Summary of Ai Readiness in Healthcare Through Storytelling Xai, by Akshat Dubey et al.
AI Readiness in Healthcare through Storytelling XAI
by Akshat Dubey, Zewen Yang, Georges Hattab
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the limitations of Artificial Intelligence (AI) adoption in real-world healthcare settings. Despite AI’s rapid advancement, the lack of trustworthiness in AI models hinders their integration into clinical practices. Explainable Artificial Intelligence (XAI) techniques aim to mitigate these issues by providing insights into model predictions. However, XAI can be defined differently depending on one’s background, expertise, and goals. To cater to diverse needs, this research develops storytelling XAI, combining multi-task distillation with interpretability techniques. This approach enables audience-centric explainability by exploiting relationships between tasks and enhancing interpretability from the domain expert’s perspective. The study focuses on both model-agnostic and model-specific methods of interpretability, supported by textual justification in a healthcare use case. The proposed methods increase trust among domain experts and machine learning experts, enabling responsible AI adoption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making Artificial Intelligence more trustworthy for use in hospitals. Right now, doctors are not sure if they can rely on AI’s predictions. To fix this, researchers developed a new way to make AI explain its decisions called storytelling XAI. This method helps AI talk to people with different backgrounds and needs. The study combines two techniques: multi-task distillation and interpretability methods. These help make the AI more understandable for doctors and other experts. The research also shows how their approach can be used in a real-life healthcare setting. |
Keywords
» Artificial intelligence » Distillation » Machine learning » Multi task
