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Summary of Applications Of Generative Ai in Healthcare: Algorithmic, Ethical, Legal and Societal Considerations, by Onyekachukwu R. Okonji et al.


by Onyekachukwu R. Okonji, Kamol Yunusov, Bonnie Gordon

First submitted to arxiv on: 15 Jun 2024

Categories

  • Main: Computer Vision and Pattern Recognition (cs.CV)
  • Secondary: Artificial Intelligence (cs.AI)

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GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The abstract discusses the rapid growth of generative AI in medical imaging and text analysis, which holds great promise for improving diagnosis and personalized care. However, this technology raises critical ethical, societal, and legal questions about accuracy, informed consent, data privacy, algorithmic limitations, liability, and accountability. The paper explores these challenges and proposes solutions to ensure the responsible implementation of generative AI in healthcare.
Low GrooveSquid.com (original content) Low Difficulty Summary
Generative AI is a powerful technology that can help doctors diagnose patients more accurately and provide personalized care. However, it also raises important questions about ethics, privacy, and how it’s used. This paper looks at some of these challenges and suggests ways to make sure this technology is used responsibly in healthcare.

Keywords

» Artificial intelligence