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Updated: May 10, 2025
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
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Can an accurate model be bad?
Melissa D McCradden1,2,3, Mjaye L Mazwi4,5, Lauren Oakden-Rayner1
1Australian Institute for Machine Learning, Adelaide, SA, Australia.
Patterns (New York, N.Y.)
|April 23, 2025
Summary
Outcome-prediction models can harm patients, even with high accuracy. This research highlights the risk of self-fulfilling prophecies and advocates for prioritizing actions over accuracy in AI integration.
Area of Science:
- Medical AI
- Clinical Decision Support
- Ethical AI
Background:
- Outcome-prediction models are increasingly used in healthcare.
- These models can exhibit high accuracy but still pose risks.
- A recent study in "Patterns" by Van Amsterdam et al. demonstrates potential patient harm.
Discussion:
- Reification of self-fulfilling prophecies: Models can create the outcomes they predict.
- Ethical implications of AI-driven predictions in patient care.
- Empirical evidence of unintended consequences from predictive models.
Key Insights:
- Model accuracy does not guarantee patient benefit or safety.
- The mechanism of self-fulfilling prophecies is a critical concern.
- AI integration requires careful consideration beyond predictive performance.
Outlook:
- Shift focus from prediction accuracy to actionable insights.
- Prioritize AI strategies that promote patient well-being and ethical practice.
- Future AI development should emphasize responsible integration and impact assessment.
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