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

St John Lynch, Niamh and Loughran, Roisin and mchugh, martin, Martin and McCaffery, Fergal (2025) Evaluating Pre-trained 3rd Party AI Models in Medical Device Software Development for Responsible and Ethical Use. In: 33rd International Artificial Intelligence and Cognitive Science (AICS) 2025, 1-2 Dec 2025, DCU, Dublin. (In Press)

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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.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Pre-trained Artificial Intelligence, transformer, foundation model, SOUP, Software Devel-opment Lifecycle, SDLC, Medical Device.
Subjects:	Computer Science > Computer Software
Research Centres:	Regulated Software Research Centre
Depositing User:	Niamh StJohnLynch
Date Deposited:	16 Dec 2025 15:16
Last Modified:	16 Dec 2025 15:16
License:	Creative Commons: Attribution-Noncommercial-Share Alike 4.0
URI:	https://eprints.dkit.ie/id/eprint/975

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