# Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model

arXiv: https://arxiv.org/abs/2404.09917 · HTML: https://mlsanity.com/paper/2404.09917/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 8 of 8)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI](https://mlsanity.com/paper/2201.08164/) (2022)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)
- [Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations](https://mlsanity.com/paper/2210.11584/) (2022)
- [Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations](https://mlsanity.com/paper/2301.07255/) (2023)
- [How model accuracy and explanation fidelity influence user trust](https://mlsanity.com/paper/1907.12652/) (2019)
- [Interpretable Medical Image Classification using Prototype Learning and Privileged Information](https://mlsanity.com/paper/2310.15741/) (2023)

## Built on by (most cited first, 4 of 4)

- [FunnyNodules: A Customizable Medical Dataset Tailored for Evaluating Explainable AI](https://mlsanity.com/paper/2511.15481/) (2025)
- [Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification](https://mlsanity.com/paper/2508.00639/) (2025)
- [Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems](https://mlsanity.com/paper/2508.10806/) (2025)
- [X-SYS: A Reference Architecture for Interactive Explanation Systems](https://mlsanity.com/paper/2602.12748/) (2026)

## Abstract (arXiv)

Due to the sensitive nature of medicine, it is particularly important and highly demanded that AI methods are explainable. This need has been recognised and there is great research interest in xAI solutions with medical applications. However, there is a lack of user-centred evaluation regarding the actual impact of the explanations. We evaluate attribute- and prototype-based explanations with the Proto-Caps model. This xAI model reasons the target classification with human-defined visual features of the target object in the form of scores and attribute-specific prototypes. The model thus provides a multimodal explanation that is intuitively understandable to humans thanks to predefined attributes. A user study involving six radiologists shows that the explanations are subjectivly perceived as helpful, as they reflect their decision-making process. The results of the model are considered a second opinion that radiologists can discuss using the model's explanations. However, it was shown that the inclusion and increased magnitude of model explanations objectively can increase confidence in the model's predictions when the model is incorrect. We can conclude that attribute scores and visual prototypes enhance confidence in the model. However, additional development and repeated user studies are needed to tailor the explanation to the respective use case.