Benefits, Implications and Ethical Concerns of Machine Learning Tools Serving Mental Health Purposes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In recent years, Healthcare and Mental Health have been in the spotlight for the creation of tools using Artificial Intelligence (AI). Mental Health technologies have proliferated in the last years, from social robotics to self-help tools and internet-based intervention programs. In particular, machine learning algorithms have been embedded into applications, for example, to provide a probability or classification regarding risk behaviours (e.g., suicide prevention tools). This paper reflects on the use of AI applied in mental healthcare tools, to assist clinicians in diagnosis, differential diagnosis and monitoring of patients with brain-related disorders. Benefits regarding the use of these technologies in mental healthcare will be discussed as well as some limitations and ethical concerns.

Original languageEnglish
Title of host publicationTechnological Innovation for Applied AI Systems - 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021, Proceedings
EditorsLuis M. Camarinha-Matos, Pedro Ferreira, Guilherme Brito
Place of PublicationCham
PublisherSpringer
Pages285-294
Number of pages10
ISBN (Electronic)978-3-030-78288-7
ISBN (Print)978-3-030-78287-0
DOIs
Publication statusPublished - 2021
Event12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021 - Virtual, Online
Duration: 7 Jul 20219 Jul 2021

Publication series

NameIFIP Advances in Information and Communication Technology
PublisherSpringer
Volume626
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Conference

Conference12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021
CityVirtual, Online
Period7/07/219/07/21

Keywords

  • Artificial Intelligence
  • Healthcare technologies
  • Machine Learning
  • Mental health

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