Graduation Semester and Year
Spring 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Science
Department
Computer Science and Engineering
First Advisor
Dajiang Zhu
Second Advisor
Junzhou Huang
Third Advisor
Li Wang
Fourth Advisor
Ming Li
Abstract
Artificial Intelligence (AI) is transforming healthcare by enabling large-scale analysis of medical data and integrating multimodal information for more comprehensive diagnostics. I present my work addressing fundamental and challenging problems in developing state-of-the-art AI models for medical data analysis, including multimodal brain data and other medical datasets. Additionally, I design brain-inspired AI models by integrating insights from organizational principles of brain networks. Specifically, my research tackles three critical aspects: (1) AI in Computational Neuroscience, where I design deep learning models for brain network analysis to uncover the organizational principles of brain networks; (2) Brain-Inspired AI, where I integrate superior brain organizational principles into AI model design and apply these brain-inspired AI models to downstream tasks; (3) Large Language Models (LLMs) for Healthcare, where I develop novel machine learning methods for LLMs to address healthcare challenges, including using LLMs for Alzheimer’s disease analysis. To assess the effectiveness of the proposed methods, I apply the developed models to a wide range of tasks and evaluate them across diverse datasets.
Keywords
Brain-inspired AI, Medical AI
Disciplines
Biomedical | Other Computer Engineering
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Yu, Xiaowei, "Medical AI: Solving Healthcare Challenges and Inspiring AI Innovation" (2025). Computer Science and Engineering Dissertations. 409.
https://mavmatrix.uta.edu/cse_dissertations/409