AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The GenXAI vision is presented, a system architecture that combines Genomic Language Models with Explainable AI (XAI) and physician-facing visual interfaces to support trustworthy, reproducible microbial species identification from metagenomic data.
It is concluded that explainability is a necessary, though not sufficient, condition for trustworthy Al, and concrete directions for future research are outlined, including standardised benchmarks, human-centred evaluation, and explainability for large generative models.
This review aims to formalize XAI methods in CompPath through the introduction of a pathology-centric vocabulary comprising seven core terms and establishment of a task-driven framework that maps five clinical questions to recommended methods, method evaluation, and deployment context.
Experimental findings show the effectiveness of XAI techniques to enhance interpretability without causing a major loss in predictive accuracy and the practical implications, limitations, and research directions on the future of explainable and trustworthy AI systems.
Suggestions are provided for the field to transition from individual and post-hoc XAIs toward intrinsically explainable designs where the reasoning logic is built directly into the model architecture to ensure that AI outputs align with human-centric clinical workflows and applications.
XAI provides a powerful framework for responsible AI development, challenges such as the performance-interpretability trade-off, lack of standardized evaluation metrics, and potential for human misinterpretation remain areas of active research.
An accessible overview of XAI is provided, including saliency maps, perturbation-based and feature-attribution approaches, concept- based methods, and example-based reasoning, as well as uncertainty quantification as a complementary approach for assessing prediction reliability, along with common misconceptions and eme...
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