Peer-reviewedCC BY-NC-NDDanish medical journal
Risks of automation in medicine - a review article for the obstetrics case
Abstract
The current overoptimism surrounding AI threatens to disrupt clinical environments. We discuss the risks related to invasive automation and describe how AI performance exaggeration may lead to clinical errors. We also investigate cognitive damage to clinicians: automation bias and de-skilling, by exploring their relation to well-defined psychological principles. Finally, we propose solutions for automation that actually benefit medicine: generalise model bias assessments and quality control, and ensure that AI integration does not erode clinicians' clinical expertise.
Related
- Menopause Education in Obstetrics and Gynecology Residency Training: A Scoping Review
- Review Article of Platelet-Rich Plasma and Facet-Mediated Mechanical Low Back Pain
- The Intertwined Threat: How Diabetes Impacts Neurological Disorders – A Review Article
- Orthopedic Risks and Recovery Considerations in Long COVID: A Narrative Review
- Balancing clinical benefits and behavioral risks of obesity-focused management in youth-onset type 2 diabetes: A narrative review
BibTeX
@article{fournel2026risks,
title = {{Risks of automation in medicine - a review article for the obstetrics case}},
author = {Joris Fournel and Aasa Feragen and Martin Tolsgaard},
journal = {Danish medical journal},
year = {2026},
month = oct,
doi = {10.61409/a10250852},
url = {https://hal.science/hal-05788036v1},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Risks of automation in medicine - a review article for the obstetrics case
AU - Joris Fournel
AU - Aasa Feragen
AU - Martin Tolsgaard
PY - 2026
DA - 2026/10/09
JO - Danish medical journal
DO - 10.61409/a10250852
UR - https://hal.science/hal-05788036v1
AB - The current overoptimism surrounding AI threatens to disrupt clinical environments. We discuss the risks related to invasive automation and describe how AI performance exaggeration may lead to clinical errors. We also investigate cognitive damage to clinicians: automation bias and de-skilling, by exploring their relation to well-defined psychological principles. Finally, we propose solutions for automation that actually benefit medicine: generalise model bias assessments and quality control, and ensure that AI integration does not erode clinicians' clinical expertise.
ER -