actionability, and effectiveness. The AI responses' readability was assessed using established algorithms (Flesch Reading Ease score, Gunning Fog Index, Flesch-Kincaid Grade Level, The Coleman-Liau Index, and Simple Measure of Gobbledygook [SMOG] Index). A brief tool (Reference Assessment AI [REF-AI]) was developed to analyze the references provided by AI outputs, assessing for reference hallucination, relevance, and quality of references.

resultsThe PEMAT-AI understandability score was very good (mean 79.44%, SD 10.44%), the DISCERN-AI rating was scored as "good" quality (mean 13.88, SD 0.93), and the Global Quality Score was high (mean 4.46/5, SD 0.50). Natural Language Assessment Tool for AI had pooled mean accuracy of 3.96 (SD 0.91), safety of 4.32 (SD 0.86), appropriateness of 4.45 (SD 0.81), actionability of 4.05 (SD 1.15), and effectiveness of 4.09 (SD 0.98). The readability algorithm consensus was "difficult to read" (Flesch Reading Ease score mean 45.97, SD 8.69; Gunning Fog Index mean 14.55, SD 4.79), averaging an 11th-grade reading level, equivalent to 15- to 17-year-olds (Flesch-Kincaid Grade Level mean 12.12, SD 4.34; The Coleman-Liau Index mean 12.75, SD 1.98; SMOG Index mean 11.06, SD 3.20). REF-AI identified 2 reference hallucinations, while the majority (28/30, 93%) of references appropriately supplemented the text. Most references (26/30, 86%) were from reputable government organizations, while a handful were direct citations from scientific literature.

conclusionsOur analysis found that ChatGPT-4 provides generally good responses to common prostate cancer queries, making it a potentially valuable tool for patient education in prostate cancer care. Objective quality assessment tools indicated that the natural language processing outputs were generally reliable and appropriate, but there is room for improvement.

Indexed as

Patient Education as TopicProstatic NeoplasmsArtificial IntelligenceHumansMaleAIAI chatbotsAI language modelantigen screeningartificial intelligencecancerChatGPTdecision-makinghealth care professionalhealth care professionalslarge language modelmanmedical informationmenmultimetric assessmentnatural language processingNLPpatient educationprostateprostate cancerprostate specific

Identifiers

PMID39141904
PMCPMC11358656

What OpenQuestion 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 OpenQuestion graph.