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VOLUME 3 , ISSUE 2 ( April-June, 2024 ) > List of Articles


Artificial Intelligence in Newborn Medicine

Thierry AGM Huisman, Thierry AGM Huisman

Keywords : Critical, Generative pre-trained transformers, Neonate, Patient triage, Predictive modeling techniques, Premature, Resource allocation, Telemedicine consultations, Timely detection

Citation Information : Huisman TA, Huisman TA. Artificial Intelligence in Newborn Medicine. 2024; 3 (2):96-110.

DOI: 10.5005/jp-journals-11002-0095

License: CC BY-NC 4.0

Published Online: 21-06-2024

Copyright Statement:  Copyright © 2024; The Author(s).


The development of artificial intelligence (AI) algorithms has evoked a mixed-feeling reaction, a combination of excitement but also some trepidation, with reminders of caution coming up each time a novel AI-related academic/medical software program is proposed. There is awareness, with some hesitancy, that these algorithms could turn out to be a continuous, transformational source of clinical and educational information. Several AI algorithms with varying strengths and weaknesses are known, but the deep-learning pathways known as the Generative Pre-trained Transformers (GPT) have evoked the most interest as clinical decision-support systems. Again, these tools still need validation and all steps should undergo multiple checks and cross-checks prior to any implementation in human medicine. If, however, testing eventually confirms the utility of these pathways, there is a possibility of a non-incremental advancement of immense value. Artificial intelligence can be helpful by facilitating appropriate analysis of the large bodies of data that are available but are not being uniformly and comprehensively analyzed at all centers. It could promote appropriate, timely diagnoses, testing for efficacy with less bias, fewer diagnostic and medication errors, and good follow-up. Predictive modeling can help in appropriate allocation of resources by identifying at-risk newborns right at the outset. Artificial intelligence can also help develop information packets to engage and educate families. In academics, it can help in an unbiased, all-inclusive analysis of medical literature on a continuous basis for education and research. We know that there will be challenges in protection of privacy in handling data, bias in algorithms, and in regulatory compliance. Continued efforts will be needed to understand and streamline AI. However, if the medical community hesitates today in overseeing this juggernaut, the inclusion (or not) of AI in medicine might not stop—it might just gradually get extrapolated into patient care from other organizations/industry for cost reasons, not justification based on actual clinical data. If we do not get involved in this process to oversee the development/incorporation of AI in newborn medicine, the questions in making decisions will just change from who, to which, when, and how. Maybe this will not be the most appropriate scenario. To conclude, AI has definite benefits; we should embrace AI developments as valuable tools that can assist physicians in analyzing large and complex datasets, which will facilitate the identification of key facts/findings that might be missed if studied by humans. On the other hand, a well-designed and critical expert review board is mandatory to prevent AI-generated systematic errors.

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