Pediatrics in artificial intelligence era: A systematic review on challenges, opportunities, and explainability
Article
Balla, Y., Tirunagari, S. and Windridge, D. 2023. Pediatrics in artificial intelligence era: A systematic review on challenges, opportunities, and explainability. Indian Pediatrics. 60 (7), pp. 561-569. https://doi.org/10.1007/s13312-023-2936-8
Type | Article |
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Title | Pediatrics in artificial intelligence era: A systematic review on challenges, opportunities, and explainability |
Authors | Balla, Y., Tirunagari, S. and Windridge, D. |
Abstract | Background: The emergence of artificial intelligence (AI) tools such as ChatGPT and Bard is disrupting a broad swathe of fields, including medicine. In pediatric medicine, AI is also increasingly being used across multiple subspecialties. However, the practical application of AI still faces a number of key challenges. Consequently, there is a requirement for a concise overview of the roles of AI across the multiple domains of pediatric medicine, which the current study seeks to address. Aim: To systematically assess the challenges, opportunities, and explainability of AI in pediatric medicine. Methodology: A systematic search was carried out on peer-reviewed databases, PubMed Central, Europe PubMed Central, and grey literature using search terms related to machine learning (ML) and AI for the years 2016 to 2022 in the English language. A total of 210 articles were retrieved that were screened with PRISMA for abstract, year, language, context, and proximal relevance to research aims. A thematic analysis was carried out to extract findings from the included studies. Results: Twenty articles were selected for data abstraction and analysis, with three consistent themes emerging from these articles. In particular, eleven articles address the current state-of-the-art application of AI in diagnosing and predicting health conditions such as behavioral and mental health, cancer, syndromic and metabolic diseases. Five articles highlight the specific challenges of AI deployment in pediatric medicines: data security, handling, authentication, and validation. Four articles set out future opportunities for AI to be adapted: the incorporation of Big Data, cloud computing, precision medicine, and clinical decision support systems. These studies collectively critically evaluate the potential of AI in overcoming current barriers to adoption. Conclusion: AI is proving disruptive within pediatric medicine and is presently associated with challenges, opportunities, and the need for explainability. AI should be viewed as a tool to enhance and support clinical decision-making rather than a substitute for human judgement and expertise. Future research should consequently focus on obtaining comprehensive data to ensure the generalizability of research findings. |
Keywords | Artificial intelligence; Data science; Deep learning; Large language model; Neoplasms; Clinical Decision-Making; Child; Pediatrics; Humans |
Sustainable Development Goals | 3 Good health and well-being |
Middlesex University Theme | Health & Wellbeing |
Publisher | Indian Academy of Pediatrics |
Springer | |
Journal | Indian Pediatrics |
ISSN | 0019-6061 |
Electronic | 0974-7559 |
Publication dates | |
Online | 24 Jul 2023 |
15 Jul 2023 | |
Publication process dates | |
Deposited | 01 Jun 2023 |
Accepted | 14 May 2023 |
Output status | Published |
Publisher's version | License File Access Level Open |
Accepted author manuscript | File Access Level Open |
Copyright Statement | This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
Additional information | S097475591600533 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s13312-023-2936-8 |
PubMed ID | 37424120 |
Web of Science identifier | WOS:001034797100014 |
Language | English |
https://repository.mdx.ac.uk/item/8q64q
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Publisher's version
s13312-023-2936-8.pdf | ||
License: CC BY 4.0 | ||
File access level: Open |
Accepted author manuscript
Paediatric_AI_Paper (6).pdf | ||
File access level: Open |
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