Cardiovascular disease (CVD) is one of the leading causes of death across the world. CVD can lead to angina, heart attacks, heart failure, strokes, and eventually, death; among many other serious conditions. The early intervention with those at a higher risk of developing CVD, typically with statin treatment, leads to better health outcomes. For this reason, clinical prediction models (CPMs) have been developed to identify those at a high risk of developing CVD so that treatment can begin at an earlier stage. Currently, CPMs are built around statistical analysis of factors linked to developing CVD, such as body mass index and family history. The emerging field of machine learning (ML) in healthcare, using computer algorithms that learn from a dataset without explicit programming, has the potential to outperform the CPMs available today. ML has already shown exciting progress in the detection of skin malignancies, bone fractures and many other medical conditions. In this review, we will analyse and explain the CPMs currently in use with comparisons to their developing ML counterparts. We have found that although the newest non-ML CPMs are effective, ML-based approaches consistently outperform them. However, improvements to the literature need to be made before ML should be implemented over current CPMs.

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The future undergraduate curriculum should reflect this, given that 38.1% of medical students will go on into primary care. 8 We do not know exactly how the future will unfold; however, we feel that even if there is a gradual shift back towards routine in-person consultations as the standard, the use of telephone consultations will undoubtedly be utilized to a much greater extent. Additionally, these audio-consultation skills are now being examined in the form of Recorded Consultation Assessments (RCA) in the Royal College of General Practitioners Curriculum. 9 We feel that this should be extended and considered for undergraduate assessments as well-preparing them for the evolving reality of the medical workplace that they will enter in tomorrow's world.