ArticleFrontiers in digital health2021
Detecting Spurious Correlations With Sanity Tests for Artificial Intelligence Guided Radiology Systems.
Article in Frontiers in digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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.
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Who cites it
8 citing papers in PubMed.
- Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics.Biology · 2026Review
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- The cost of explainability in artificial intelligence-enhanced electrocardiogram models.NPJ digital medicine · 2025Article
- Detecting and Mitigating the Clever Hans Effect in Medical Imaging: A Scoping Review.Journal of imaging informatics in medicine · 2025Article
- Unmasking the Clever Hans effect in AI models: shortcut learning, spurious correlations, and the path toward robust intelligence.Frontiers in artificial intelligence · 2025Review
- Review
- Deep Learning and Domain-Specific Knowledge to Segment the Liver from Synthetic Dual Energy CT Iodine Scans.Diagnostics (Basel, Switzerland) · 2022Article
- Adversarial counterfactual augmentation: application in Alzheimer's disease classification.Frontiers in radiology · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Artificial intelligence (AI) has been successful at solving numerous problems in machine perception. In radiology, AI systems are rapidly evolving and show progress in guiding treatment decisions, diagnosing, localizing disease on medical images, and improving radiologists' efficiency. A critical component to deploying AI in radiology is to gain confidence in a developed system's efficacy and safety. The current gold standard approach is to conduct an analytical validation of performance on a generalization dataset from one or more institutions, followed by a clinical validation study of the system's efficacy during deployment. Clinical validation studies are time-consuming, and best practices dictate limited re-use of analytical validation data, so it is ideal to know ahead of time if a system is likely to fail analytical or clinical validation. In this paper, we describe a series of sanity tests to identify when a system performs well on development data for the wrong reasons. We illustrate the sanity tests' value by designing a deep learning system to classify pancreatic cancer seen in computed tomography scans.
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Registered trials
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 OpenQuestion graph.