Citation

Straw I. Health Policy and Technology 2021; 10(1): 191-195.

Copyright

(Copyright © 2021)

DOI

10.1016/j.hlpt.2020.11.006

PMID

unavailable

Abstract

BACKGROUND: The tools of emotion mining were developed in the field of computer science to detect and evaluate human emotions. The development of social media and digital phenotyping has empowered researchers to apply these techniques to the field of medicine. In recent years we have seen an evolution of novel health technologies that utilise data-driven emotion mining for mental health diagnostics and assessment.

AIM: In this review of psychiatric AI we provide a high level overview of these technologies and their integration into clinical practice. The aim of this paper is to describe novel ethical scenarios posed by these models through a series of case studies, and establish how existing ethics frameworks can be adapted to meet these new challenges. With the techniques of emotion mining being used increasingly for predictions of self-harm, suicide and homicide this review comes at a crucial time.

METHODS: The article is presented as a narrative review and therefore does not detail the methodology of a systematic literature review. Instead, we offer a high level overview of AI in mental health and explore a number of case studies which highlight the novel ethical challenges these tools present.

RESULTS and Conclusion: Despite the growth of these technologies, there has not been a parallel growth in the ethical debate. For digital health to be implemented in an equitable manner, clinicians must be ethically equipped to appraise these systems. In the final section of our review we provide practical recommendations for healthcare professionals who are faced with implementing these new systems. © 2020

Language: en

Keywords

Psychology; human; mental health; homicide; suicide; Ethics; Review; Psychiatry; Mental health; Social determinants; Public health; clinical practice; informed consent; automutilation; priority journal; emotion; methodology; confidentiality; health care personnel; justice; Medical ethics; patient autonomy; Health inequalities; mining; beneficence; social determinants of health; Artificial intelligence; study; Health equity; Digital medicine