This summer, we invite participants from all departments to present their research work in the form of a virtual poster presentation at the annual Alumni reunion poster session. This event is a continued tradition designed to increase the interaction between graduate students and alumni. This is an opportunity to share your research, ideas and interact with Michigan Tech alumni, and to expand your network connections. This year, you will be able to present your poster while staying safe from the comfort of your own home.

"There is an increasing interest in Artificial Intelligence (AI) for supporting and assisting medical diagnosis. One potential application of AI is as the first point of contact for initial. Even though many AI-driven clinical prediction tools have achieved high accuracy performance, the lack of explainability of these tools continues to spark criticism (Amann et al., 2020). If these systems do not provide explanation about why the diagnoses are made, the high-performance ones may be ignored or rejected. In this paper, we examine this problem using a simulation experiment. We tested an evolving clinical diagnosis scenario and used LIME (Ribeiro et al., 2016) algorithm to generate feature-based graphical explanations for probable clinical conditions. We found that explanation helps improve satisfaction measures during the critical re-diagnosis period but had little effect later when an alternate diagnosis resolved the case successfully. Furthermore, we found that providing positive and negative examples (contrasts) along with the current case features were no more helpful than providing the current case features only. We also evaluated the understanding of the explanations using knowledge tests and found that explanation did not help improved understanding of the AI-generated diagnosis decisions."

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