Evidence map›Paper›PMID 37731897›Full record

ReviewThe Lancet regional health. Western Pacific2023

ChatGPT: promise and challenges for deployment in low- and middle-income countries.

Xiaofei Wang, Hayley M Sanders, Yuchen Liu, Kennarey Seang, Bach Xuan Tran, Atanas G Atanasov, Yue Qiu, Shenglan Tang, Josip Car, Ya Xing Wang and 3 more

Abstract readReview
In one paragraph

Review in The Lancet regional health. Western Pacific, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 2 pooled it
–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

73 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Article
  6. Local LLMs as cooperative agents for low-cost surgical navigation support.International journal of computer assisted radiology and surgery · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Artificial General Intelligence and Its Threat to Public Health.Journal of evaluation in clinical practice · 2025
    Article

13 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

13 authors.

Xiaofei WangKey Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Hayley M SandersSection of Plastic Surgery, Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA.
Yuchen LiuKey Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Kennarey SeangGrant Management Office, University of Health Sciences, Phnom Penh, Cambodia.
Bach Xuan TranDepartment of Health Economics, Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam.
Atanas G AtanasovLudwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
Yue QiuInstitute for Hospital Management, Tsinghua University, Beijing, China.
Shenglan TangDuke Global Health Institute, Duke University, Durham, NC, USA.
Josip CarCentre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore.
Ya Xing WangBeijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Tien Yin WongSingapore Eye Research Institute, Singapore National Eye Centre, Singapore.
Yih-Chung ThamSingapore Eye Research Institute, Singapore National Eye Centre, Singapore.
Kevin C ChungSection of Plastic Surgery, Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In low- and middle-income countries (LMICs), the fields of medicine and public health grapple with numerous challenges that continue to hinder patients' access to healthcare services. ChatGPT, a publicly accessible chatbot, has emerged as a potential tool in aiding public health efforts in LMICs. This viewpoint details the potential benefits of employing ChatGPT in LMICs to improve medicine and public health encompassing a broad spectrum of domains ranging from health literacy, screening, triaging, remote healthcare support, mental health support, multilingual capabilities, healthcare communication and documentation, medical training and education, and support for healthcare professionals. Additionally, we also share potential concerns and limitations associated with the use of ChatGPT and provide a balanced discussion on the opportunities and challenges of using ChatGPT in LMICs.

Indexed as

ChatGPTEquityGlobal healthLarge language modelLow to middle income countriesPublic health

Identifiers

PMID37731897
PMCPMC10507635

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.