SynthesisRevista medica del Instituto Mexicano del Seguro Social2025
[Medical education and artificial intelligence: perspectives and ethical challenges].
Synthesis in Revista medica del Instituto Mexicano del Seguro Social, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- [Diagnosis of congenital heart disease using Deep Learning in pediatric chest X-rays: A proof of concept].Revista medica del Instituto Mexicano del Seguro Social · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) drives innovation in medical education by enabling personalized learning, instant feedback, and clinical simulations. However, it raises concerns such as loss of human skills, misinformation, and ethical issues. ChatGPT has gained prominence for its natural language generation capabilities, despite lacking real understanding. The aim of this article was to analyze the use of AI, particularly ChatGPT, in medical education, identifying its benefits, limitations, and ethical implications, which is why a qualitative integrative review was conducted through a systematic search in PubMed (2020-2025), using MeSH descriptors related to artificial intelligence and medical education. A total of 37 open-access, full-text articles in English were included. The information was analyzed and synthesized using a thematic approach, organized into 4 categories: pedagogical applications, benefits, challenges, and ethical recommendations, thanks to which we had these results: AI supports clinical simulations, personalized learning, and equitable access. Benefits include enhanced clinical reasoning and autonomous learning. Challenges remain, such as bias, data privacy, and misinformation. Five pillars for integration are proposed, along with a professional classification into consumers, translators, and developers. Digital curation emerges as a key element to ensure quality and reliability. We conclude that AI can transform medical education if implemented ethically, critically, and with a human-centered approach.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.