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AI Use Cases

A collection of over 250 uses for artificial intelligence

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Injury prediction and prevention

Introduction

With the integration of the digital age, there is a growing applicability of Machine Learning (ML) and Artificial Intelligence (AI) in different fields. Another area of concentration is the sports industry. In enhancing performance, increasing fan involvement or even managing and minimizing risks of injuries, ML and AI have growing acumen in reshaping the sports domain. Of all the uses of these technologies, injury prediction and prevention is among the most crucial since these are among the areas that can greatly benefit from them. Through the application of ML and AI, sports teams and organizations can be able to analyze and pick out clues that may result in strains or injuries and therefore take the necessary steps to avoid them while at the same time improving on the athletes’ performance.

Challenges

There are, however, a number of barriers in the application of AI and ML to injury prediction and prevention in sports, as promising as these technologies are. A major barrier is the issue of data; there is a shortage of quality and consistent data. There are many factors that contribute to injuries during sports activities such as genetics, nutrition, training load, sleep, and even mental health. Organising all this data in a certain format may be a challenging task. Also, there are issues of privacy when it comes to health information. Such concerns include the protection of such sensitive information. Finally, the flexibility of human physiology and the dynamic nature of sports events are some of the challenges that hinder the development of an AI model that can reliably forecast injuries.

AI Solutions

There are however, several challenges that one will encounter in the use of AI in injury prediction and prevention in sports. This is where machine learning algorithms come in handy, these algorithms are capable of identifying trends and risks factors that may cause injuries through the analysis of large data sets. For example, Kitman Labs has created an AI engine that employ machine learning to understand the movements of athletes and their vulnerabilities. Likewise, Zone7 is an AI-based application that employs historical data to determine and avoid injuries. It incorporates high-level analytics and ML models that break down over 250 parameters including training load, sleep, nutrition, biomechanics, and many others. Other than that, wearable devices are also used to gather the information about athletes’ performance and their physiological state in real-time, which can be further analyzed by AI to determine the possibility of getting an injury.

Benefits

There are a number of ways in which ML and AI can be used in the sports industry to improve injury prediction and prevention and each of them presents a list of advantages. First of all, it can greatly minimize the possibility of injuries which will in turn increase athletes’ career span and productivity. This is because with the prediction of injuries, the necessary precautions can be taken and sports teams will not incur huge costs due to player injuries. Secondly, it can assist in developing individualized training regimes since it will establish the risk factors for each person. Finally, it can give a clear picture of the players’ health and performance status thus aiding and assisting coaches and medical personnel.

Return on Investment

The ROI of AI and ML in injury prevention and prediction can be seen as quite impressive. A research done by Accenture states that AI has the potential of minimizing player injuries by 25% in football which translates to potential cost savings of up to $12.5 billion for the top five European leagues in terms of player salaries. Furthermore, in enhancing performance and the longevity of players’ careers, it can generate more revenue from players’ transfers as well as sponsorships. The return on investment is not only economic but also comprises enhanced player condition, team results, and spectators’ interest.

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