Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review

Abstract

Machine learning (ML) holds great potential for predicting clinical outcomes in heterogeneous chronic respiratory diseases (CRD) affecting children, where timely individualised treatments offer opportunities for health optimisation. This paper identifies rate-limiting steps in ML prediction model development that impair clinical translation and discusses regulatory, clinical and ethical considerations for ML implementation. A scoping review of ML prediction models in paediatric CRDs was undertaken using the PRISMA extension scoping review guidelines. From 1209 results, 25 articles published between 2013 and 2021 were evaluated for features of a good clinical prediction model using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines.Most of the studies were in asthma (80%), with few in cystic fibrosis (12%), bronchiolitis (4%) and childhood wheeze (4%). There were inconsistencies in model reporting and studies were limited by a lack of validation, and absence of equations or code for replication. Clinician involvement during ML model development is essential and diversity, equity and inclusion should be assessed at each step of the ML pipeline to ensure algorithms do not promote or amplify health disparities among marginalised groups. As ML prediction studies become more frequent, it is important that models are rigorously developed using published guidelines and take account of regulatory frameworks which depend on model complexity, patient safety, accountability and liability.

Defining medical liability when artificial intelligence is applied on diagnostic algorithms: a systematic review

“…The development of predictive AI models which were trained with data that do not reflect the context of use of the algorithm represent the so-called “contextual bias” ( 17 ). As consequence, despite the potential to improve the access to care ( 21 , 22 ), AI algorithms may amplify or create health disparities among marginalized groups ( 23 ), augment racial and demographic disparities ( 13 ), and exacerbate inequities in health outcomes ( 18 ).…”

Virtual monitoring in CF – the importance of continuous monitoring in a multi-organ chronic condition

“…AI could be used to develop predictive models to identify patients who are at high risk of pulmonary exacerbation, disease progression or non-adherence to their treatment plan. This information can be used to develop targeted interventions, such as personalised education and support, to improve adherence, and ultimately improve patient outcomes ( 36 ).…”

Evaluating AI's Role in Enhancing DE and I: A Bibliometric Approach

“…By analyzing a comprehensive set of academic publications, we aim to uncover patterns, influential studies, and gaps in the current body of knowledge as suggested by our previous research study (Charul et.al). This approach will provide a robust foundation for understanding how AI can be leveraged to support DE&I efforts effectively and responsibly [18][19][20][21][22][23].…”