Use of artificial intelligence in paediatric anaesthesia: a systematic review
Objectives: Although the development of artificial intelligence (AI) technologies in medicine has been significant, their application to paediatric anaesthesia is not well characterised. As the paediatric operating room is a data-rich environment that requires critical clinical decision-making, this...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | BJA Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772609623000035 |
| _version_ | 1797861096773648384 |
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| author | Ryan Antel Ella Sahlas Genevieve Gore Pablo Ingelmo |
| author_facet | Ryan Antel Ella Sahlas Genevieve Gore Pablo Ingelmo |
| author_sort | Ryan Antel |
| collection | DOAJ |
| description | Objectives: Although the development of artificial intelligence (AI) technologies in medicine has been significant, their application to paediatric anaesthesia is not well characterised. As the paediatric operating room is a data-rich environment that requires critical clinical decision-making, this systematic review aims to characterise the current use of AI in paediatric anaesthesia and to identify barriers to the successful integration of such technologies. Methods: This review was registered with PROSPERO (CRD42022304610), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in five electronic databases (Embase, Medline, Central, Scopus, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for paediatric anaesthesia (<18 yr old) within the perioperative setting. Results: From 3313 records identified in the initial search, 40 were included in this review. Identified applications of AI were described for patient risk factor prediction (24 studies; 60%), anaesthetic depth estimation (2; 5%), anaesthetic medication/technique decision guidance (2; 5%), intubation assistance (1; 2.5%), airway device selection (3; 7.5%), physiological variable monitoring (6; 15%), and operating room scheduling (2; 5%). Multiple domains of AI were discussed including machine learning, computer vision, fuzzy logic, and natural language processing. Conclusion: There is an emerging literature regarding applications of AI for paediatric anaesthesia, and their clinical integration holds potential for ultimately improving patient outcomes. However, multiple barriers to their clinical integration remain including a lack of high-quality input data, lack of external validation/evaluation, and unclear generalisability to diverse settings. Systematic review protocol: CRD42022304610 (PROSPERO). |
| first_indexed | 2024-04-09T21:56:34Z |
| format | Article |
| id | doaj.art-92aa1e3902844625b9ee40b8241419c8 |
| institution | Directory Open Access Journal |
| issn | 2772-6096 |
| language | English |
| last_indexed | 2024-04-09T21:56:34Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | BJA Open |
| spelling | doaj.art-92aa1e3902844625b9ee40b8241419c82023-03-24T04:23:35ZengElsevierBJA Open2772-60962023-03-015100125Use of artificial intelligence in paediatric anaesthesia: a systematic reviewRyan Antel0Ella Sahlas1Genevieve Gore2Pablo Ingelmo3Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada; Corresponding author.Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, CanadaSchulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Montreal, Quebec, CanadaDepartment of Anesthesia, Montreal Children's Hospital, McGill University, Montreal, Quebec, CanadaObjectives: Although the development of artificial intelligence (AI) technologies in medicine has been significant, their application to paediatric anaesthesia is not well characterised. As the paediatric operating room is a data-rich environment that requires critical clinical decision-making, this systematic review aims to characterise the current use of AI in paediatric anaesthesia and to identify barriers to the successful integration of such technologies. Methods: This review was registered with PROSPERO (CRD42022304610), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in five electronic databases (Embase, Medline, Central, Scopus, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for paediatric anaesthesia (<18 yr old) within the perioperative setting. Results: From 3313 records identified in the initial search, 40 were included in this review. Identified applications of AI were described for patient risk factor prediction (24 studies; 60%), anaesthetic depth estimation (2; 5%), anaesthetic medication/technique decision guidance (2; 5%), intubation assistance (1; 2.5%), airway device selection (3; 7.5%), physiological variable monitoring (6; 15%), and operating room scheduling (2; 5%). Multiple domains of AI were discussed including machine learning, computer vision, fuzzy logic, and natural language processing. Conclusion: There is an emerging literature regarding applications of AI for paediatric anaesthesia, and their clinical integration holds potential for ultimately improving patient outcomes. However, multiple barriers to their clinical integration remain including a lack of high-quality input data, lack of external validation/evaluation, and unclear generalisability to diverse settings. Systematic review protocol: CRD42022304610 (PROSPERO).http://www.sciencedirect.com/science/article/pii/S2772609623000035artificial intelligencecritical caremachine learningpaediatric anaesthesiaperioperative medicine |
| spellingShingle | Ryan Antel Ella Sahlas Genevieve Gore Pablo Ingelmo Use of artificial intelligence in paediatric anaesthesia: a systematic review BJA Open artificial intelligence critical care machine learning paediatric anaesthesia perioperative medicine |
| title | Use of artificial intelligence in paediatric anaesthesia: a systematic review |
| title_full | Use of artificial intelligence in paediatric anaesthesia: a systematic review |
| title_fullStr | Use of artificial intelligence in paediatric anaesthesia: a systematic review |
| title_full_unstemmed | Use of artificial intelligence in paediatric anaesthesia: a systematic review |
| title_short | Use of artificial intelligence in paediatric anaesthesia: a systematic review |
| title_sort | use of artificial intelligence in paediatric anaesthesia a systematic review |
| topic | artificial intelligence critical care machine learning paediatric anaesthesia perioperative medicine |
| url | http://www.sciencedirect.com/science/article/pii/S2772609623000035 |
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