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.

基于 Proto-Caps 模型的 AI 方法和 xAI 解决方案提供了可理解的多模态解释，对放射科医师的决策过程有帮助，但需要进一步开发和重复用户研究以适应相应的用例。

评估医疗分类模型的属性和原型的可解释性

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

Artificial Intelligence in Medicine has made significant progress with
emerging applications in medical imaging, patient care, and other areas. While
these applications have proven successful in retrospective studies, very few of
them were applied in practice.The field of Medical AI faces various challenges,
in terms of building user trust, complying with regulations, using data
ethically.Explainable AI (XAI) aims to enable humans understand AI and trust
its results. This paper presents a literature review on the recent developments
of XAI solutions for medical decision support, based on a representative sample
of 198 articles published in recent years. The systematic synthesis of the
relevant articles resulted in several findings. (1) model-agnostic XAI
techniques were mostly employed in these solutions, (2) deep learning models
are utilized more than other types of machine learning models, (3)
explainability was applied to promote trust, but very few works reported the
physicians participation in the loop, (4) visual and interactive user interface
is more useful in understanding the explanation and the recommendation of the
system. More research is needed in collaboration between medical and AI
experts, that could guide the development of suitable frameworks for the
design, implementation, and evaluation of XAI solutions in medicine.

近年来，医疗人工智能领域已取得显著进展，但面临着建立用户信任、符合法规和合乎道德使用数据等挑战。这篇论文针对代表性样本中的 198 篇文章对医疗决策支持的可解释人工智能解决方案进行了文献综述，发现大多数解决方案采用了模型无关的可解释人工智能技术，深度学习模型被广泛应用，用户界面的可视化和交互更有助于理解解释和系统推荐。在医疗和人工智能专家之间加强合作，以指导可解释人工智能解决方案在医学领域的设计、实施和评估，仍需要开展更多的研究。