Engaging in regular physical activity is important for all ages.1,2 The reasons why people seek a physically active lifestyle vary and may include leisure, competition, socialization, maintenance and improvement of fitness and health.3 Sports practice is an important way for achieving an active lifestyle. However, physical activity- and sports-related injuries are considered major ‘adverse events’ of such practice. Data from an emergency department in Ireland, showed that sports injuries accounted for 14% of all attendances over 6 months, yielding a negative impact in people's lives and long-term consequences.4 Furthermore, approximately 4.3 million nonfatal sports or recreation-related injuries are seen annually in the emergency department in the US, specifically affecting children and adolescents.5

The balance between optimal athletic performance and prevention of sports-related injuries is of primary importance for sports staff members and society.6 However, understanding the interaction and contribution of several factors (e.g., biophysical, social, psychological, cultural, and environmental) to sports-related injury etiology as well as developing, evaluating, and implementing prevention strategies remain major challenges in the field of Sports and Exercise Medicine (SEM).7 In general, individual factors are assumed to have a linear and unidirectional contribution to sports injuries.7,8 This narrow approach relies on correlation and regression analyses and, despite the vast effort to predict sports injuries, it has been limited in its ability to successfully identify predictive factors.7 Recently, complex systems approaches have been suggested to aid in understanding the multifactorial complex nature of sports injuries.7 Complex systems consider that a phenomenon (e.g., injury) occurs not from the linear interaction between isolated factors, but from the complex, dynamic, and non-linear interaction among a web of determinants (for a review see Fonseca et al.7 and Bittencourt et al.8). In this sense, sports injuries occur in dynamic environments and can be referred to as complex problems.9

With the advances in processing power, memory, storage, and real-time data acquisition, computers can help to solve complex problems. In this aspect, artificial intelligence (AI) is widely accepted as a technology offering an alternative way to tackle complex problems. AI is a branch of computer science that is broadly defined as “mimicking human cognition using machines and/or computer science techniques”.10 Machine Learning is a subdiscipline of AI in which computer algorithms learn from large datasets and identify interaction patterns among variables without human interference.11 AI is gradually changing the landscape of healthcare and biomedical research, especially in prediction and diagnosis, but also regarding treatment efficiency and outcome prediction, drug discovery and repurposing, epidemic outbreak prediction, and precision health.12 Thus, there is a plethora of opportunities for AI in the fields of ‘Sport Sciences’ and SEM. In fact, some leagues such as the National Football League (NFL), Major League Baseball (MLB), and German Football League (DFL) have been hosting several ‘competitions’ on Kaggle website (www.kaggle.com), looking to solve real-world problems using AI or Big Data analytics. In this masterclass, we aim to introduce the concepts, applications, challenges, and future of AI in the context of sports.

The term AI is not new. The first use of the term AI was in a Computer Science Conference in 1956.13 Although there is no well-endorsed definition in the literature, AI can be understood as a technology with the ability to respond to environmental information (or new data) and then change its operation to maximize performance mimicking the problem-solving and decision-making capabilities of the human mind.13

Recent interest in AI has been driven by advances in Machine Learning. Machine Learning is a subfield of AI that develops algorithms (using mathematics, statistics, logic, and computer programming) with the ability to identify patterns in data that automatically improve from experience, that is, without being specifically programmed.13 Machine Learning approaches fall into four categories: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Learning methods, definitions, and models are presented in Table 1.

Claudino et al.14 reviewed the literature on Machine Learning models used in sports to predict injury risk and sport performance and found 171 publications in the field of signal processing, 161 publications in the field of image processing, 151 on modelling and planning, and 57 on user interaction. Artificial Neural Network was the most common technique used in both injury risk (representing 10%) and sports performance (representing 26%) models. The decision tree classifier and support vector machine were the next most commonly used techniques, each representing 5% of injury risk assessment. Regarding sports performance prediction, the decision tree classifier was used in 17% of cases, while the Markov process and support vector machine were used in 9%. Soccer had the highest percentage of studies (12%) applying AI for injury risk assessment, followed by basketball, American football, Australian football, and team handball (each with 3%). Basketball had the highest percentage (19%) of studies using AI for performance prediction, followed by soccer (14%) and volleyball (9%).

Although the use of AI-based solutions in sports is growing, there are diverse challenges to their successful implementation. AI systems have complex life cycles, including data acquisition, training, testing, clinical implementation, ethical, and social issues. This section explores some challenges that may be paramount to the appropriate and optimal use of AI.

