AI Timeouts: A Structured Approach to Deliberate AI Use in Clinician Education
- 1Department of Medicine VA West Los Angeles Medical Center
- 2Department of Medicine UCLA
Abstract
The rapid adoption of generative artificial intelligence (AI) in clinical medicine has outpaced structured education and guidance on how to safely use this new tool. This gap is particularly consequential in medical education where lack of deliberate use of this tool can lead to poor development of clinical reasoning. We propose a set of AI Timeouts for clinician educators, a pair of structured cognitive pauses adapted from the pre-procedural timeout and cognitive forcing strategies used to mitigate diagnostic error. The Intent Timeout precedes AI use and prompts clinicians to consider whether AI is the appropriate tool for the task, address privacy and data stewardship concerns, and weigh the potential impact on their own clinical reasoning. The Appraisal Timeout follows AI use and guides clinicians to evaluate the fit, rationale, and actionable priority of an AI-generated output before acting on it. Because the framework is grounded in a checklist practice already familiar to all clinicians, it requires no specialized AI literacy and can be adopted immediately by an individual clinician, a supervising attending, or an institution.
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BibTeX
@article{tran2026timeouts,
title = {{AI Timeouts: A Structured Approach to Deliberate AI Use in Clinician Education}},
author = {George Tran and Edward Lee and Satya Patel},
journal = {Journal of Brown hospital medicine},
year = {2026},
month = oct,
doi = {10.56305/001c.170189},
url = {https://europepmc.org/article/PMC/PMC13730618},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - AI Timeouts: A Structured Approach to Deliberate AI Use in Clinician Education
AU - George Tran
AU - Edward Lee
AU - Satya Patel
PY - 2026
DA - 2026/10/01
JO - Journal of Brown hospital medicine
DO - 10.56305/001c.170189
UR - https://europepmc.org/article/PMC/PMC13730618
AB - The rapid adoption of generative artificial intelligence (AI) in clinical medicine has outpaced structured education and guidance on how to safely use this new tool. This gap is particularly consequential in medical education where lack of deliberate use of this tool can lead to poor development of clinical reasoning. We propose a set of AI Timeouts for clinician educators, a pair of structured cognitive pauses adapted from the pre-procedural timeout and cognitive forcing strategies used to mitigate diagnostic error. The Intent Timeout precedes AI use and prompts clinicians to consider whether AI is the appropriate tool for the task, address privacy and data stewardship concerns, and weigh the potential impact on their own clinical reasoning. The Appraisal Timeout follows AI use and guides clinicians to evaluate the fit, rationale, and actionable priority of an AI-generated output before acting on it. Because the framework is grounded in a checklist practice already familiar to all clinicians, it requires no specialized AI literacy and can be adopted immediately by an individual clinician, a supervising attending, or an institution.
SN - 2994-5593
ER -