Evaluating Pre-trained 3rd Party AI Models in Medical Device Software Development for Responsible and Ethical Use

It is well understood that new standards are necessary to ensure the use of Artificial Intel-ligence-enabled Medical Devices (AIeMD) are adequately controlled. CEN-CENELEC (European focus) and IEC (Global focus) are developing such standards. Yet the use of pre-trained AI models, available as Software of Unknown Provenance (SOUP) from 3rd party suppliers, remain excluded from the standards under development. This paper aims to demonstrate the need for standardisation of the qualification process for these models beyond that understood by existing standards such as ISO 13485 and IEC 62304. This is necessary in order to achieve responsible and ethical AI for use in healthcare. This paper demonstrates the need for guidance by setting out emerging use cases as well as the risks and potential risk mitigations available when using pre-trained models as SOUP in the Software Development Lifecycle of AIeMD. It goes further to outline the necessary char-acteristics that should be standardised. Evaluation questions are provided and demonstrate salient points necessary for responsible and ethical AI.

Publication Details

Journal
STOR (Dundalk Institute of Technology)
Published
2025-12-02
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
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article

Evaluating Pre-trained 3rd Party AI Models in Medical Device Software Development for Responsible and Ethical Use

STOR (Dundalk Institute of Technology)
Artificial Intelligence in Healthcare and Education
article

Evaluating Pre-trained 3rd Party AI Models in Medical Device Software Development for Responsible and Ethical Use

article en

Abstract

It is well understood that new standards are necessary to ensure the use of Artificial Intel-ligence-enabled Medical Devices (AIeMD) are adequately controlled. CEN-CENELEC (European focus) and IEC (Global focus) are developing such standards. Yet the use of pre-trained AI models, available as Software of Unknown Provenance (SOUP) from 3rd party suppliers, remain excluded from the standards under development. This paper aims to demonstrate the need for standardisation of the qualification process for these models beyond that understood by existing standards such as ISO 13485 and IEC 62304. This is necessary in order to achieve responsible and ethical AI for use in healthcare. This paper demonstrates the need for guidance by setting out emerging use cases as well as the risks and potential risk mitigations available when using pre-trained models as SOUP in the Software Development Lifecycle of AIeMD. It goes further to outline the necessary char-acteristics that should be standardised. Evaluation questions are provided and demonstrate salient points necessary for responsible and ethical AI.

STOR (Dundalk Institute of Technology)
Partnerships for the goals
Openalex Percentile: Top 42%
Artificial Intelligence in Healthcare and Education
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