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Ethical data acquisition for LLMs and AI algorithms in healthcare.

Marta Williams1, Wasie Karim2, Justin Gelman2

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Artificial intelligence (AI) in healthcare needs patient data. Ethical data acquisition prioritizing autonomy, ownership, and privacy is crucial for AI development and patient trust.

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Area of Science:

  • Healthcare technology
  • Medical ethics
  • Data science

Background:

  • Artificial intelligence (AI) integration into healthcare systems is rapidly increasing.
  • AI algorithms require substantial patient data for development and refinement.
  • Current data acquisition models in the US (opt-out) and other regions (opt-in) present ethical considerations.

Purpose of the Study:

  • To analyze ethical principles in patient data acquisition for healthcare AI.
  • To advocate for a data acquisition paradigm that prioritizes patient rights.
  • To highlight the importance of autonomy, data ownership, and privacy in AI development.

Main Methods:

  • Ethical framework analysis of data acquisition models.
  • Review of current practices in the United States and other global regions.
  • Argumentative synthesis of ethical principles and AI data needs.

Main Results:

  • The prevailing opt-out system in the US raises concerns regarding informed consent and patient autonomy.
  • Opt-in models potentially offer stronger protection for patient rights but may impact data availability.
  • A balanced approach is needed to ethically source data for AI advancement.

Conclusions:

  • Ethical considerations, including patient autonomy, data ownership, and privacy, must guide healthcare AI data acquisition.
  • Prioritizing these ethical principles is essential for building patient trust and ensuring responsible AI implementation.
  • Future healthcare AI development should adopt data acquisition strategies aligned with robust ethical standards.