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The practical implementation of artificial intelligence technologies in medicine.

Jianxing He1,2, Sally L Baxter3,4,5, Jie Xu6

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Artificial intelligence (AI) in medicine shows promise but faces hurdles in clinical use. Key challenges include data privacy, algorithm transparency, standardization, interoperability, and patient safety, alongside regulatory considerations.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Workflow Optimization

Background:

  • Rapid advancement of artificial intelligence (AI) technologies in medicine.
  • Limited real-world clinical implementation despite technological progress.

Purpose of the Study:

  • To review practical challenges in integrating AI into clinical workflows.
  • To examine data sharing, privacy, transparency, standardization, interoperability, and patient safety concerns.
  • To summarize the US regulatory landscape and compare it internationally.

Main Methods:

  • Review of key practical issues in AI implementation.
  • Analysis of data sharing, privacy, and transparency.
  • Examination of standardization and interoperability challenges.
  • Assessment of patient safety considerations.
  • Summary of US regulatory environment with international comparisons (Europe, China).

Main Results:

  • Significant practical barriers hinder AI adoption in clinical settings.
  • Data privacy, algorithmic transparency, and standardization are critical concerns.
  • Interoperability across platforms and ensuring patient safety require robust solutions.
  • The regulatory landscape in the US is evolving, with international variations.

Conclusions:

  • Addressing practical implementation challenges is crucial for realizing AI's potential in healthcare.
  • Standardized approaches to data and interoperability are needed.
  • Clear regulatory frameworks and a focus on patient safety are essential for successful AI integration.