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Area of Science:

  • Healthcare Technology
  • Artificial Intelligence
  • Public Health

Background:

  • Healthcare technology adoption is hindered by accessibility and cost.
  • Advancements in artificial intelligence (AI) are significant, but models can exhibit biases.
  • Potential misuse of AI by state actors to influence public opinion is a concern.

Purpose of the Study:

  • To examine the potential effects of "pro-state" biases on healthcare delivery.
  • To illustrate healthcare risks associated with AI knowledge editing and biased post-training methods.

Main Methods:

  • Case study analysis using the DeepSeek AI model.
  • Examination of AI post-training methods and knowledge editing techniques.

Main Results:

  • AI models, including DeepSeek, may contain "pro-state" biases.
  • Biased AI can introduce healthcare risks through unknown or flawed post-training processes.
  • Potential for misuse of AI by power structures to influence public opinion.

Conclusions:

  • Addressing AI biases is crucial for equitable healthcare technology adoption.
  • Understanding AI knowledge editing is essential to mitigate healthcare risks.
  • Generative AI's impact on healthcare depends on overcoming accessibility, cost, and bias challenges.