BiasMedium
COVID-19 Detection and Prognostication Models Allegedly Flagged for Methodological Flaws and Underlying Biases
- Occurred
- Jan 1, 2020
- Published
- Mar 15, 2021
- Source
- AI Incident Database
Summary
Peer-review of papers about COVID-19 detection and prognostication algorithms from 2020, including deployed models, revealed none to be ready for clinical use, due to methodological flaws and underlying biases such as lacking external validation or not specifying data sources and model training details.
Developers
- Icahn School of Medicine researchers
- Unknown
Deployers
- Mount Sinai Hospital
- Unknown
Harmed parties
- COVID-19 patients
- COVID-19 healthcare providers
Classification
Category confidence: 85%
Severity confidence: 80%
Rationale: Models flagged for biases and methodological flaws affecting clinical use readiness.
Classifier: Qwen/Qwen3.5-35B-A3B-FP8