This chapter discusses the practical applications of artificial intelligence (AI) in diagnosing autism spectrum disorder (ASD) through an innovative iPad-based serious game. It begins by specifying that the focus is on machine learning as it applies to health diagnosis. The case study presented involves a game designed to detect ASD in children with greater accuracy and earlier in the child’s life than traditional diagnostic methods. In the chapter, we begin by contrasting two perspectives on ASD: the medical view, which sees it as a disability, and the sociological perspective, which advocates for neurodiversity. Next, we emphasize the importance of early diagnosis for ASD, reporting on how it can significantly improve developmental trajectories and life outcomes for affected children. Professor Jonathan Delafield-Butt, who developed the game, shares insights on how movement patterns in children can indicate autism and how the technology has evolved to utilize touch-sensitive screens for data collection. Professor Delafield-Butt argues that AI does not replace the expertise of health professionals but serves as a complementary tool, expediting diagnosis and enhancing decision-making. The chapter concludes by detailing the various forms of value generated by this AI- powered tool, including economic benefits for healthcare systems and enhanced wellbeing for families and children undergoing diagnosis. By providing clearer, quicker insights into a child’s condition, the game not only alleviates anxiety for parents but also facilitates timely intervention for children with ASD. Ultimately, the article illustrates how AI can transform medical diagnostics in a socially inclusive manner, benefiting both practitioners and patients.