Evidence map›Paper›PMID 39320544›Full record

ArticleEuropean geriatric medicine2024

Is Artificial Intelligence ageist?

Yanira Aranda Rubio, Juan José Baztán Cortés, Fernando Canillas Del Rey

Abstract read
PubMed Publisher
In one paragraph

Article 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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Yanira Aranda RubioGeriatrics Service, Hospital Universitario de la Cruz Roja, San José y Santa Adela, Avenue Reina Victoria, 22-26, Moncloa-Aravaca, 28003, Madrid, Spain. yanira.aranda@salud.madrid.org.ORCID 0000-0003-1901-5811
Juan José Baztán CortésGeriatrics Service, Hospital Universitario de la Cruz Roja, San José y Santa Adela, Avenue Reina Victoria, 22-26, Moncloa-Aravaca, 28003, Madrid, Spain.
Fernando Canillas Del ReyTraumatology and Orthopedic Surgery Service, Hospital Universitario de la Cruz Roja, San José y Santa Adela, Madrid, Spain.ORCID 0000-0002-3326-3494

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionGenerative Artificial Intelligence (AI) is a technological innovation with wide applicability in daily life, which could help elderly people. However, it raises potential conflicts, such as biases, omissions and errors.

methodsDescriptive study through the negative stereotypes towards aging questionnaire (CENVE) conducted on chatbots ChatGPT, Gemini, Perplexity, YOUChat, and Copilot was conducted.

resultsOf the chatbots studied, three were above 50% in responses with negative stereotypes, Copilot with high ageism level results, followed by Perplexity. In the health section, Copilot was the chatbot with the most negative connotations regarding old age (13 out of 20 points). In the personality section, Copilot scored 14 out of 20, followed by YOUChat.

conclusionThe Copilot chatbot responded to the statements more ageistically than the other platforms. These results highlight the importance of addressing any potential biases in AI to ensure that the responses provided are fair and respectful for all potential users.

Indexed as

AgeismArtificial IntelligenceAgingGenerative Artificial IntelligenceHumansSocial MediaStereotypingSurveys and QuestionnairesChatbotsGenerative Artificial IntelligenceNegative stereotypesOlder people

Identifiers

PMID39320544

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.