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Health·The Trace·Dual reading·Published 2026-08-06

integration of AI and large language models in medical education

Source article: The ethical challenges in the integration of artificial intelligence and large language models in medical education: A scoping review

Abstract: With the rapid development of artificial intelligence (AI), large language models (LLMs), such as ChatGPT have shown potential in medical education, offering personalized learning experiences. However, this integration raises ethical concerns, including privacy, autonomy, and transparency. This study employed a scoping review methodology, systematically searching relevant literature published between January 2010 and August 31, 2024, across three major databases: PubMed, Embase, and Web of Science. Through rigor…

TRV-2026-0670Peer-reviewedPermanent record — cite & verify
Trace impact reading

Negative state: both sides are scored from claims and sources, not community votes.

P 75The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.G 68The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.
The ethical challenges in the integration of artificial intelligence and large language models in medical education: A scoping review

AI governance guideline publications by institution types by Authors of the study: Nicholas Kluge Corrêa Camila Galvão James William Santos Carolina Del Pino Edson Pontes Pinto Camila Barbosa Diogo Massmann Rodrigo Mambrini Luiza Galvão Edmund Terem Nythamar de Oliveira. CC BY 4.0 · https://creativecommons.org/licenses/by/4.0

The quick read

Published October 22, 2025, this PLOS One scoping review examined literature on AI and large language models like ChatGPT in medical education. It found potential for personalized learning alongside a set of ethical challenges, synthesizing 50 studies from three major databases covering 2010 to August 2024.

The review matters because medical schools are already experimenting with LLM tutors and assessment aids, yet governance remains fragmented. It organizes risks into seven dimensions and proposes mitigation strategies, but as of its August 2024 search cutoff it does not test interventions or long-term outcomes, leaving effectiveness and durability of safeguards uncertain.

Main points
  • Scoping review screened 1,192 records from PubMed, Embase, and Web of Science for literature between January 2010 and August 31, 2024.
  • 50 articles met inclusion criteria after rigorous screening.
  • Kimi AI tool was used for preliminary screening and extraction, with two independent researchers validating all AI-generated content against original sources.
Gain

Large language models such as ChatGPT can provide personalized learning experiences when integrated into medical education.

Problem

Integrating AI and LLMs into medical education raises ethical concerns across privacy and data security, algorithmic bias, accountability, fairness, reliability, dependency, and patient autonomy.

The rundown

The authors searched PubMed, Embase, and Web of Science for literature published between January 2010 and August 31, 2024, starting from 1,192 records and selecting 50 articles that met inclusion criteria.

During data processing the Kimi AI tool was utilized to facilitate preliminary literature screening, extraction of key information, and construction of content frameworks, with reliability ensured through cross-verification by two independent researchers against original source materials.

Sources

Reader signal

How should this claim be treated?

The debate