Bellini Valentina, Calabrò Francesco, Bignami Elena, Haja Tudor Mihai, Fasterholdt Iben, Rasmussen Benjamin Sb, Cecchi Rossana
Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Viale Gramsci 14, Parma, 43126, Italy.
Laboratory of Forensic Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy.
J Med Syst. 2025 Aug 23;49(1):108. doi: 10.1007/s10916-025-02235-7.
This work aims to explore the transferability of the Model for Assessing the value of Artificial Intelligence in medical imaging (MAS-AI) in the Italian context through a case-study.We applied the MAS-AI, a model for assessing AI in healthcare, to fulfil a technology assessment of an AI model developed within our institution. The model, called New organization model for the surgical unit (BLOC-OP), uses AI to improve the schedule efficiency of the surgical unit. The analysis of BLOC-OP's features, as they were described in the project presentation, was conducted through the requirements for the assessment contained in the MAS-AI model.The methodological framework of MAS-AI was fully followed, allowing us to conduct a comprehensive assessment of the BLOC-OP model in all its aspects. We provided a detailed description of each domain within the framework, along with a summary table.The case study demonstrates the feasibility of applying MAS-AI to organizational AI models in a national context different from where the framework was originally developed. Rather than proposing a new model, we tested the adaptability of MAS-AI in evaluating a non-imaging AI system. This confirms its flexibility beyond its original scope and supports its potential as a generalizable tool for AI evaluation in healthcare.
本研究旨在通过一个案例研究，探讨医学影像人工智能价值评估模型（MAS-AI）在意大利背景下的可转移性。我们应用MAS-AI（一种用于评估医疗保健领域人工智能的模型）对我们机构开发的一个人工智能模型进行技术评估。该模型名为手术科室新组织模型（BLOC-OP），利用人工智能提高手术科室的排班效率。根据MAS-AI模型中包含的评估要求，对项目展示中描述的BLOC-OP的特征进行了分析。我们完全遵循了MAS-AI的方法框架，从而能够对BLOC-OP模型的各个方面进行全面评估。我们提供了框架内每个领域的详细描述以及一个汇总表。该案例研究证明了在与该框架最初开发背景不同的国家背景下，将MAS-AI应用于组织性人工智能模型的可行性。我们并非提出一个新模型，而是测试了MAS-AI在评估非影像人工智能系统方面的适应性。这证实了它在原始范围之外的灵活性，并支持其作为医疗保健领域人工智能评估通用工具的潜力。
