Evidence map›Paper›PMID 40599373›Full record

ArticleSurgery in practice and science2025

Artificial intelligence in surgical care for low- and middle-income countries: Challenges, opportunities, and the path forward.

William Nkenguye

Abstract readLetter
PubMed Full text at PMC Publisher
In one paragraph

Article in Surgery in practice and science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Article
  6. Article
  7. Review
  8. 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

1 author.

William NkenguyeSchool of Public Health, Department of Epidemiology and Applied Biostatistics, KCMC University, P O Box 2240, Moshi, Tanzania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The application of Artificial Intelligence (AI) and Machine Learning (ML) in surgical care has significantly advanced healthcare delivery in high-income countries (HICs), yet remains underutilized in lower- and middle-income countries (LMICs). With a growing burden of surgical diseases, limited surgical expertise, and constrained healthcare resources, AI and ML offer transformative potential in optimizing surgical workflows, improving patient outcomes, and expanding access to specialized care. This manuscript explores the current status, challenges, and future prospects of AI and ML in surgical care for LMICs, emphasizing the critical need for investment, capacity building, and policy development to bridge the healthcare gap.

Indexed as

AfricaArtificial intelligenceAugmented realityDigital healthHealthcare innovationLMICsMachine learningSurgery

Identifiers

PMID40599373
PMCPMC12208932

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.