Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Applying the Model for Assessing the Value of AI (MAS-AI) Framework To Organizational AI: A Case Study of Surgical Scheduling Assessment in Italy
2
Zitationen
7
Autoren
2025
Jahr
Abstract
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.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.393 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.259 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.688 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.502 Zit.