Assessing ChatGPT’s Role in Sarcopenia and Nutrition: Insights from a Descriptive Study on AI-Driven Solutions Article Swipe
Özlem Karataş
,
Seden Demirci
,
Kaan Pota
,
Serpil Tuna
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jcm14051747
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jcm14051747
Background: Sarcopenia, an age-related decline in muscle mass and function, poses significant health risks. While AI tools like ChatGPT-4 (ChatGPT-4o) are increasingly used in healthcare, their accuracy in addressing sarcopenia remains unclear. Methods: ChatGPT-4’s responses to 20 frequently asked sarcopenia-related questions were evaluated by 34 experts using a four-criterion scale (relevance, accuracy, clarity, Ccmpleteness). Responses were rated from 1 (low) to 5 (high), and interrater reliability was assessed via intraclass correlation coefficient (ICC). Results: ChatGPT-4 received consistently high median scores (5.0), with ≥90% of evaluators rating responses ≥4. Relevance had the highest mean score (4.7 ± 0.5), followed by accuracy (4.6 ± 0.6), clarity (4.6 ± 0.6), and completeness (4.6 ± 0.7). ICC analysis showed poor agreement (0.416), with Completeness displaying moderate agreement (0.569). Conclusions: ChatGPT-4 provides highly relevant and structured responses but with variability in accuracy and clarity. While it shows potential for patient education, expert oversight remains essential to ensure clinical validity. Future studies should explore patient-specific data integration and AI comparisons to refine its role in sarcopenia management.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jcm14051747
- https://www.mdpi.com/2077-0383/14/5/1747/pdf?version=1741166976
- OA Status
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- Cited By
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- References
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4408217805Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/jcm14051747Digital Object Identifier
- Title
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Assessing ChatGPT’s Role in Sarcopenia and Nutrition: Insights from a Descriptive Study on AI-Driven SolutionsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-05Full publication date if available
- Authors
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Özlem Karataş, Seden Demirci, Kaan Pota, Serpil TunaList of authors in order
- Landing page
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https://doi.org/10.3390/jcm14051747Publisher landing page
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https://www.mdpi.com/2077-0383/14/5/1747/pdf?version=1741166976Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2077-0383/14/5/1747/pdf?version=1741166976Direct OA link when available
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Medicine, Sarcopenia, Descriptive research, Gerontology, Internal medicine, Statistics, MathematicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.mean | 92 |
| abstract_inverted_index.poor | 115 |
| abstract_inverted_index.role | 167 |
| abstract_inverted_index.used | 22 |
| abstract_inverted_index.were | 41, 55 |
| abstract_inverted_index.with | 81, 118, 133 |
| abstract_inverted_index.(low) | 59 |
| abstract_inverted_index.0.5), | 96 |
| abstract_inverted_index.0.6), | 102, 106 |
| abstract_inverted_index.0.7). | 111 |
| abstract_inverted_index.While | 14, 139 |
| abstract_inverted_index.asked | 38 |
| abstract_inverted_index.poses | 10 |
| abstract_inverted_index.rated | 56 |
| abstract_inverted_index.scale | 49 |
| abstract_inverted_index.score | 93 |
| abstract_inverted_index.shows | 141 |
| abstract_inverted_index.their | 25 |
| abstract_inverted_index.tools | 16 |
| abstract_inverted_index.using | 46 |
| abstract_inverted_index.≥4. | 87 |
| abstract_inverted_index.(5.0), | 80 |
| abstract_inverted_index.(ICC). | 72 |
| abstract_inverted_index.Future | 154 |
