Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review.
Rebecca Giddings, Anabel Joseph, Thomas Callender, Sam M Janes, Mihaela van der Schaar, Jessica Sheringham, Neal Navani
Author Information
Rebecca Giddings: Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK. Electronic address: r.giddings@ucl.ac.uk.
Anabel Joseph: Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK.
Thomas Callender: Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK.
Sam M Janes: Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK.
Mihaela van der Schaar: Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK; The Alan Turing Institute, London, UK.
Jessica Sheringham: Department of Applied Health Research, University College London, London, UK.
Neal Navani: Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK.
Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models is scarce. We aimed to review health-care professional (HCP) and patient perceptions of ML risk prediction models in published literature, to inform future risk prediction model development. Following database and citation searches, we identified 41 articles suitable for inclusion. Article quality varied with qualitative studies performing strongest. Overall, perceptions of ML risk prediction models were positive. HCPs and patients considered that models have the potential to add benefit in the health-care setting. However, reservations remain; for example, concerns regarding data quality for model development and fears of unintended consequences following ML model use. We identified that public views regarding these models might be more negative than HCPs and that concerns (eg, extra demands on workload) were not always borne out in practice. Conclusions are tempered by the low number of patient and public studies, the absence of participant ethnic diversity, and variation in article quality. We identified gaps in knowledge (particularly views from under-represented groups) and optimum methods for model explanation and alerts, which require future research.
