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TRUVACE RECORD VERSION
record: TRV-2026-0670
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-06T14:24:54.285029Z
status: published
lens: trace
sector: health
headline: The ethical challenges in the integration of artificial intelligence and large language models in medical education: A scoping review
dek: 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…
gain_title: Large language models such as ChatGPT can provide personalized learning experiences when integrated into medical education.
problem_title: Integrating AI and LLMs into medical education raises ethical concerns across privacy and data security, algorithmic bias, accountability, fairness, reliability, dependency, and patient autonomy.
trace_subject: integration of AI and large language models in medical education
gain_reading: Large language models such as ChatGPT can provide personalized learning experiences when integrated into medical education.
gain_evidence: offering personalized learning experiences
problem_reading: Integrating AI and LLMs into medical education raises ethical concerns across privacy and data security, algorithmic bias, accountability, fairness, reliability, dependency, and patient autonomy.
problem_evidence: raises ethical concerns, including privacy, autonomy, and transparency | seven core ethical dimensions: privacy and data security, algorithmic bias, accountability attribution, fairness assurance, technological reliability, application dependency, and patient autonomy
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.
limitation: 
tag: Automated dual reading
key_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.
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:
- peer_reviewed | PLOS One | https://doi.org/10.1371/journal.pone.0333411 | 2025-10-22
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