Fairness or Fallacy? Mapping the Landscape of AI Bias in Disease Detection

Key points

• Due to the increasing complexity of medical data and the growing volume of diagnostic images, the substantial computational power needed to process such information creates both opportunities and challenges.1
• To accomplish this objective, numerous methodologies have been proposed to integrate AI into disease detection systems.1
• In this paper, we review different state-of-the-art techniques which have been proposed for various areas of disease detection, specifically focusing on bias identification and mitigation.1
• Furthermore, these results reveal significant research gaps that can assist researchers in addressing AI-related bias in medical diagnosis.1

Sources (1)

• [1]Fairness or Fallacy? Mapping the Landscape of AI Bias in Disease Detection
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 11:41 AM

  Artificial intelligence (AI) has several transformative effects on healthcare, like to facilitate clinicians in disease detection and to manage patient care with enhanced precision.

  Due to the increasing complexity of medical data and the growing volume of diagnostic images, the substantial computational power needed to process such information creates both opportunities and challenges.

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