Leveraging artificial intelligence for self-diagnosis systems: innovation in healthcare management

Nicola Cobelli, Fabio Cassia and Marta Maria Ugolini

Chapter 10 in Multidisciplinary Movements in AI and Generative AI, 2025, pp 179-196 from Edward Elgar Publishing

Abstract: The application of artificial intelligence (AI) to provide individuals with self-diagnosis systems (typically available through mobile apps) is emerging as a new trend in healthcare. AI-based self-diagnosis systems have the potential to contribute to the digital transition by integrating digitalisation and healthcare, specifically understood as the wellbeing of the individual and society at large. These systems rely on the latest developments in digitalisation and use algorithms that continuously learn from a large amount of clinical and medical data to optimise diagnoses based on the symptoms reported by users. If properly integrated with the services provided by healthcare systems, these innovations may improve service delivery for individuals. Specifically, AI-based self-diagnosis systems could play an important role from the perspective of the world's ageing societies, which will result in an increasing number of patients with specific healthcare needs. However, several challenges still need to be addressed, including the evaluation of the correctness of diagnoses, privacy protection, the risk of dehumanisation of healthcare, and effects on the relationship between AI and healthcare professionals, creating the need for proper regulation. Overall, knowledge of AI-based self-diagnosis systems and their impacts remains limited, particularly from the consumer perspective. Individuals’ attitudes towards and (potential) use of self-diagnosis systems are still mostly unknown. This chapter systematically maps current knowledge on this topic and derives reflections and implications. The results of this analysis will be useful for several stakeholders, including healthcare decision makers and policymakers. This work highlights the urgency of exploring how AI impacts the end user today. An examination of the studies presented reveals a large gap in knowledge about end user perceptions, attitudes and beliefs – not least the risk of misinterpretation of results rendered by AI-based self-diagnosis software. From a broader perspective, this chapter underscores the urgent need for in-depth studies of end user use of these innovations. This has implications for advancing our understanding of the digital transition and its effects on individual and societal wellbeing.

Keywords: Artificial intelligence; AI; Self-diagnosis; Bibliometric analysis; Bibliographic coupling; Network analysis (search for similar items in EconPapers)
Date: 2025
ISBN: 9781035358656
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