Sycophantic AI, critical-feedback tolerance, and the case for productive friction in medical training
Concerns about generative AI in medical training often focus on eroded reasoning and deskilling. Such competence-based accounts are incomplete. We identify a neglected casualty: critical-feedback tolerance (CFT), the capacity to receive disconfirming evaluative feedback and remain productively engaged. Affirmation-optimized systems may lower CFT undetectably, surfacing only under authentic critique. This conceptual Perspective defines CFT, differentiates it from six established constructs, specifies measurable facets, and states explicit refutation conditions.
Authors
- Zhi Chen (ORCID: https://orcid.org/0000-0002-9467-9151)
- 吉康祥 (ORCID: https://orcid.org/0000-0002-5679-2348)
- 贾波
- Mengyun Sui
- Long Xue
- Huilin Sheng
Institutions
- Shanghai Medical College of Fudan University (CN)
- Fudan University (CN)
- Huadong Hospital (CN)
- Pudong Maternal and Child Health Hospital (CN)
- Central Hospital of Putuo District (CN)
- First Affiliated Hospital of Soochow University (CN)
- Huashan Hospital (CN)
- Shanghai Municipal Center For Disease Control Prevention (CN)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2026-10-16
- DOI
- https://doi.org/10.1038/s41746-026-03389-5
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Science and Technology Major Project