関連する実験動画

Updated: Sep 9, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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サブグループ学習性のある医療データセットにおける暗黙的および明示的な人種バイアスの検出,特徴付け,緩和:アルゴリズム開発および検証研究

Faris Gulamali1, Ashwin Shreekant Sawant1, Lora Liharska1

  • 1Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, United States, 1 2122416500.

Journal of medical Internet research
|September 4, 2025
PubMed
まとめ

新しいメトリックであるAEquityは データ収集と再表示を ガイドすることで 医療データにおけるアルゴリズムのバイアスを 効果的に軽減します このアプローチは,様々なデータセットとアルゴリズムで既存の方法を上回り,AI診断の公平性を向上させます.

キーワード:
バイアスデータ中心の人工知能公平さ機械学習

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