Navigating the Quandaries of Artificial Intelligence-Driven Mental Health Decision Support in Healthcare

Introduction

Artificial Intelligence (AI) is a general term that refers to using a system or machine with human behavior to mimic the thoughts and actions of a human (Hamet & Tremblay, 2017; Humerick, Matthew, 2018). AI can perform advanced tasks previously considered only humans and handle large amounts of data that are too complex for a human to handle (Pannu, 2015). AI can be used in many areas, such as security, healthcare, transport, industrial automation, and agriculture (Pannu, 2015). The potential of AI is considered endless, and many industries are eager to tap into its possibilities (Pannu, 2015). One of these is healthcare, where AI has the potential to become a new asset. Healthcare generally uses outdated technologies and systems (Chowdhury, 2012), and while there is great interest in using AI, it currently has the biggest role in research. As each industry has specific needs, AI systems must be developed with these needs in mind, as the general complexity of healthcare can bring some challenges to the use of AI (Morley et al., 2020). Healthcare must identify these challenges, as the consequences can endanger people’s health and well-being (Morley et al., 2020). One use for AI is as an intelligent decision support system (eng: Intelligent Decision Support Systems, IDSS). With the help of intelligent decision support, users can enter data and then get a result that can be used to make a decision (Tariq & Rafi, 2012).

A care context in great need of support is the treatment of mental illness (Baker & Kirk-Wade, 2023; Kakuma et al., 2011). Mental illness is increasing above all among the ages 10–34 at the same time as there is a global shortage of healthcare professionals in the field of, for example, psychiatrists, psychologists, and psychotherapists (Kakuma et al., 2011; Richter et al., 2019). Diagnosing mental illness is also complex as patients can suffer from both somatic illnesses, which refer to physical and bodily illnesses, and psychological problems in parallel (Davenport & Kalakota, 2019). Intelligent decision support has great potential in healthcare for mental illness, where there is an increased need for early interventions for diagnosis, as there are currently no resources and personnel with relevant skills. With the help of intelligent decision support, the burden on the staff can also be reduced (Davenport & Kalakota, 2019). If AI is to be used as decision support in mental illness, its ethical challenges must be identified and investigated.

When using complex AI systems, it is not uncommon for its users to not understand how the system works (Barredo Arrieta et al., 2020). AI also processes large amounts of personal and personal data where ethical complications can arise (Pannu, 2015). As interest in AI has increased in recent years, there has also been an increased need to understand ethics, accountability, and additional difficulties with justice and responsibility (Kokciyan et al., 2021). Due to its complexity and high opacity, ethical challenges arise in the use of AI that need to be explored (Brey, 2012). Due to the complexity of the challenges and the importance of understanding them well, this essay aims to answer the question: What ethical challenges arise when using intelligent decision support in healthcare for mental illness? The study aims to investigate and understand the complex ethical challenges with AI as decision support in mental health care. The use of intelligent decision support may expose both physicians and patients to undesired consequences, and an understanding of the ethical challenges that may arise may facilitate the safe use of the system. Ethical challenges identified from the literature are further investigated through an empirical study to understand which ethical issues become relevant when using AI in mental health care.