Evidence map›Paper›PMID 37444647›Full record

ArticleHealthcare (Basel, Switzerland)2023

ChatGPT and the Future of Digital Health: A Study on Healthcare Workers' Perceptions and Expectations.

Mohamad-Hani Temsah, Fadi Aljamaan, Khalid H Malki, Khalid Alhasan, Ibraheem Altamimi, Razan Aljarbou, Faisal Bazuhair, Abdulmajeed Alsubaihin, Naif Abdulmajeed, Fatimah S Alshahrani and 9 more

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.

0numbers the graph read from it
0cells of the map it votes in
79citing 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

79 citing papers in PubMed.

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19 more citing papers are in PubMed but not listed here.

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

19 authors.

Mohamad-Hani TemsahCollege of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.ORCID 0000-0002-4389-9322
Fadi AljamaanCollege of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.ORCID 0000-0001-8404-6652
Khalid H MalkiResearch Chair of Voice, Swallowing, and Communication Disorders, ENT Department, College of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.ORCID 0000-0002-5986-7843
Khalid AlhasanCollege of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.ORCID 0000-0002-4291-8536
Ibraheem AltamimiCollege of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.ORCID 0000-0002-5149-0877
Razan AljarbouPediatric Department, King Saud University Medical City, King Saud University, Riyadh 11411, Saudi Arabia.
Faisal BazuhairPediatric Department, King Saud University Medical City, King Saud University, Riyadh 11411, Saudi Arabia.
Abdulmajeed AlsubaihinCollege of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.
Naif AbdulmajeedPediatric Department, King Saud University Medical City, King Saud University, Riyadh 11411, Saudi Arabia.
Fatimah S AlshahraniCollege of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.
Reem TemsahCollege of Pharmacy, Alfaisal University, Riyadh 11533, Saudi Arabia.
Turki AlshahraniPediatric Department, King Saud University Medical City, King Saud University, Riyadh 11411, Saudi Arabia.
Lama Al-EyadhyCollege of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.
Serin Mohammed AlkhateebCollege of Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan.
Basema SaddikSharjah Institute of Medical Research, University of Sharjah, Sharjah 27272, United Arab Emirates.ORCID 0000-0002-4682-5927
Rabih HalwaniSharjah Institute of Medical Research, University of Sharjah, Sharjah 27272, United Arab Emirates.
Amr JamalCollege of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.ORCID 0000-0002-4051-6592
Jaffar A Al-TawfiqSpecialty Internal Medicine and Quality Department, Johns Hopkins Aramco Healthcare, Dhahran 34465, Saudi Arabia.ORCID 0000-0002-5752-2235
Ayman Al-EyadhyCollege of Medicine, King Saud University, Riyadh 11587, Saudi Arabia.ORCID 0000-0002-6051-9125

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to assess the knowledge, attitudes, and intended practices of healthcare workers (HCWs) in Saudi Arabia towards ChatGPT, an artificial intelligence (AI) Chatbot, within the first three months after its launch. We also aimed to identify potential barriers to AI Chatbot adoption among healthcare professionals. A cross-sectional survey was conducted among 1057 HCWs in Saudi Arabia, distributed electronically via social media channels from 21 February to 6 March 2023. The survey evaluated HCWs' familiarity with ChatGPT-3.5, their satisfaction, intended future use, and perceived usefulness in healthcare practice. Of the respondents, 18.4% had used ChatGPT for healthcare purposes, while 84.1% of non-users expressed interest in utilizing AI Chatbots in the future. Most participants (75.1%) were comfortable with incorporating ChatGPT into their healthcare practice. HCWs perceived the Chatbot to be useful in various aspects of healthcare, such as medical decision-making (39.5%), patient and family support (44.7%), medical literature appraisal (48.5%), and medical research assistance (65.9%). A majority (76.7%) believed ChatGPT could positively impact the future of healthcare systems. Nevertheless, concerns about credibility and the source of information provided by AI Chatbots (46.9%) were identified as the main barriers. Although HCWs recognize ChatGPT as a valuable addition to digital health in the early stages of adoption, addressing concerns regarding accuracy, reliability, and medicolegal implications is crucial. Therefore, due to their unreliability, the current forms of ChatGPT and other Chatbots should not be used for diagnostic or treatment purposes without human expert oversight. Ensuring the trustworthiness and dependability of AI Chatbots is essential for successful implementation in healthcare settings. Future research should focus on evaluating the clinical outcomes of ChatGPT and benchmarking its performance against other AI Chatbots.

Indexed as

AI chatbotsartificial intelligenceChatGPTcredibilityhealthcare workersmedicolegal implicationsperception

Identifiers

PMID37444647
PMCPMC10340744

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.