Evidence map›Paper›PMID 39011463›Full record

ArticleThe Journal of innovations in cardiac rhythm management2024

ChatGPT for Patients: A Comprehensive Study on Atrial Fibrillation Awareness.

Rahul Vyas, Arpita Pawa, Chanza Shaikh, Anaiya Singh, Hetvi Shah, Shubhika Jain, Vijaywant Brar

Abstract read
In one paragraph

Article in The Journal of innovations in cardiac rhythm management, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

7 authors.

Rahul VyasDepartment of Internal Medicine, LSU, Shreveport, LA, USA.
Arpita PawaDepartment of Internal Medicine, Willis-Knighton Health System, Shreveport, LA, USA.
Chanza ShaikhDepartment of Internal Medicine, LSU, Shreveport, LA, USA.
Anaiya SinghDepartment of Internal Medicine, LSU, Shreveport, LA, USA.
Hetvi ShahR.C.S.M Government Medical College, Kolhapur, Maharashtra, India.
Shubhika JainKasturba Medical College, Manipal, Karnataka, India.
Vijaywant BrarDepartment of Cardiology, LSU, Shreveport, LA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Due to the intricate nature of atrial fibrillation (AF), the diagnostic process often gives rise to a spectrum of concerns and inquiries. A 20-question survey on AF, covering general concerns, diagnosis, treatment, and post-diagnosis inquiries, was conducted via Google Forms (Google LLC, Mountain View, CA, USA). The questions were input into the Chat Generative Pre-trained Transformer (ChatGPT) system (OpenAI LP, San Francisco, CA, USA) in November 2023, and the responses were meticulously collated within the same Google Forms. The survey, involving 30 experienced physicians, including 22 cardiologists and 8 hospitalists, practicing for an average of 18 years, assessed artificial intelligence (AI)-generated responses to 20 medical queries. Out of 600 evaluations, "excellent" responses were most common (29.50%), followed by "very good" (26%), "good" (19.50%), and "fair" (17.3%). The least common response was "poor" (7.67%). Questions were categorized into "general concerns," "diagnosis-related," "treatment-related," and "post-diagnosis general questions." Across all categories, >50% of experts rated responses as "excellent" or "very good," indicating the potential for improvement in the AI's clinical response methodology. This study highlights the efficacy of ChatGPT as an AF informational resource, with expert-rated responses comparable to those of clinicians. While proficient, concerns include infrequent updates and ethical considerations. Nevertheless, it underscores the growing role of AI in health care information access.

Indexed as

Artificial intelligenceatrial fibrillationChatGPT

Identifiers

PMID39011463
PMCPMC11238883

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.