Title:
Advancing Personalized Medicine Through Generative Artificial Intelligence
Advancing Personalized Medicine Through Generative Artificial Intelligence
Author(s)
Shi, Wenqi
Advisor(s)
Wang, May Dongmei
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Abstract
The primary goal of next-generation healthcare systems is to deliver preventive, predictive, precise, participatory, and personalized health, thereby improving the quality of patient care. Despite the abundance of peer-reviewed papers demonstrating novel artificial intelligence (AI)-enabled solutions for precision medicine, a significant gap remains in translating these scientific discoveries into clinical practice to effectively benefit patients and clinicians. To begin, developing informed clinical decision support necessitates a comprehensive, large-scale analysis of various biomedical and clinical data. Moreover, lack of explainability is another major barrier to the widespread adoption of clinical decision support systems (CDSSs)in real-world settings. For example, clinicians struggle to trust decisions made by black-box models, where experts require more substantial clinical evidence beyond simple predictions for effective clinical validation and decision support. In addition, the integration of new AI tools may create an extra burden for clinicians and disrupt existing clinical workflows, resulting in compromised clinical efficiency and effectiveness. The objective of this thesis is to advance cutting-edge AI-enabled CDSSs, including LLMs, to facilitate translational personalized medicine. By advancing emerging AI in personalized medicine, the proposed framework aims to bridge the gap between biomedical research and clinical practice, thereby promoting the adoption of AI in real-world clinical practice.
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Date Issued
2024-04-15
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Resource Subtype
Dissertation