Pathway Forward for Responsible Generative AI Implementation in Healthcare

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dc.contributorDing, Ying
dc.creatorMaddipoti, Anish
dc.date.accessioned2024-01-10T14:13:01Z
dc.date.available2024-01-10T14:13:01Z
dc.date.issued2023-11
dc.description.abstractThis thesis explores a pathway forward for responsible generative AI implementation in healthcare. It analyzes existing use cases and gathers stakeholder perspectives to reveal tensions between innovation opportunities and ethical risks of large language models (LLMs). The thesis proposes a practical framework built on objectives of data stewardship, oversight, access equity and transparency to integrate these technologies ethically. It examines assessing alignment, requirements, integration approaches and psychological readiness across health systems. With context-specific governance that can earn stakeholder trust, generative AI will enhance care coordination, outcomes, and clinical knowledge while still upholding patient interests. This thesis provides ideas to promote judicious innovation that increases healthcare access, efficiency and insights while mitigating potential pitfalls. In summary, it charts a framework for leveraging the promise of LLMs to improve medicine, through collaborative policies upholding safety, ethics and human-centric values amidst rapid technological advancement.
dc.description.departmentPlan II Honors Program
dc.identifier.urihttps://hdl.handle.net/2152/123393
dc.identifier.urihttps://doi.org/10.26153/tsw/50191
dc.language.isoeng
dc.relation.ispartofPlan II Honors Theses - Openly Available
dc.rights.restrictionopen
dc.subjectGenerative AI
dc.subjectLarge Language Models
dc.subjectHealthcare
dc.titlePathway Forward for Responsible Generative AI Implementation in Healthcare
dc.typethesis

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