| abstract_inverted_index.ensure | 151 |
| abstract_inverted_index.expert | 146 |
| abstract_inverted_index.health | 12 |
| abstract_inverted_index.highly | 127 |
| abstract_inverted_index.median | 78 |
| abstract_inverted_index.muscle | 6 |
| abstract_inverted_index.rating | 85 |
| abstract_inverted_index.refine | 165 |
| abstract_inverted_index.risks. | 13 |
| abstract_inverted_index.scores | 79 |
| abstract_inverted_index.should | 156 |
| abstract_inverted_index.showed | 114 |
| abstract_inverted_index.≥90% | 82 |
| abstract_inverted_index.(high), | 62 |
| abstract_inverted_index.clarity | 103 |
| abstract_inverted_index.decline | 4 |
| abstract_inverted_index.experts | 45 |
| abstract_inverted_index.explore | 157 |
| abstract_inverted_index.highest | 91 |
| abstract_inverted_index.patient | 144 |
| abstract_inverted_index.remains | 30, 148 |
| abstract_inverted_index.studies | 155 |
| abstract_inverted_index.(0.416), | 117 |
| abstract_inverted_index.(0.569). | 123 |
| abstract_inverted_index.Methods: | 32 |
| abstract_inverted_index.Results: | 73 |
| abstract_inverted_index.accuracy | 26, 99, 136 |
| abstract_inverted_index.analysis | 113 |
| abstract_inverted_index.assessed | 67 |
| abstract_inverted_index.clarity, | 52 |
| abstract_inverted_index.clarity. | 138 |
| abstract_inverted_index.clinical | 152 |
| abstract_inverted_index.followed | 97 |
| abstract_inverted_index.moderate | 121 |
| abstract_inverted_index.provides | 126 |
| abstract_inverted_index.received | 75 |
| abstract_inverted_index.relevant | 128 |
| abstract_inverted_index.unclear. | 31 |
| abstract_inverted_index.ChatGPT-4 | 18, 74, 125 |
| abstract_inverted_index.Relevance | 88 |
| abstract_inverted_index.Responses | 54 |
| abstract_inverted_index.accuracy, | 51 |
| abstract_inverted_index.agreement | 116, 122 |
| abstract_inverted_index.essential | 149 |
| abstract_inverted_index.evaluated | 42 |
| abstract_inverted_index.function, | 9 |
| abstract_inverted_index.oversight | 147 |
| abstract_inverted_index.potential | 142 |
| abstract_inverted_index.questions | 40 |
| abstract_inverted_index.responses | 34, 86, 131 |
| abstract_inverted_index.validity. | 153 |
| abstract_inverted_index.addressing | 28 |
| abstract_inverted_index.displaying | 120 |
| abstract_inverted_index.education, | 145 |
| abstract_inverted_index.evaluators | 84 |
| abstract_inverted_index.frequently | 37 |
| abstract_inverted_index.interrater | 64 |
| abstract_inverted_index.intraclass | 69 |
| abstract_inverted_index.sarcopenia | 29, 169 |
| abstract_inverted_index.structured | 130 |
| abstract_inverted_index.(relevance, | 50 |
| abstract_inverted_index.Background: | 0 |
| abstract_inverted_index.Sarcopenia, | 1 |
| abstract_inverted_index.age-related | 3 |
| abstract_inverted_index.coefficient | 71 |
| abstract_inverted_index.comparisons | 163 |
| abstract_inverted_index.correlation | 70 |
| abstract_inverted_index.healthcare, | 24 |
| abstract_inverted_index.integration | 160 |
| abstract_inverted_index.management. | 170 |
| abstract_inverted_index.reliability | 65 |
| abstract_inverted_index.significant | 11 |
| abstract_inverted_index.variability | 134 |
| abstract_inverted_index.(ChatGPT-4o) | 19 |
| abstract_inverted_index.Completeness | 119 |
| abstract_inverted_index.Conclusions: | 124 |
| abstract_inverted_index.completeness | 108 |
| abstract_inverted_index.consistently | 76 |
| abstract_inverted_index.increasingly | 21 |
| abstract_inverted_index.ChatGPT-4’s | 33 |
| abstract_inverted_index.Ccmpleteness). | 53 |
| abstract_inverted_index.four-criterion | 48 |
| abstract_inverted_index.patient-specific | 158 |
| abstract_inverted_index.sarcopenia-related | 39 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5060715292 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I197907453 |
| citation_normalized_percentile.value | 0.75828122 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |