In their paper ‘Designing AI for mental health diagnosis: challenges from sub-Saharan African value-laden judgements on mental health disorders’, Ugar and Malele focused on the challenges and considerations surrounding the design and implementation of artificial intelligence (AI) and machine learning (ML) technologies for diagnosing mental health disorders in South Africa. Although the authors recognise the application of AI and ML in healthcare, they put forward the challenges, particularly in adopting Wakefield’s hybrid theory, where elements of naturalism and normativism are combined in defining mental disorders. They argue that a generic or universally designed AI or ML would not be appropriate in countries with strong and varied local contexts. Hence, they believe that current and future AI developers should consider the cultural nuances, value systems and contextual factors in designing and implementing AI technologies for mental health diagnosis in Africa.

When the authors argue that a universal definition of mental health may not be appropriate in Africa, we would like to offer something in contrast. If nomenclature like the Diagnostic and Statistical Manual of Mental Disorders (DSM) can be widely accepted, why does AI, which relies on these codes, encounter scepticism? Recognising the significant influence of culture on all aspects of patient care in psychiatry, the DSM-IV introduced the …