2025 systematic review in npj Digital Medicine
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Systematic review examining algorithmic bias in 690 clinical decision instruments, identifying critical sources of bias including demographic skew, geographic concentration, and outcome definition issues—directly relevant to AI safety concerns around fairness and bias in deployed healthcare systems.
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This 2025 systematic review in npj Digital Medicine examines algorithmic bias in clinical decision instruments (CDIs) across 690 tools used in healthcare. The authors identify four potential sources of bias: skewed participant demographics (73% White, 55% male), geographically concentrated investigator teams (52% North America, 31% Europe), limited use of race/ethnicity as predictor variables (1.9%), and outcome definitions that may introduce socioeconomic bias (26% involve follow-up). The study highlights an equity dilemma where CDIs standardize best practices but may perpetuate existing healthcare disparities, recommending that developers and clinicians carefully consider these bias sources during instrument development and implementation.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI-Driven Institutional Decision Capture | Risk | 73.0 |
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Potential for Algorithmic Bias in Clinical Decision Instrument Development | npj Digital Medicine
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Potential for Algorithmic Bias in Clinical Decision Instrument Development
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Subjects
Diagnosis
Outcomes research
Predictive medicine
Clinical trial design
Abstract
Clinical decision instruments (CDIs) face an equity dilemma. They reduce disparities in patient care through data-driven standardization of best practices. However, this standardization may perpetuate bias and inequality within healthcare systems. We perform a quantitative, systematic review to characterize four potential sources of bias in the development of 690 CDIs. We find evidence for potential algorithmic bias in CDI development through various analyses: self-reported participant demographics are skewed—e.g. 73% of participants are White, 55% are male; investigator teams are geographically skewed—e.g. 52% in North America, 31% in Europe; CDIs use predictor variables that may be prone to bias—e.g. 1.9% (13/690) of CDIs use Race and Ethnicity ; outcome definitions may introduce bias—e.g. 26% (177/690) of CDIs involve follow-up, which may skew representation based on socioeconomic status. As CDIs become increasingly prominent in medicine, we recommend that these factors are considered during development and clearly conveyed to clinicians.
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Introduction
Clinical decision i
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