Evidence map›Paper›PMID 41846502›Full record

ArticlePsychiatry investigation2026

Technostress in the Era of Generative Artificial Intelligence: Mental Health Implications in Medical Education.

Yoo Jin Um

Abstract read
In one paragraph

Article in Psychiatry investigation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Yoo Jin UmDepartment of Medical Education, College of Medicine, Hanyang University, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveGenerative artificial intelligence (GenAI) has rapidly expanded across education. While its educational benefits are widely recognized, concerns are emerging regarding its potential psychological impact. This narrative review aimed to synthesize current evidence on GenAI-related technostress and associated mental health risks among medical trainees and educators.

methodsA narrative review was conducted based on a systematic search of PubMed, supplemented by reference list tracking. Publications from January 2010 to January 2026 were considered, with emphasis on studies published after the release of ChatGPT in 2022.

resultsEmerging evidence suggests that GenAI use introduces increased demands, including cognitive monitoring burden, ambiguity regarding appropriate use, and performance-related pressures. These factors may contribute to technostress, digital fatigue, and emotional strain. Medical trainees appear particularly vulnerable due to high-stakes evaluation, academic integrity concerns, and ongoing professional identity formation, whereas educators more commonly experience role overload, techno-uncertainty, and concerns about professional displacement. Conceptual integration with stress-vulnerability and problematic technology use models suggests potential pathways linking GenAI exposure to anxiety, burnout, sleep disturbance, and dependency-like use patterns.

conclusionGenAI is emerging not only as a powerful educational tool but also as a potential source of technology-related psychological strain. Informing by psychiatric perspectives strategies and clear institutional guidance may be essential to maximize the benefits of GenAI while mitigating its unintended mental health risks in medical education settings.

Indexed as

BurnoutDigital fatigueGenerative artificial intelligenceMedical education

Identifiers

PMID41846502
PMCPMC13084270

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.