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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
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Fairness in AI for healthcare.

Siân Carey1, Allan Pang1,2,3, Marc de Kamps1

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Artificial intelligence (AI) can improve healthcare but may worsen existing disparities. This study examines bias in healthcare AI and explores current and future research solutions to mitigate these issues.

Keywords:
Artificial intelligenceBiasDiscriminationFairnessHealthcareMachine learning

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Area of Science:

  • Healthcare Technology
  • Medical Ethics
  • Computer Science

Background:

  • Artificial intelligence (AI) offers transformative potential for healthcare delivery and patient outcomes.
  • Concerns exist that AI may perpetuate or amplify existing health disparities.
  • Understanding and addressing bias in healthcare AI is critical for equitable implementation.

Purpose of the Study:

  • To discuss the pervasive issue of bias within healthcare artificial intelligence.
  • To explore the potential for AI to exacerbate existing health inequities.
  • To highlight ongoing and future research addressing bias in healthcare AI.

Main Methods:

  • Literature review of AI in healthcare.
  • Analysis of bias in AI algorithms.
  • Synthesis of research on mitigation strategies.

Main Results:

  • AI's dual potential: significant healthcare improvements versus exacerbation of disparities.
  • Identification of key sources and types of bias in healthcare AI.
  • Overview of emerging solutions and research directions for bias mitigation.

Conclusions:

  • Addressing bias in healthcare AI is essential for equitable technology deployment.
  • Continued research is vital to develop and implement fair and unbiased AI systems.
  • Proactive strategies are needed to ensure AI benefits all patient populations.