This study will employ a cross-sectional time horizon in terms of collecting momentous data regarding the importance of AI algorithms and ML to reduce the diagnostic errors among young adult patients in the healthcare system in the UK. According to Wang & Cheng (2020), using a cross-sectional time horizon gives cost effective advantages in terms of saving time and resources and helping to accumulate momentary data at a single point in time. In this context, using a cross-sectional time horizon, this current study would get effective relevant data from a vast amount of data regarding the subject matter at a specific point of time. Moreover, opting for the cross-sectional time horizon, the current research would be proficient in terms of delivering multiple variables such as AI and ML, diagnostic errors, and UK healthcare at a momentary time.