Evidence map›Paper›PMID 37560249›Full record

ReviewAmerican journal of translational research2023

Artificial intelligence for healthcare and medical education: a systematic review.

Li Sun, Changhao Yin, Qiuling Xu, Weina Zhao

Abstract readReview
PubMed Full text at PMC
In one paragraph

Review in American journal of translational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
70citing 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

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

  1. Pooled it
  2. Trial
  3. Bridging Borders, Bridging Barriers: Artificial Intelligence for Dental Education.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026
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  7. Are Psychiatry Faculty Prepared to Guide AI Use? A National Survey.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026
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  14. Observational
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10 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

4 authors.

Li SunDepartment of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University Mudanjiang 157011, Heilongjiang, China.
Changhao YinDepartment of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University Mudanjiang 157011, Heilongjiang, China.
Qiuling XuDepartment of Physiology, Mudanjiang Medical University Mudanjiang 157011, Heilongjiang, China.
Weina ZhaoDepartment of Neurology, Hongqi Hospital Affiliated to Mudanjiang Medical University Mudanjiang 157011, Heilongjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHuman society has entered the age of artificial intelligence, medical practice and medical education are undergoing profound changes. Artificial intelligence (AI) is now applied in many industries, particularly in healthcare and medical education, where it deeply intersects. The purpose of this paper is to overview the current situation and problems of "AI+medicine/medical" education and to provide our own perspective on the current predicament.

methodsWe searched PubMed, Embase, Cochrane and CNKI databases to assess the literature on AI+medical/medical education from 2017 to July 2022. The main inclusion criteria include literature describing the current situation or predicament of "AI+medical/medical education".

resultsStudies have shown that the current application of AI in medical education is focused on clinical specialty training and continuing education, with the main application areas being radiology, diagnostics, surgery, cardiology, and dentistry. The main role is to assist physicians to improve their efficiency and accuracy. In addition, the field of combining AI with medicine/medical education is steadily expanding, and the most urgent need is for policy makers, experts in the medical field, AI and education, and experts in other fields to come together to reach consensus on ethical issues and develop regulatory standards. Our study also found that most medical students are positive about adding AI-related courses to the existing medical curriculum. Finally, the quality of research on "AI+medical/medical education" is poor.

conclusionIn the context of the COVID-19 pandemic, our study provides an innovative systematic review of the latest "AI+medicine/medical curriculum". Since the AI+medicine curriculum is not yet regulated, we have made some suggestions.

Indexed as

Artificial intelligencehealthcaremedical educationreview

Identifiers

PMID37560249
PMCPMC10408516

What OpenQuestion holds

Textmetadata
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.