The first challenge is data availability. In general, Machine Learning methods heavily depend on the nature and the characteristics of data collected to work effectively and efficiently.57 In addition to the need of sufficient data to work properly, Machine Learning requires external validity and continuous availability of data with progressively larger datasets.58 Thus, monitoring performance and retraining of Machine Learning models (robustness) should be considered from the beginning. Considering that the availability of data may be expensive, there is a need to advance data shareability and reusability. Shareability and reusability can contribute to transfer learning (i.e., due to similarities among some sports, a model trained on a sport-specific data can be tested in another).59,60 Biases on data (unfairness) have been reported and it can lead to biased trained Machine Learning models.61 In these cases (e.g., racial bias), minorities are underrepresented contributing to lower prediction performance. To mitigate biases in data, particularly those affecting underrepresented groups, it is proposed: (i) to determine the availability of diverse populations; (ii) to consider diversity in model design while framing hypotheses; (iii) to use recommended preprocessing bias mitigation techniques; (iv) to perform regular assessment of model performance across different demographic groups; (v) to engage with diverse domain experts, multidisciplinary teams, and community members; and (vi) to maintain transparency in reporting data sources.62 Also, most of the available Machine Learning studies in the literature have poor methodological quality. Efforts to improve the design, conduction, reporting, and validation of Machine Learning studies are necessary to bridge the gap towards its application in clinical practice.63

Explainability of Machine Learning models (i.e., results of a used method must be interpretable for humans) is another challenge.64 Currently, many Machine Learning models may be considered ‘black boxes.’ The lack of transparency of ‘black box’ approaches hinders independent evaluation of model performance, interpretability, utility, and generalizability prior to implementation.65 To enhance the explainability of Machine Learning models in sports AI, researchers can integrate techniques such as model simplification and employ explainability frameworks such as Local Interpretable Model-Agnostic Explanations (LIME) or SHapley Additive exPlanations (SHAP) to provide insights into model decision processes. Furthermore, incorporating visualizations, practical examples, and interpretable reports in model outputs can aid non-expert users in comprehending the results.66,67

Regulatory guidance, liability, legal responsibility, and ethical considerations must be in the mind of those in charge of designing, conducting, and implementing AI-based applications in any field, thus also in sports. Some standard and general ethics considerations applicable to AI include68: (1) honesty; (2) truthfulness; (3) transparency; (4) benevolence and non-malevolence; (5) human dignity, autonomy, privacy, and safety; and (6) justice. However, moral values and ethical standards are context-specific and, therefore, they might differ among populations, such as countries, regions, ethnic groups, etc.

Context-specific ethical aspects beyond those described here might be worth consideration, and the sports professionals and researchers must be aware of such aspects and preferably they should discuss AI-related ethical issues with other collaborators, stakeholders, coaches, health professionals, athletes, and/or end-users, when applicable, to ensure that AI applications comply with the required ethical standards. Therefore, it is essential to adopt comprehensive measures for protecting sensitive information, such as: (i) evaluating transparency and effectiveness in obtaining user consent for data collection and processing activities; (ii) ensuring AI systems comply with laws and regulations governing personal data privacy; (iii) data minimization, anonymization, and de-identification techniques; (iv) implementing robust access controls and encryption mechanisms; and (v) conducting privacy impact assessments to identify and address privacy risks.69,70

Industry 4.0, often referred to as the fourth industrial revolution, integrates intelligent digital technologies into manufacturing and industrial processes. It represents a transformative shift in how technology is embedded in industries, enhancing efficiency and connectivity.71 This concept, first introduced in Hanover, Germany, in 2011, serves as a model for subsequent initiatives like Health 4.0.72 Health 4.0 integrates innovative technologies such as the Internet of Health Things (IoHT), medical CyberPhysical Systems (medical CPS), health cloud, health fog, Big Data analytics, Machine Learning, and blockchain to enhance individualized prevention, diagnosis, treatment, and public health.73

Similarly, Sports 4.0 has the potential to tailor and implement foundational principles from Industry and Health 4.0, including: (i) interoperability (allowing different devices and systems to connect); (ii) virtualization (creating digital models of systems and processes); (iii) decentralization (enabling self-governing systems); (iv) real-time capability for prompt data gathering and analysis; (v) service orientation to develop software for interacting with devices; and (vi) modularity (enhancing specific components for meeting new requirements and reusing available modules to build new sports systems).74 These features in Sports 4.0 could change the SEM field by supporting personalized injury prevention, diagnosis, management, training, and rehabilitation, focusing on a coordinated and individual-centric care to optimize health outcomes and encourage physical activity.