Visual Analytics for Clinical Decision Support based on Evidence-based Medicine

PhD Thesis Proposal Defence


Title: "Visual Analytics for Clinical Decision Support based on Evidence-based 
Medicine"

by

Mr. Rui SHENG


Abstract:

Artificial intelligence (AI) for clinical decision support has become 
increasingly prevalent in healthcare. However, those AI models always remain 
opaque, limiting clinicians' ability to understand and appropriately integrate 
AI recommendations into practice. Prior work has introduced explainable AI 
techniques, such as feature attribution and counterfactual explanations, but 
these methods are largely model-centric and poorly aligned with clinicians' 
realistic reasoning process. Specifically, clinical decision-making is 
typically grounded in evidence-based medicine (EBM), where patient-specific 
findings are interpreted in the context of prior clinical trials, guidelines, 
and medical literature. This thesis investigates visual analytics for clinical 
decision support through the lens of evidence-based medicine, aiming to enable 
human–AI collaboration that better aligns with clinicians' common 
decision-making practice.

The thesis makes three complementary contributions to evidence-based clinical 
decision support through visual analytics. First, it investigates how visual 
analytics can support the construction of high-quality clinical evidence in 
clinical trials. It introduces an interactive system that helps clinicians 
design more appropriate eligibility criteria for clinical trials by exposing 
trade-offs among population coverage, cohort representativeness, and outcome 
validity. Second, before designing AI-assisted decision-making systems that are 
meaningfully integrated with clinical evidence, the thesis examines existing 
design practices for human–AI clinical systems. Through a systematic review of 
43 papers, it synthesizes 15 core information entities and 12 reusable design 
patterns for AI-driven clinical systems. These findings provide a design 
foundation for building more effective clinical decision support systems and 
reveal that existing systems rarely incorporate literature-based evidence as a 
first-class, richly structured design element in AI-driven workflows. Third, 
the thesis explores how clinicians can more effectively use clinical evidence 
in AI-assisted decision-making. It presents a visual analytics system that 
integrates patient-specific AI explanations with evidence from the clinical 
literature, thereby supporting informed and balanced clinical decisions.

Taken together, these contributions advance a unified research agenda that 
spans evidence construction, design knowledge for human-AI systems in clinical 
decision-making, and evidence-centered clinical decision support with AI. The 
findings suggest that effective human-AI collaboration requires not only more 
transparent AI models, but also careful integration with clinicians' common 
practice.


Date:                   Tuesday, 9 June 2026

Time:                   2:00pm - 4:00pm

Venue:                  Room 3494
                        Lift 25/26

Committee Members:      Prof. Huamin Qu (Supervisor)
                        Dr. Dan Xu (Chairperson)
                        Prof. Ke Yi