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September 10, 2025

Bias detection and mitigation in AI models trained on clinical datasets

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Authors

VJVeerendra Nath Jasthi

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Overview

Observational analysis identifies biases in model performance across demographics, suggesting improved fairness in AI applications.

Key Points

  • Mitigation strategies improved model fairness scores while maintaining accuracy, addressing demographic inequities.
  • Bias in clinical AI models can lead to unequal outcomes across gender, age, and ethnicity subgroups.
  • Detection methods revealed significant sources of bias within clinical datasets impacting model performance.
  • Implementing reweighting and data augmentation strategies enhances the ethical use of AI in healthcare.

Cite This Study

Veerendra Nath Jasthi (2025) studied this question.

synapsesocial.com/papers/68c1924e9b7b07f3a0616a11https://doi.org/10.71097/ijsat.v16.i1.7999
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Bias recognition and mitigation strategies in artificial intelligence healthcare applications2025 · 287 citations
  2. 2Addressing Bias in AI Algorithms for Health Applications2025 · 5 citations
  3. 3When Bias Becomes Knowledge: How Sociodemographic Inequities Shape Medical AI2026
  4. 4Review of Data Bias in Healthcare Applications2024 · 4 citations
  5. 5Bias Mitigation Across the Healthcare Artificial Intelligence Lifecycle: A Structured Narrative Review2026