15 used two explainable AI techniques to predict COVID-19 severity. Eighty-seven patients were considered in this study. Shapley additive values (SHAP) and local interpretable model-agnostic explanations (LIME) were used to make the models understandable. The most critical cytokine markers are VEGF...
Explainable AI based wearable electronic optical data analysis with quantum photonics and quadrature amplitude neural computingOptical communicationWearable sensor data gatheringQuantum photonicsMachine learning architectureFeedforward neural computingNetwork optimizationThe electrocardiogram, electroencephalogram, blood ...
Questioning the AI: informing design practices for explainable AI user experiences. In Proc. 2020 CHI Conference on Human Factors in Computing Systems, CHI ‘20 1–15 (ACM, 2020). Sheridan, R. P. Interpretation of QSAR models by coloring atoms according to changes in predicted activity: how ...
In the field of chemical engineering, understanding the dynamics and probability of drop coalescence is not just an academic pursuit, but a critical requirement for advancing process design by applying energy only where it is needed to build necessary interfacial structures, increasing efficiency towards...
Sign up for theNature Briefing: AI and Roboticsnewsletter — what matters in AI and robotics research, free to your inbox weekly. Email address Sign up I agree my information will be processed in accordance with theNatureand Springer Nature LimitedPrivacy Policy....
But these approaches have their limitations in terms of scalability, adaptability, and accuracy. The number of real life, high value use cases for AI anomaly detection have grown a lot over the years and are expected to continue to grow. Advances in artificial intelligence (...
In this research, we will explore AI-based optical sensors in healthcare applications, and more particularly, to improve AI solutions for disease detection. 1.1 Motivation The coronavirus disease 2019 (COVID-19) pandemic’s health support has greatly benefited from recent advancements in a number ...
such assumption cannot be met in several cases, making difficult a fair assessment of the XAI outcome by the end users (Bruijn et al.2022). Another concern related to the current XAI methods is the lack of causality in the outcome. More precisely, current AI models primarily rely on identif...
Machine-learned computational chemistry has led to a paradoxical situation in which molecular properties can be accurately predicted, but they are difficult to interpret. Explainable AI (XAI) tools can be used to analyze complex models, but they are highly dependent on the AI technique and the ori...
and time-consuming for extensive experiments32,33. Additionally, it has been shown that they can be suboptimal in prediction performance25,34. In order to overcome these limitations, we developed the single-cell imaging flow cytometry AI (scifAI) framework for the unbiased analysis of high-dimensi...