Building Trust in Diagnostic Artificial Intelligence: An Explainable Approach to Medical Decision Support; Interpreting Trust: The Social Construction of IBM Watson for Oncology 18 views

Artificial Intelligence is increasingly used in clinical diagnostics; yet many systems fail to capture physician trust because their decision-making processes are unclear and difficult to interpret. To address this challenge, I propose the design of an explainable diagnostic AI system that balances predictive performance with clinically meaningful explanations. The proposed solution leverages transformer-based architectures augmented with weighting techniques and natural-language generation to communicate diagnostic reasoning in human-readable terms. Rather than relying solely on statistical feature visualizations, the system emphasizes explanations aligned with established clinical reasoning practices. Publicly available electronic health record data will be used to evaluate diagnostic accuracy and interpretability. The anticipated outcome is a diagnostic model that improves transparency without sacrificing performance, thereby supporting shared decision-making between clinicians and patients. Future work includes clinician-centered usability studies, refinement of explanation interfaces, and evaluation of the system’s effectiveness across different clinical settings.

BS (Bachelor of Science)

Trust in Healthcare; Healthcare AI; IBM Watson for Oncology

School of Engineering and Applied Science Bachelor of Science in Computer Science Technical Advisor: Rosanne Vrugtman STS Advisor: Benjamin Laugelli

English

All rights reserved by the author (no additional license for public reuse)

2026-05-08