A growing number of repetitive and time-consuming tasks, such as processing, and analysis of medical data, as well as reporting of findings can be performed by AI (12, 13). For example, language-based AI such as ChatGPT (OpenAI, CA, USA) has already demonstrated its potential to assist healthcare professionals in writing medical reports (14), although its applicability with respect to various types of sports writing remains to be established.

AI systems can process large sets of data and perform complex calculations, thereby enabling accurate and efficient analysis of (longitudinal) data on numerous athletes (9). For example, certain types of AI can already analyze the spatiotemporal behavior of soccer players in a manner that allows, among other things, automatic identification of dynamic attack formations, information which might be of value in connection with tactical training (15).

In connection with its rapid processing and analysis of large amounts of data, AI may reveal patterns, trends, relationships, and other insights not immediately apparent to human observers (16–20).

When the data employed by AI fails to encompass all relevant aspects of the question being posed (6) and/or is characterized by low quality, flaws, bias, incompleteness, or unreliability, the analyses conducted and the prospective insights derived or conclusions drawn could be fallacious. Particularly worrisome in this context is the fact that the data employed to train AI models are often historical and may not always capture emerging trends, changes in techniques, strategies or rules, or individual differences that can exert a significant impact on the current situation. Similarly, changes in the situation of individual athletes, such as injuries, improvements or deterioration in form, or personal circumstances may not be adequately accounted for. To mitigate this limitation, it is essential to continuously update AI models, as well as to incorporate mechanisms that can identify emerging trends and individual variations.

If the training data are biased (e.g., with respect to race, sex or age), recommendations based on this data may be biased as well, as widely discussed in the medical literature (21). This situation may be exacerbated by the propagation of biases from earlier to later models of AI.

Some AI systems, such as artificial neural networks, can be considered “black boxes” which perform data analysis and decision-making that is un- or even counterintuitive to human brains (6). This absence of transparency in AI models can raise concerns among athletes, coaches, and other stakeholders. For instance, when an AI model is initially trained using data regarding elite athletes, but subsequently applied to non-elite or sub-elite athletes, it may introduce biases that could result in erroneous decisions and pose potential risks to athletes which would not be apparent to the humans involved, due to the opaque nature of the decision-making process.

While AI can provide valuable insights, it should not replace human judgment entirely. Athletes and coaches should not abandon their own critical evaluations, but make the final decisions, relying on AI for support. Unfortunately, in this context recent research findings indicate the presence of a tendency towards excessive reliance on AI for decision-making, with an associated potential risk of making the wrong decision (22).

Application of AI to sport science raises ethical concerns e.g., regarding the privacy of and consent to use the data collected, which must be addressed. In addition, over-reliance on AI may limit the personal experience and thereby hinder the personal growth of prospective trainers.

Reliable application of AI technology to sport science may require a considerable level of expertise, significant financial investment, specialized infrastructure, and individuals who can accurately interpret the output (6). This could give teams with more resources a considerable advantage.

As AI becomes more prevalent, humans may have to do more and more “cleaning” up and otherwise preparing data for usage by AI. This may necessitate hiring additional personnel or require regular staff members to allocate more of their time to such duties, potentially reducing the time they have available for interaction with their athletes.

AI allows overall individual athletic performance and relevant individual physiological variables to be analyzed both in real-time and long-term. Such feedback enables short- and long-term adjustments (23) in training load and other interventions that can help individualize and optimize both training and competitive performance. Sufficiently comprehensive data, combining video recordings with a wide variety of measurements (24), also promises to predict future performance and aid in early diagnosis of injury, followed by design, monitoring and assessment of appropriate rehabilitation.

On the basis of their analyses of individual athletes, including their health and history of injuries, AI-based systems may aid coaches and medical staff in preventing injuries before they occur (17). In this context, artificial neural networks, decision trees and support vectors are already being used to assess risk for injury in connection with different team sports (17).

By analyzing large amounts of data concerning the determinants of performance, including physical attributes, and other potential indicators of success for individual athletes, AI can potentially help identify new talent (19, 20).

In addition to simplifying the monitoring of indicators of performance which have been previously difficult to measure, the correct comprehensive analysis and interpretation of data on both individual and groups of athletes by AI could help reveal novel indicators of performance (25).

The massive amounts of objective data that can be collected and analyzed by AI allow coaches and athletes to more easily make data- and, ideally, evidence-based decisions about training, competition, and coaching strategies concerning, e.g., training intensities, recovery protocols, and tactical adjustments. For instance, training plans for kickboxers generated automatically can be tailored to each individual athlete's current level of performance, period of development, and performance goals, thereby not only reducing the time required for planning, but also providing plans that were more comprehensive and personalized than those designed by an expert coach (26).

The performance of repetitive and time-consuming tasks by AI could free up more time for personal activities that improve the relationship between a coach and his athletes, which is often regarded as a key factor in the effectiveness of training (27).

AI-powered platforms and systems have the potential to facilitate collaboration and other types of interactions between sport scientists, coaches, and athletes, enabling easy sharing of data, insights, and guidelines for best practices.

The collection, storage and analysis of sensitive data on individual athletes by AI can expose organizations to privacy breaches, cyber-attacks, or other forms of unauthorized access. Thus, robust data protection is required. In addition, there is the possibility that the company managing the AI system may utilize personal data for the purpose of enhancing their models, which may lead to unintentional exposure of this data to third-party users.

The incorporation of AI into sport science raises a number of legal and regulatory concerns, such as ownership of data and copyrights, liability for deleterious consequences of AI-based decisions, and compliance with prevailing regulations concerning data protection. To address such challenges, the European Parliament recently issued a resolution aimed at legislative regulation of AI (28).

For a variety of reasons, malicious actors may attempt to manipulate the data employed by AI systems, even introducing false information, actions commonly referred to as adversarial attacks and which can alter the decisions arrived at. One such example would be manipulating the camera of a self-driving car so that it overlooks red traffic lights (29). In a similar manner, sports data could be altered by competitors to cause the opposing team to, for example, adopt suboptimal tactics.

Usage of AI in sport science may impair the psychological well-being of athletes, leading, for example, to performance anxiety, pressure to conform to AI-generated recommendations, and less trust in the coach. Communication and psychological aspects of the relationships between athletes, coaches and the support staff may be damaged.

As with any new technology, integration of AI into sports science may face resistance (6). There can be many reasons for such resistance and these need to be assessed employing, e.g., the technology acceptance framework (30) to survey coaches, athletes and other stakeholders.