Evidence map›Paper›PMID 38615289›Full record

Observational studyEuropean geriatric medicine2024

Geriatrics and artificial intelligence in Spain (Ger-IA project): talking to ChatGPT, a nationwide survey.

Daniel Rosselló-Jiménez, S Docampo, Y Collado, L Cuadra-Llopart, F Riba, M Llonch-Masriera

Abstract readObservational Study
PubMed Publisher
In one paragraph

Observational study in European geriatric medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Observational
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Daniel Rosselló-JiménezGeriatric Medicine Department, Hospital Universitari de Terrassa, Consorci Sanitari de Terrassa, Carr. Torrebonica, s/n, Terrassa, 08227, Barcelona, Spain. rossello.daniel@gmail.com.ORCID 0000-0001-9734-2274
S DocampoGeriatric Medicine Department, Hospital Santa Creu, Tortosa, Tortosa, Tarragona, Spain.ORCID 0000-0002-6817-5769
Y ColladoGeriatric Medicine Department, Hospital Universitari de Terrassa, Consorci Sanitari de Terrassa, Carr. Torrebonica, s/n, Terrassa, 08227, Barcelona, Spain.
L Cuadra-LlopartGeriatric Medicine Department, Hospital Universitari de Terrassa, Consorci Sanitari de Terrassa, Carr. Torrebonica, s/n, Terrassa, 08227, Barcelona, Spain.ORCID 0000-0002-9787-4876
F RibaGeriatric Medicine Department, Hospital Santa Creu, Tortosa, Tortosa, Tarragona, Spain.ORCID 0000-0002-7703-2946
M Llonch-MasrieraGeriatric Medicine Department, Hospital Universitari de Terrassa, Consorci Sanitari de Terrassa, Carr. Torrebonica, s/n, Terrassa, 08227, Barcelona, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe purposes of the study was to describe the degree of agreement between geriatricians with the answers given by an AI tool (ChatGPT) in response to questions related to different areas in geriatrics, to study the differences between specialists and residents in geriatrics in terms of the degree of agreement with ChatGPT, and to analyse the mean scores obtained by areas of knowledge/domains.

methodsAn observational study was conducted involving 126 doctors from 41 geriatric medicine departments in Spain. Ten questions about geriatric medicine were posed to ChatGPT, and doctors evaluated the AI's answers using a Likert scale. Sociodemographic variables were included. Questions were categorized into five knowledge domains, and means and standard deviations were calculated for each.

results130 doctors answered the questionnaire. 126 doctors (69.8% women, mean age 41.4 [9.8]) were included in the final analysis. The mean score obtained by ChatGPT was 3.1/5 [0.67]. Specialists rated ChatGPT lower than residents (3.0/5 vs. 3.3/5 points, respectively, P < 0.05). By domains, ChatGPT ​​scored better (M: 3.96; SD: 0.71) in general/theoretical questions rather than in complex decisions/end-of-life situations (M: 2.50; SD: 0.76) and answers related to diagnosis/performing of complementary tests obtained the lowest ones (M: 2.48; SD: 0.77).

conclusionScores presented big variability depending on the area of knowledge. Questions related to theoretical aspects of challenges/future in geriatrics obtained better scores. When it comes to complex decision-making, appropriateness of the therapeutic efforts or decisions about diagnostic tests, professionals indicated a poorer performance. AI is likely to be incorporated into some areas of medicine, but it would still present important limitations, mainly in complex medical decision-making.

Indexed as

Artificial IntelligenceGeriatricsAdultAgedFemaleGeriatriciansHumansMaleMiddle AgedSpainSurveys and QuestionnairesAgeingAI (artificial intelligence)Deep learningGeriatricsMedical decision-makingOlder people

Identifiers

PMID38615289

What OpenQuestion holds

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Registered trials

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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.