Summary of The Explanation Necessity For Healthcare Ai, by Michail Mamalakis et al.
The Explanation Necessity for Healthcare AI
by Michail Mamalakis, Héloïse de Vareilles, Graham Murray, Pietro Lio, John Suckling
First submitted to arxiv on: 31 May 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 Machine learning models in healthcare require explanations that enhance trustworthiness and acceptance. A lack of guidelines on explanation necessity hinders interpretability advancements. This paper proposes a novel categorization system comprising four classes: self-explainable, semi-explainable, non-explainable, and new-patterns discovery. The framework guides the required level of explanation (local, global, or both) and incorporates three key factors: robustness, variability, and representation dimensionality. This tool determines the appropriate depth of explainability needed in AI medical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI models in healthcare need explanations to be trustworthy. A new system helps decide when and how much to explain. The system has four categories: easy to understand, needs some explanation, not needed, or new patterns found. It also considers three important things: making sure the test is good, experts agree, and what’s being represented. This makes it easier for researchers to figure out how much to explain in medical AI applications. |
Keywords
» Artificial intelligence » Machine learning
