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Summary
This summary is machine-generated.

Healthcare organizations need ethical oversight for generative artificial intelligence (AI) tools, like large language models (LLMs), to ensure patient safety during scaled deployments. An ethical assessment process identifies and addresses potential AI-related harms in clinical settings.

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Artificial intelligencedocumentationethicalgenerative AIlarge language modelsummarization

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Area of Science:

  • Healthcare Technology
  • Artificial Intelligence Ethics
  • Clinical Informatics

Background:

  • Healthcare organizations are increasingly adopting generative AI, particularly large language models (LLMs), for clinical applications.
  • The transition from AI experimentation to scaled deployment necessitates robust ethical oversight systems.
  • Clinical summarization tools are a key area of early interest for LLM adoption in healthcare.

Purpose of the Study:

  • To describe an ethical assessment process for identifying and addressing challenges associated with generative AI in healthcare.
  • To assist healthcare organizations in evaluating the adoption of LLMs by outlining a practical assessment framework.
  • To proactively manage risks and ensure patient safety as AI tools are integrated into clinical workflows.

Main Methods:

  • Employed stakeholder interviewing to explore risks and concerns related to AI tool integration.
  • Identified areas of misaligned values and priorities among different stakeholder groups.
  • Assessed specific LLM applications, including tools for drafting nursing notes and generating clinical notes from patient-clinician conversations.

Main Results:

  • The ethical assessment process identified potential problems affecting patient care prior to or during AI deployment.
  • Stakeholder interviews revealed diverse concerns regarding the integration of AI into clinical workflows.
  • Specific ethical issues were highlighted in the assessment of nursing note drafting and clinical note generation tools.

Conclusions:

  • A structured ethical assessment process is crucial for the responsible deployment of generative AI in healthcare.
  • Proactive identification and mitigation of ethical challenges are essential to prevent patient harm.
  • Ongoing monitoring and stakeholder engagement are necessary to manage the evolving ethical landscape of AI in clinical practice.