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Old wine in new bottles? What is new with AI for mental health diagnosis?
  1. Marcos Paulo de Lucca Silveira1,2
  1. 1São Paulo School of Economics, Sao Paulo, SP, Brazil
  2. 2Fundação José Luiz Egydio Setúbal, Sao Paulo, SP, Brazil
  1. Correspondence to Dr Marcos Paulo de Lucca Silveira, São Paulo School of Economics, Sao Paulo, SP, Brazil; marcos.silveira{at}fgv.br

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Ugar and Malele1 critique the use of ‘generic’ technologies like artificial intelligence (AI) and machine learning (ML) for mental health diagnoses, particularly in sub-Saharan African countries. They highlight how these AI medical tools often overlook traditional perspectives and local contexts. The article has the merit of working on ethical issues regarding the particularities and risks of using AI and ML for health diagnosis in the Global South, an urgent and neglected topic.

According to the authors, the use of these AI technologies leads to overgeneralisation in diagnosing mental disorders, which could be especially problematic in the mental health field because of value-laden judgements intrinsic to the definition of mental health disorders. This argument is theoretically grounded in the hybrid conceptualisation of mental disorders proposed by Wakefield.2 This author’s perspective incorporates both factual and value components in defining mental disorders, framing them as context-dependent ‘harmful dysfunctions’ that are sensitive to social norms and cultural perspectives. However, there is no elaborate discussion in the article that justifies or substantiates the adherence to this hybrid conceptualisation and not to other theoretical conceptualisations …

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Footnotes

  • Contributors MPLS conducted the research and prepared the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; internally peer reviewed.

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