Towards a SAFETY-AI framework for Healthcare Education

Kinza Salim, Vanita Kouomogne Nana, Mark T. Marshall, Hoang D. Nguyen
Reliable and Trustworthy Artificial Intelligence 2025, PMLR 310:102-114, 2025.

Abstract

Safety is an integral part of healthcare professionalism, and with new technological developments, such as Artificial Intelligence (AI), there is an ongoing need to develop guardrails for healthcare education. The landscape of AI safety frameworks for healthcare education is evolving, with significant development in regulatory compliance, ethical governance, and practical implementation approaches. This paper addresses the need for building a SAFETY-AI framework for healthcare education and proposes a solution towards it. It also provides subjective insights regarding trustworthiness, reliability and the existing concepts of safety in healthcare setups. This work stands as a roadmap for safety in AI practices for healthcare policy makers, educators and clinicians.

Cite this Paper

BibTeX
@InProceedings{pmlr-v310-salim25a, title = {Towards a SAFETY-AI framework for Healthcare Education}, author = {Salim, Kinza and Nana, Vanita Kouomogne and Marshall, Mark T. and Nguyen, Hoang D.}, booktitle = {Reliable and Trustworthy Artificial Intelligence 2025}, pages = {102--114}, year = {2025}, editor = {Nguyen, Hoang D. and Le, Duc-Trong and Björklund, Johanna and Vu, Xuan-Son}, volume = {310}, series = {Proceedings of Machine Learning Research}, month = {12 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v310/main/assets/salim25a/salim25a.pdf}, url = {https://proceedings.mlr.press/v310/salim25a.html}, abstract = {Safety is an integral part of healthcare professionalism, and with new technological developments, such as Artificial Intelligence (AI), there is an ongoing need to develop guardrails for healthcare education. The landscape of AI safety frameworks for healthcare education is evolving, with significant development in regulatory compliance, ethical governance, and practical implementation approaches. This paper addresses the need for building a SAFETY-AI framework for healthcare education and proposes a solution towards it. It also provides subjective insights regarding trustworthiness, reliability and the existing concepts of safety in healthcare setups. This work stands as a roadmap for safety in AI practices for healthcare policy makers, educators and clinicians.} }
Endnote
%0 Conference Paper %T Towards a SAFETY-AI framework for Healthcare Education %A Kinza Salim %A Vanita Kouomogne Nana %A Mark T. Marshall %A Hoang D. Nguyen %B Reliable and Trustworthy Artificial Intelligence 2025 %C Proceedings of Machine Learning Research %D 2025 %E Hoang D. Nguyen %E Duc-Trong Le %E Johanna Björklund %E Xuan-Son Vu %F pmlr-v310-salim25a %I PMLR %P 102--114 %U https://proceedings.mlr.press/v310/salim25a.html %V 310 %X Safety is an integral part of healthcare professionalism, and with new technological developments, such as Artificial Intelligence (AI), there is an ongoing need to develop guardrails for healthcare education. The landscape of AI safety frameworks for healthcare education is evolving, with significant development in regulatory compliance, ethical governance, and practical implementation approaches. This paper addresses the need for building a SAFETY-AI framework for healthcare education and proposes a solution towards it. It also provides subjective insights regarding trustworthiness, reliability and the existing concepts of safety in healthcare setups. This work stands as a roadmap for safety in AI practices for healthcare policy makers, educators and clinicians.
APA
Salim, K., Nana, V.K., Marshall, M.T. & Nguyen, H.D.. (2025). Towards a SAFETY-AI framework for Healthcare Education. Reliable and Trustworthy Artificial Intelligence 2025, in Proceedings of Machine Learning Research 310:102-114 Available from https://proceedings.mlr.press/v310/salim25a.html.

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