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Issues and Limitations on the Road to Fair and Inclusive AI Solutions for Biomedical Challenges.

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Performance reporting in biomedical AI is hindered by bias and noise from human interpretation. Addressing this "noise-bias cascade" requires better study design and diverse representation for inclusive AI solutions.

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Area of Science:

  • Biomedical AI
  • Medical Decision Support Systems
  • Artificial Intelligence in Healthcare

Background:

  • Performance reporting in AI development can introduce bias and noise.
  • Human data interpretation and selection are key sources of measurement bias and noise.
  • The 'noise-bias cascade' describes the interconnected nature of these issues in AI.

Purpose of the Study:

  • To explore the correlation between performance reporting and inclusive AI solutions for biomedical problems.
  • To examine bias and noise in medical decision support systems.
  • To provide actionable solutions for mitigating bias and noise in AI development.

Main Methods:

  • Analysis of the AI development lifecycle, from data collection to deployment.
  • Conceptualization of the 'noise-bias cascade' to explain interconnected bias and noise.
  • Evaluation of current AI model capabilities in handling noise versus bias.

Main Results:

  • Bias, stemming from human interpretation, poses a greater challenge than noise for AI performance.
  • Current AI models are adept at handling noise but struggle with inherent biases.
  • The entire AI development lifecycle is susceptible to noise and bias introduction.

Conclusions:

  • Mitigating bias requires rigorous study design, statistical analysis, transparent reporting, and diverse representation.
  • Integrating uncertainty measures during AI model deployment is crucial for fairness and inclusivity.
  • Comprehensive strategies are needed to minimize both bias and noise for improved medical AI performance.