Evidence map›Paper›PMID 40239208›Full record

ArticleJMIR cancer2025

Assessing the Quality and Reliability of ChatGPT's Responses to Radiotherapy-Related Patient Queries: Comparative Study With GPT-3.5 and GPT-4.

Ana Grilo, Catarina Marques, Maria Corte-Real, Elisabete Carolino, Marco Caetano

Abstract readComparative Study
In one paragraph

Article in JMIR cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 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

5 authors.

Ana GriloResearch Center for Psychological Science of the Faculty of Psychology, University of Lisbon to CICPSI, Faculdade de Psicologia, Universidade de Lisboa, Av. D. João II, Lote 4.69.01, Parque das Nações, Lisboa, 1990-096, Portugal, 351 964371101.ORCID 0000-0003-1986-8814
Catarina MarquesEscola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, Lisboa, Portugal.ORCID 0009-0001-8479-7285
Maria Corte-RealEscola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, Lisboa, Portugal.ORCID 0009-0003-2025-8631
Elisabete CarolinoResearch Center for Psychological Science of the Faculty of Psychology, University of Lisbon to CICPSI, Faculdade de Psicologia, Universidade de Lisboa, Av. D. João II, Lote 4.69.01, Parque das Nações, Lisboa, 1990-096, Portugal, 351 964371101.ORCID 0000-0003-4165-7052
Marco CaetanoEscola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, Lisboa, Portugal.ORCID 0000-0002-8128-1830

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Patients frequently resort to the internet to access information about cancer. However, these websites often lack content accuracy and readability. Recently, ChatGPT, an artificial intelligence-powered chatbot, has signified a potential paradigm shift in how patients with cancer can access vast amounts of medical information, including insights into radiotherapy. However, the quality of the information provided by ChatGPT remains unclear. This is particularly significant given the general public's limited knowledge of this treatment and concerns about its possible side effects. Furthermore, evaluating the quality of responses is crucial, as misinformation can foster a false sense of knowledge and security, lead to noncompliance, and result in delays in receiving appropriate treatment. Objective: This study aims to evaluate the quality and reliability of ChatGPT's responses to common patient queries about radiotherapy, comparing the performance of ChatGPT's two versions: GPT-3.5 and GPT-4. Methods: We selected 40 commonly asked radiotherapy questions and entered the queries in both versions of ChatGPT. Response quality and reliability were evaluated by 16 radiotherapy experts using the General Quality Score (GQS), a 5-point Likert scale, with the median GQS determined based on the experts' ratings. Consistency and similarity of responses were assessed using the cosine similarity score, which ranges from 0 (complete dissimilarity) to 1 (complete similarity). Readability was analyzed using the Flesch Reading Ease Score, ranging from 0 to 100, and the Flesch-Kincaid Grade Level, reflecting the average number of years of education required for comprehension. Statistical analyses were performed using the Mann-Whitney test and effect size, with results deemed significant at a 5% level (P=.05). To assess agreement between experts, Krippendorff α and Fleiss κ were used. Results: GPT-4 demonstrated superior performance, with a higher GQS and a lower number of scores of 1 and 2, compared to GPT-3.5. The Mann-Whitney test revealed statistically significant differences in some questions, with GPT-4 generally receiving higher ratings. The median (IQR) cosine similarity score indicated substantial similarity (0.81, IQR 0.05) and consistency in the responses of both versions (GPT-3.5: 0.85, IQR 0.04; GPT-4: 0.83, IQR 0.04). Readability scores for both versions were considered college level, with GPT-4 scoring slightly better in the Flesch Reading Ease Score (34.61) and Flesch-Kincaid Grade Level (12.32) compared to GPT-3.5 (32.98 and 13.32, respectively). Responses by both versions were deemed challenging for the general public. Conclusions: Both GPT-3.5 and GPT-4 demonstrated having the capability to address radiotherapy concepts, with GPT-4 showing superior performance. However, both models present readability challenges for the general population. Although ChatGPT demonstrates potential as a valuable resource for addressing common patient queries related to radiotherapy, it is imperative to acknowledge its limitations, including the risks of misinformation and readability issues. In addition, its implementation should be supported by strategies to enhance accessibility and readability.

Indexed as

Artificial IntelligenceInternetNeoplasmsRadiotherapyGenerative Artificial IntelligenceHumansReproducibility of ResultsSurveys and Questionnairesaccuracyartificial intelligencecancer awarenesschatbotchat generative pretrained transformerChatGPThealth informationinternet accesslarge language modelnatural language processingOpenAIpatient informationpatient querypatients with cancerqualityradiotherapyreadability

Identifiers

PMID40239208
PMCPMC12017613

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.