AI-Based Doping Detection: Technical Approaches, Advantages and Challenges

Abstract

Doping in competitive sports threatens fairness, athlete health, and integrity. Traditional doping detection methods such as laboratory analyses and Athlete Biological Passports (ABP) face limitations in scalability, adaptability, and timeliness. Artificial Intelligence (AI) offers promising solutions by analyzing complex datasets, detecting subtle patterns, and enabling real-time monitoring. This paper reviews AI-based technical approaches, evaluates the advantages over conventional methods, and discusses the key challenges of implementation, including data quality, ethical concerns, and regulatory integration. In recent years, Artificial Intelligence (AI) has emerged as a powerful tool in anti-doping efforts due to its ability to process large and complex datasets, identify subtle patterns, and support real-time monitoring. This paper examines AI-based technical approaches to doping detection, such as machine learning, deep learning, anomaly detection, and natural language processing. It highlights the advantages of AI-enabled systems, including enhanced sensitivity, scalability, adaptability, and cost efficiency. At the same time, the study discusses key challenges such as data quality and availability, model transparency, ethical and legal concerns, privacy issues, and integration with regulatory frameworks. The paper concludes that while AI has significant potential to strengthen anti-doping mechanisms, its effective implementation requires careful governance, ethical oversight, and collaboration between technologists, sports scientists, and regulatory bodies.

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