Evidence map›Paper›PMID 37692604›Full record

ReviewCureus2023

Role of Artificial Intelligence in Global Surgery: A Review of Opportunities and Challenges.

Kashish Malhotra, Benjamin Ngie Xiong Wong, Susie Lee, Helena Franco, Carol Singh, Laura A Cabrera Silva, Habab Iraqi, Akatya Sinha, Sule Burger, Danyca Shadé Breedt and 3 more

Open access · diamondAbstract readReview
In one paragraph

Review in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
7.2field-weighted citation impact, top 2% of its field
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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

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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

13 authors at 2 institutions in 2 countries.

Kashish MalhotraDepartment of Surgery, Dayanand Medical College and Hospital, Ludhiana, IND.
Benjamin Ngie Xiong WongDepartment of Surgery, Austin Hospital, Melbourne, AUS.
Susie LeeDepartment of Orthopaedics, Toowoomba Hospital, Queensland, AUS.
Helena FrancoDepartment of Surgery, Bond University, Queensland, AUS.
Carol SinghDepartment of Surgery, Dayanand Medical College and Hospital, Ludhiana, IND.
Laura A Cabrera SilvaDepartment of Surgery, Universidad El Bosque, Bogotá, COL.
Habab IraqiDepartment of Surgery, Al-Yarmouk College of Medical Sciences, Khartoum, SDN.
Akatya SinhaDepartment of Surgery, MGM (Mahatma Gandhi Mission's) Medical College and Hospital, Mumbai, IND.
Sule BurgerDepartment of Surgery, Ngwelezana Hospital, KwaZulu-Natal, ZAF.
Danyca Shadé BreedtDepartment of Surgery, King Edward VIII Hospital, Durban, ZAF.
Kashish GoyalDepartment of Internal Medicine, Dayanand Medical College and Hospital, Ludhiana, IND.
Mert Marcel DagliDepartment of Neurosurgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA.
Ashvind BawaDepartment of Surgery, Dayanand Medical College and Hospital, Ludhiana, IND.
Dayanand Medical College & Hospital · INUniversity of Pennsylvania · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Global surgery broadly refers to a rapidly expanding multidisciplinary field concerned with providing better and equitable surgical care across international health systems. Global surgery initiatives primarily focus on capacity building, advocacy, education, research, and policy development in low- and middle-income countries (LMICs). The inadequate surgical, anesthetic, and obstetric care currently contributes to 18 million preventable deaths each year. Hence, there is a growing interest in the rapid growth of artificial intelligence (AI) that provides a distinctive opportunity to enhance surgical services in LMICs. AI modalities have been used for personalizing surgical education, automating administrative tasks, and developing realistic and cost-effective simulation-training programs with provisions for people with special needs. Furthermore, AI may assist with providing insights for governance, infrastructure development, and monitoring/predicting stock take or logistics failure that can help in strengthening global surgery pillars. Numerous AI-assisted telemedicine-based platforms have allowed healthcare professionals to virtually assist in complex surgeries that may help to improve surgical accessibility across LMICs. Challenges in implementing AI technology include the misrepresentation of minority populations in the datasets leading to discriminatory bias. Human hesitancy, employment uncertainty, automation bias, and role of confounding factors need to be further studied for equitable utilization of AI. With a focused and evidence-based approach, AI could help several LMICs overcome bureaucratic inefficiency and develop more efficient surgical systems.

Indexed as

ai and robotics in healthcareartificial intelligenceglobal healthglobal surgerylow- and-middle-income countriessurgical equity

Identifiers

PMID37692604
PMCPMC10486145
OpenAlexW4385703424

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.