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<p>This work aims to explore the transferability of the <i>Model for Assessing the value of Artificial Intelligence in medical imaging</i> (MAS-AI) in the Italian context through a case-study.</p><p>We applied the MAS-AI, a model for assessing AI in healthcare, to fulfil a technology assessment of an AI model developed within <i>our institution.</i> The model, called <i>New organization model for the surgical unit</i> (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.</p><p>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.</p><p>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.</p>

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Applying the Model for Assessing the Value of AI (MAS-AI) Framework To Organizational AI: A Case Study of Surgical Scheduling Assessment in Italy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月23日

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  • Valentina Bellini,
  • Francesco Calabrò,
  • Elena Bignami,
  • Tudor Mihai Haja,
  • Iben Fasterholdt,
  • Benjamin SB Rasmussen,
  • Rossana Cecchi

摘要

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.