The growing interest in eXplainable Artificial Intelligence (XAI) has stimulated research on models with built-in interpretability, among which part-prototype models are particularly prominent. Part-Prototype Models (PPMs) classify inputs by comparing them to learned prototypes and provide human-understandable explanations of the form "this looks like that". Despite this intrinsic interpretability, PPMs have not yet emerged as a competitive alternative to post-hoc explanation methods. This survey reviews work published between 2019 and 2025 and derives a taxonomy of the challenges faced by current PPMs. The analysis reveals a diverse set of open problems. The main issue concerns the quality and number of learned prototypes. Further challenges include limited generalization across tasks and contexts, as well as methodological shortcomings such as non-standardized evaluation. Five broad research directions are identified: improving predictive performance, developing theoretically grounded architectures, establishing frameworks for human-AI collaboration, aligning models with human concepts, and defining robust metrics and benchmarks for evaluation. The survey aims to stimulate further research and promote intrinsically interpretable models for practical applications. A curated list of the surveyed papers is available at https://github.com/aix-group/ppm-survey.

翻译：可解释人工智能（XAI）的日益兴起推动了具有内在可解释性的模型研究，其中部分原型模型尤为突出。部分原型模型（PPMs）通过将输入与学习到的原型进行比较来进行分类，并提供“这个看起来像那个”形式的人类可理解解释。尽管具有这种内在可解释性，PPMs尚未成为事后解释方法的有力替代方案。本综述回顾了2019年至2025年间发表的研究，并归纳出现有PPMs所面临挑战的分类体系。分析揭示了一组多样化的开放性问题。主要问题涉及学习到的原型的质量和数量。进一步的挑战包括跨任务和跨场景的泛化能力有限，以及诸如缺乏标准化评估等方法论缺陷。研究指出了五大研究方向：提升预测性能、开发理论支撑的架构、建立人机协作框架、使模型与人类概念对齐，以及定义用于评估的稳健指标和基准。本综述旨在推动进一步研究，并促进面向实际应用的内在可解释模型发展。所综述论文的精选列表可见于https://github.com/aix-group/ppm-survey。