Evidence map›Paper›PMID 39118788›Full record

ArticleFrontiers in artificial intelligence2024

Doctor AI? A pilot study examining responses of artificial intelligence to common questions asked by geriatric patients.

Ian Moore, Christopher Magnante, Ellie Embry, Jennifer Mathis, Scott Mooney, Shereen Haj-Hassan, Maria Cottingham, Prasad R Padala

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
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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

8 authors.

Ian MooreGeriatric Research Education and Clinical Center (GRECC), Central Arkansas Veterans Healthcare System (CAVHS), Little Rock, AR, United States.
Christopher MagnanteGeriatric Research Education and Clinical Center (GRECC), Central Arkansas Veterans Healthcare System (CAVHS), Little Rock, AR, United States.
Ellie EmbryGeriatric Research Education and Clinical Center (GRECC), Central Arkansas Veterans Healthcare System (CAVHS), Little Rock, AR, United States.
Jennifer MathisGeriatric Research Education and Clinical Center (GRECC), Central Arkansas Veterans Healthcare System (CAVHS), Little Rock, AR, United States.
Scott MooneyGeriatric Research Education and Clinical Center (GRECC), Central Arkansas Veterans Healthcare System (CAVHS), Little Rock, AR, United States.
Shereen Haj-HassanTennessee Valley Veteran Affairs Healthcare System (TVHS), Nashville, TN, United States.
Maria CottinghamTennessee Valley Veteran Affairs Healthcare System (TVHS), Nashville, TN, United States.
Prasad R PadalaGeriatric Research Education and Clinical Center (GRECC), Central Arkansas Veterans Healthcare System (CAVHS), Little Rock, AR, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: AI technologies have the potential to transform patient care. AI has been used to aid in differential diagnosis and treatment planning for psychiatric disorders, administer therapeutic protocols, assist with interpretation of cognitive testing, and patient treatment planning. Despite advancements, AI has notable limitations and remains understudied and further research on its strengths and limitations in patient care is required. This study explored the responses of AI (Chat-GPT 3.5) and trained clinicians to commonly asked patient questions. Methods: Three clinicians and AI provided responses to five dementia/geriatric healthcare-related questions. Responses were analyzed by a fourth, blinded clinician for clarity, accuracy, relevance, depth, and ease of understanding and to determine which response was AI generated. Results: AI responses were rated highest in ease of understanding and depth across all responses and tied for first for clarity, accuracy, and relevance. The rating for AI generated responses was 4.6/5 (SD = 0.26); the clinician s' responses were 4.3 (SD = 0.67), 4.2 (SD = 0.52), and 3.9 (SD = 0.59), respectively. The AI generated answers were identified in 4/5 instances. Conclusions: AI responses were rated more highly and consistently on each question individually and overall than clinician answers demonstrating that AI could produce good responses to potential patient questions. However, AI responses were easily distinguishable from those of clinicians. Although AI has the potential to positively impact healthcare, concerns are raised regarding difficulties discerning AI from human generated material, the increased potential for proliferation of misinformation, data security concerns, and more.

Indexed as

artificial intelligence (AI)dementiadiagnosis and treatment planninggeriatricsimmersive technologiespatient feedback

Identifiers

PMID39118788
PMCPMC11306168

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.