Safe AI for health and beyond -- Monitoring to transform a health service

• URL: http://arxiv.org/abs/2303.01513v3
• Date: Tue, 6 Jun 2023 12:02:18 GMT
• Title: Safe AI for health and beyond -- Monitoring to transform a health service
• Authors: Mahed Abroshan, Michael Burkhart, Oscar Giles, Sam Greenbury, Zoe Kourtzi, Jack Roberts, Mihaela van der Schaar, Jannetta S Steyn, Alan Wilson, May Yong
• Abstract summary: We will assess the infrastructure required to monitor the outputs of a machine learning algorithm. We will present two scenarios with examples of monitoring and updates of models.
• Score: 51.8524501805308
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning techniques are effective for building predictive models because they identify patterns in large datasets. Development of a model for complex real-life problems often stop at the point of publication, proof of concept or when made accessible through some mode of deployment. However, a model in the medical domain risks becoming obsolete as patient demographics, systems and clinical practices change. The maintenance and monitoring of predictive model performance post-publication is crucial to enable their safe and effective long-term use. We will assess the infrastructure required to monitor the outputs of a machine learning algorithm, and present two scenarios with examples of monitoring and updates of models, firstly on a breast cancer prognosis model trained on public longitudinal data, and secondly on a neurodegenerative stratification algorithm that is currently being developed and tested in clinic.

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