mlsanityData to 14 Oct 2026

Applications of Generative AI in Healthcare: algorithmic, ethical, legal and societal considerations

arXiv 2406.10632 ↗First posted 15 Jun 2024cs.CV cs.AIcited by 4

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Abstract (arXiv)

Generative AI is rapidly transforming medical imaging and text analysis, offering immense potential for enhanced diagnosis and personalized care. However, this transformative technology raises crucial ethical, societal, and legal questions. This paper delves into these complexities, examining issues of accuracy, informed consent, data privacy, and algorithmic limitations in the context of generative AI's application to medical imaging and text. We explore the legal landscape surrounding liability and accountability, emphasizing the need for robust regulatory frameworks. Furthermore, we dissect the algorithmic challenges, including data biases, model limitations, and workflow integration. By critically analyzing these challenges and proposing responsible solutions, we aim to foster a roadmap for ethical and responsible implementation of generative AI in healthcare, ensuring its transformative potential serves humanity with utmost care and precision.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 27 of its 103 references that are in the map. First posted on arXiv 15 Jun 2024.

Builds on

27 of 103 references.

References · 27

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15 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025SAIF: A Comprehensive Framework for Evaluating the Risks of Generative AI in the Public Sectorcited by 2
  2. 2025Evaluating Large Language Models on the Spanish Medical Intern Resident (MIR) Examination 2024/2025:A Comparative Analysis of Clinical Reasoning and Knowledge Applicationcited by 0
  3. 2025AI-Generated Content in Cross-Domain Applications: Research Trends, Challenges and Propositionscited by 0
  4. 2026Evaluating a Multi-Agent Voice-Enabled Smart Speaker for Care Homes: A Safety-Focused Frameworkcited by 0