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Artificial intelligence applications in the football codes: A systematic review.
- Source :
- Journal of Sports Sciences; Jul2024, Vol. 42 Issue 13, p1184-1199, 16p
- Publication Year :
- 2024
-
Abstract
- Artificial Intelligence (AI) is increasingly being adopted across many domains such as transport, healthcare, defence and sport, with football codes no exception. Though there is a range of potential benefits of AI, concern has also been expressed regarding potential risks. An important first step in ensuring that AI applications in football are usable, beneficial, safe and ethical is to understand the current range of applications, the AI models adopted and their proposed functions. This systematic review aimed to identify different applications of AI across football codes to synthesise current knowledge and determine whether potential risks are being considered. The systematic review included 190 peer-reviewed articles. Nine areas of application were found ranging from athlete evaluation and event detection to match outcome prediction and injury detection and prediction. In total, 27 different AI models were identified, with artificial neural networks the most frequently applied. Five AI assessment metrics were identified including specificity, recall, precision, accuracy and F1-score. Four potential risks were identified, concerning data security, usability, data biases and inappropriate athlete load management. It is concluded that, though a wide range of AI applications currently exist, further work is required to develop AI for football and identify and manage potential risks. [ABSTRACT FROM AUTHOR]
- Subjects :
- RISK assessment
DATA security
SPORTS
ARTIFICIAL intelligence
SPORTS injuries
AUSTRALIAN football
PROFESSIONAL peer review
INFORMATION storage & retrieval systems
SYSTEMATIC reviews
MEDLINE
MEDICAL coding
ARTIFICIAL neural networks
ONLINE information services
SENSITIVITY & specificity (Statistics)
Subjects
Details
- Language :
- English
- ISSN :
- 02640414
- Volume :
- 42
- Issue :
- 13
- Database :
- Complementary Index
- Journal :
- Journal of Sports Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 179359968
- Full Text :
- https://doi.org/10.1080/02640414.2024.2383065