Can Artificial Intelligence Educate Patients? Comparative Analysis of ChatGPT and DeepSeek Models in Meniscus Injuries Article Swipe
Bahri bozgeyik
,
Erman Öğümsöğütlü
,
Erman Öğümsöğütlü
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/healthcare13222980
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/healthcare13222980
Background: Meniscus injuries are among the most common traumatic and degenerative conditions of the knee joint. Patient education plays a critical role in treatment adherence, surgical preparation, and postoperative rehabilitation. The use of artificial intelligence (AI)-based large language models (LLMs) is rapidly increasing in healthcare. This study aimed to compare the quality and readability of responses to frequently asked patient questions about meniscus injuries generated by ChatGPT-5 and DeepSeek R1. Materials and Methods: Twelve frequently asked questions regarding the etiology, symptoms, diagnosis, imaging, and treatment of meniscus injuries were presented to both AI models. The responses were independently evaluated by two experienced orthopedic surgeons using a response rating system and a 4-point Likert scale to assess accuracy, clarity, comprehensiveness, and consistency. Readability was analyzed using the Flesch–Kincaid Reading Ease Score (FRES) and the Flesch–Kincaid Grade Level (FKGL). Interrater reliability was determined using intraclass correlation coefficients (ICCs). Results: DeepSeek performed significantly better than ChatGPT in the response rating system (p = 0.017) and achieved higher scores for comprehensiveness on the 4-point Likert scale (p = 0.005). No significant differences were observed between the two models in terms of accuracy, clarity, or consistency (p > 0.05). Both models produced comparable readability scores (p > 0.05), corresponding to a high-school reading level. Conclusions: Both ChatGPT and DeepSeek show promise as supportive tools for educating patients about meniscus injuries. While DeepSeek demonstrated higher overall content quality, both models generated understandable information suitable for general patient education. Further refinement is needed to improve clarity and accessibility, ensuring that AI-based materials are appropriate for diverse patient populations.
Related Topics
Concepts
Readability
Likert scale
Intraclass correlation
Inter-rater reliability
CLARITY
Meniscus
Rating scale
Physical therapy
Orthopedic surgery
Consistency (knowledge bases)
Scale (ratio)
Reliability (semiconductor)
Reading (process)
Medicine
Usability
Medical physics
Psychology
Artificial intelligence
Quality (philosophy)
Writing assessment
Physical medicine and rehabilitation
Computer science
Applied psychology
Medical education
Osteoarthritis
MEDLINE
Capsulorhexis
Evidence-based medicine
Cronbach's alpha
Patient education
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/healthcare13222980
- https://www.mdpi.com/2227-9032/13/22/2980/pdf?version=1763620752
- OA Status
- gold
- References
- 29
- OpenAlex ID
- https://openalex.org/W7106120225
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7106120225Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/healthcare13222980Digital Object Identifier
- Title
-
Can Artificial Intelligence Educate Patients? Comparative Analysis of ChatGPT and DeepSeek Models in Meniscus InjuriesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-20Full publication date if available
- Authors
-
Bahri bozgeyik, Erman Öğümsöğütlü, Erman ÖğümsöğütlüList of authors in order
- Landing page
-
https://doi.org/10.3390/healthcare13222980Publisher landing page
- PDF URL
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https://www.mdpi.com/2227-9032/13/22/2980/pdf?version=1763620752Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-9032/13/22/2980/pdf?version=1763620752Direct OA link when available
- Concepts
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Readability, Likert scale, Intraclass correlation, Inter-rater reliability, CLARITY, Meniscus, Rating scale, Physical therapy, Orthopedic surgery, Consistency (knowledge bases), Scale (ratio), Reliability (semiconductor), Reading (process), Medicine, Usability, Medical physics, Psychology, Artificial intelligence, Quality (philosophy), Writing assessment, Physical medicine and rehabilitation, Computer science, Applied psychology, Medical education, Osteoarthritis, MEDLINE, Capsulorhexis, Evidence-based medicine, Cronbach's alpha, Patient educationTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
29Number of works referenced by this work
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| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2227-9032/13/22/2980/pdf?version=1763620752 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Healthcare |
| primary_location.landing_page_url | https://doi.org/10.3390/healthcare13222980 |
| publication_date | 2025-11-20 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2003328208, https://openalex.org/W2613026238, https://openalex.org/W4323050332, https://openalex.org/W4384561707, https://openalex.org/W4411055740, https://openalex.org/W4404301296, https://openalex.org/W4391136726, https://openalex.org/W1976013449, https://openalex.org/W4393289861, https://openalex.org/W4384499300, https://openalex.org/W4410936371, https://openalex.org/W1967390364, https://openalex.org/W1507711477, https://openalex.org/W2337155942, https://openalex.org/W4412043430, https://openalex.org/W4413112172, https://openalex.org/W4412046732, https://openalex.org/W4410988545, https://openalex.org/W4366603527, https://openalex.org/W3131540942, https://openalex.org/W4411463172, https://openalex.org/W4410331119, https://openalex.org/W3184049543, https://openalex.org/W2143386650, https://openalex.org/W4405769224, https://openalex.org/W2810661566, https://openalex.org/W4220957945, https://openalex.org/W4412774982, https://openalex.org/W4414295927 |
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