Application and potential of artificial intelligence in neonatal medicine

Chang Kwok, T’ng; Henry, Caroline; Saffaran, Sina; Meeus, Marisse; Bates, Declan; Van Laere, David; Boylan, Geraldine; Boardman, James P.; Sharkey, Don

Abstract

Neonatal care is becoming increasingly complex with large amounts of rich, routinely recorded physiological, diagnostic and outcome data. Artificial intelligence (AI) has the potential to harness this vast quantity and range of information and become a powerful tool to support clinical decision making, personalised care, precise prognostics, and enhance patient safety. Current AI approaches in neonatal medicine include tools for disease prediction and risk stratification, neurological diagnostic support and novel image recognition technologies. Key to the integration of AI in neonatal medicine is the understanding of its limitations and a standardised critical appraisal of AI tools. Barriers and challenges to this include the quality of datasets used, performance assessment, and appropriate external validation and clinical impact studies. Improving digital literacy amongst healthcare professionals and cross-disciplinary collaborations are needed to harness the full potential of AI to help take the next significant steps in improving neonatal outcomes for high-risk infants.

Citation

Chang Kwok, T., Henry, C., Saffaran, S., Meeus, M., Bates, D., Van Laere, D., Boylan, G., Boardman, J. P., & Sharkey, D. (2022). Application and potential of artificial intelligence in neonatal medicine. Seminars in Fetal and Neonatal Medicine, 27(5), Article 101346. https://doi.org/10.1016/j.siny.2022.101346

• Journal Article Type: Article
• Acceptance Date: Mar 31, 2022
• Online Publication Date: Apr 18, 2022
• Publication Date: Oct 1, 2022
• Deposit Date: Apr 26, 2022
• Publicly Available Date: Apr 28, 2022
• Journal: Seminars in Fetal and Neonatal Medicine
• Print ISSN: 1744-165X
• Electronic ISSN: 1878-0946
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 27
• Issue: 5
• Article Number: 101346
• DOI: https://doi.org/10.1016/j.siny.2022.101346
• Keywords: Pediatrics, Perinatology and Child Health
• Public URL: https://nottingham-repository.worktribe.com/output/7834009
• Publisher URL: https://www.sfnmjournal.com/article/S1744-165X(22)00025-7/fulltext
• Related Public URLs: https://www.sciencedirect.com/science/article/pii/S1744165X22000257