Exploring ChatGPT effectiveness in addressing direct patient queries on colorectal cancer screening Article Swipe
Marcello Maida
,
Yuichi Mori
,
Lorenzo Fuccio
,
Sandro Sferrazza
,
Alessandro Vitello
,
Antonio Facciorusso
,
Cesare Hassan
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1055/a-2568-9416
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1055/a-2568-9416
Recent studies showed that large language models (LLMs) could enhance understanding of colorectal cancer (CRC) screening, potentially increasing participation rates. However, a limitation of these studies is that questions posed to LLMs are generated by experts. This study aimed to investigate ChatGPT-4o effectiveness in answering CRC screening queries directly generated by patients. Ten consecutive subjects aged 50 to 69 years who were eligible for the Italian national CRC screening program but not actively involved were enrolled. Four possible scenarios for CRC screening were presented to each participant and they were asked to formulate one question per scenario to gather additional information. These questions were then posed to ChatGPT in two separate sessions. The responses were evaluated by five senior experts, who rated each answer based on three criteria: accuracy, completeness, and comprehensibility, using a 5-point Likert scale. In addition, the same 10 patients who created the questions assessed the answers, rating each response as complete, understandable, and trustworthy on a dichotomous scale (yes/no). Experts rated the responses with mean scores of 4.1 ± 1.0 for accuracy, 4.2 ± 1.0 for completeness, and 4.3 ± 1.0 for comprehensibility. Patients rated responses as complete in 97.5%, understandable in 95%, and trustworthy in 100% of cases. Consistency over time was confirmed by an 86.8% similarity between session responses. Despite variability in questions and answers, ChatGPT confirmed good performances in answering CRC screening queries, even when used directly by patients.
Related Topics
Concepts
Trustworthiness
Medicine
Likert scale
Consistency (knowledge bases)
Colorectal cancer screening
Rating scale
Family medicine
Completeness (order theory)
Question answering
Session (web analytics)
Information retrieval
Colorectal cancer
Computer science
Cancer
Artificial intelligence
World Wide Web
Psychology
Internal medicine
Colonoscopy
Internet privacy
Mathematical analysis
Developmental psychology
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1055/a-2568-9416
- http://www.thieme-connect.de/products/ejournals/pdf/10.1055/a-2568-9416.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408935628
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408935628Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1055/a-2568-9416Digital Object Identifier
- Title
-
Exploring ChatGPT effectiveness in addressing direct patient queries on colorectal cancer screeningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-28Full publication date if available
- Authors
-
Marcello Maida, Yuichi Mori, Lorenzo Fuccio, Sandro Sferrazza, Alessandro Vitello, Antonio Facciorusso, Cesare HassanList of authors in order
- Landing page
-
https://doi.org/10.1055/a-2568-9416Publisher landing page
- PDF URL
-
https://www.thieme-connect.de/products/ejournals/pdf/10.1055/a-2568-9416.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.thieme-connect.de/products/ejournals/pdf/10.1055/a-2568-9416.pdfDirect OA link when available
- Concepts
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Trustworthiness, Medicine, Likert scale, Consistency (knowledge bases), Colorectal cancer screening, Rating scale, Family medicine, Completeness (order theory), Question answering, Session (web analytics), Information retrieval, Colorectal cancer, Computer science, Cancer, Artificial intelligence, World Wide Web, Psychology, Internal medicine, Colonoscopy, Internet privacy, Mathematical analysis, Developmental psychology, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
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10Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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