27 citations · 29 across the 4 of their papers we have counts for
4 papers
MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems
Shruthi Chari, Oshani Seneviratne, Prithwish Chakraborty +2
Explanations are crucial for building trustworthy AI systems, but a gap often exists between the explanations provided by models and those needed by users. To address this gap, we…
Improving Primary Healthcare Workflow Using Extreme Summarization of Scientific Literature Based on Generative AI
Gregor Stiglic, Leon Kopitar, Lucija Gosak +5
Primary care professionals struggle to keep up to date with the latest scientific literature critical in guiding evidence-based practice related to their daily work. To help solve…
Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes
Shruthi Chari, Prasant Acharya, Daniel M. Gruen +8
Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by contextual explanations that let the practitioner connect system inference…
Distillation to Enhance the Portability of Risk Models Across Institutions with Large Patient Claims Database
Steve Nyemba, Chao Yan, Ziqi Zhang +4
Artificial intelligence, and particularly machine learning (ML), is increasingly developed and deployed to support healthcare in a variety of settings. However, clinical decision s…