ArticleRevista da Associacao Medica Brasileira (1992)2026
Can artificial intelligence manage malnutrition? A critical look at ChatGPT's performance in geriatric nutrition.
Article in Revista da Associacao Medica Brasileira (1992), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence represents a rapidly advancing innovation in healthcare with the potential to revolutionize the field of clinical nutrition.
objectiveThe aim of this study was to evaluate ChatGPT's potential to support the clinical decision-making process regarding nutrition in older adults.
methodsTwelve questions and three clinical vignettes addressing fundamental concepts of malnutrition, including general information, diagnosis, follow-up, and treatment, were created and asked to ChatGPT. Three geriatricians independently examined ChatGPT's responses. The quality of the responses was assessed using the Quality Analysis of Medical Artificial Intelligence tool.
resultsThe inter-rater reliability among the authors was calculated, and an excellent intraclass correlation coefficient of 0.84 (95%CI 0.77-0.89; p<0.001) was found. The total mean Quality Analysis of Medical Artificial Intelligence score for the ChatGPT-generated responses to questions related to malnutrition was 26.60, indicating very good quality. In evaluating the clinical scenarios, the lowest scores were observed in source use. The total Quality Analysis of Medical Artificial Intelligence, accuracy, relevance, and use of sources scores for the clinical scenario involving the patient with a hip fracture were statistically significantly lower compared to other scenarios.
conclusionOur study highlighted that ChatGPT has the potential to generate correct answers related to complex clinical scenarios about malnutrition. ChatGPT can help clinicians make more informed decisions regarding patients' nutritional requirements and management by utilizing more up-to-date medical resources and guidelines.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.