Observational studyJMIR formative research2025
ChatGPT's Attitude, Knowledge, and Clinical Application in Geriatrics Practice and Education: Exploratory Observational Study.
Observational study in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Article
- AI-assisted PEG aftercare education for older adults: clinician-informed chatbot design (PEGAssist).European geriatric medicine · 2026Article
- A comparative evaluation of ChatGPT and Gemini for stroke caregiver support: accuracy, safety, and response characteristics.Revista da Associacao Medica Brasileira (1992) · 2026Article
- ChatGPT Performance Deteriorated in Patients with Comorbidities When Providing Cardiological Therapeutic Consultations.Healthcare (Basel, Switzerland) · 2025Article
- An artificial intelligence perspective on geriatric syndromes: assessing the information accuracy and readability of ChatGPT.European geriatric medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe increasing use of ChatGPT in clinical practice and medical education necessitates the evaluation of its reliability, particularly in geriatrics.
objectiveThis study aimed to evaluate ChatGPT's trustworthiness in geriatrics through 3 distinct approaches: evaluating ChatGPT's geriatrics attitude, knowledge, and clinical application with 2 vignettes of geriatric syndromes (polypharmacy and falls).
methodsWe used the validated University of California, Los Angeles, geriatrics attitude and knowledge instruments to evaluate ChatGPT's geriatrics attitude and knowledge and compare its performance with that of medical students, residents, and geriatrics fellows from reported results in the literature. We also evaluated ChatGPT's application to 2 vignettes of geriatric syndromes (polypharmacy and falls).
resultsThe mean total score on geriatrics attitude of ChatGPT was significantly lower than that of trainees (medical students, internal medicine residents, and geriatric medicine fellows; 2.7 vs 3.7 on a scale from 1-5; 1=strongly disagree; 5=strongly agree). The mean subscore on positive geriatrics attitude of ChatGPT was higher than that of the trainees (medical students, internal medicine residents, and neurologists; 4.1 vs 3.7 on a scale from 1 to 5 where a higher score means a more positive attitude toward older adults). The mean subscore on negative geriatrics attitude of ChatGPT was lower than that of the trainees and neurologists (1.8 vs 2.8 on a scale from 1 to 5 where a lower subscore means a less negative attitude toward aging). On the University of California, Los Angeles geriatrics knowledge test, ChatGPT outperformed all medical students, internal medicine residents, and geriatric medicine fellows from validated studies (14.7 vs 11.3 with a score range of -18 to +18 where +18 means that all questions were answered correctly). Regarding the polypharmacy vignette, ChatGPT not only demonstrated solid knowledge of potentially inappropriate medications but also accurately identified 7 common potentially inappropriate medications and 5 drug-drug and 3 drug-disease interactions. However, ChatGPT missed 5 drug-disease and 1 drug-drug interaction and produced 2 hallucinations. Regarding the fall vignette, ChatGPT answered 3 of 5 pretests correctly and 2 of 5 pretests partially correctly, identified 6 categories of fall risks, followed fall guidelines correctly, listed 6 key physical examinations, and recommended 6 categories of fall prevention methods.
conclusionsThis study suggests that ChatGPT can be a valuable supplemental tool in geriatrics, offering reliable information with less age bias, robust geriatrics knowledge, and comprehensive recommendations for managing 2 common geriatric syndromes (polypharmacy and falls) that are consistent with evidence from guidelines, systematic reviews, and other types of studies. ChatGPT's potential as an educational and clinical resource could significantly benefit trainees, health care providers, and laypeople. Further research using GPT-4o, larger geriatrics question sets, and more geriatric syndromes is needed to expand and confirm these findings before adopting ChatGPT widely for geriatrics education and practice.
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.