Machine Learning Applications for Brain Disorders: Towards Inclusivity and Equity

KongFatt Wong-Lin; Paula McClean; Daniela Tropea

doi:10.1007/978-981-95-2070-1_9

Machine Learning Applications for Brain Disorders: Towards Inclusivity and Equity

KongFatt Wong-Lin
, Paula McClean
, Daniela Tropea

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Brain disorders are generally complex and are associated with many challenges, including inclusivity and equity issues. Here, we discuss some of the challenges, and show, with dementia as case study, that machine learning, especially in its more practical manifestations, can offer more standardised, consistent, objective, inclusive, equitable, and efficient assessments and decision-making.

Original language	English
Title of host publication	Sex, Gender, and Emerging Technology in Healthcare: Mitigating Bias and Fostering Equity
Subtitle of host publication	From Biology to Care: Sex and Gender Impacts on Health and Medicine
Editors	Heisook Lee, Nayoung Kim, Sabine Oertelt-Prigione
Place of Publication	Singapore
Publisher	Springer Nature, Singapore
Chapter	9
Pages	181-196
Number of pages	17
Edition	1
ISBN (Electronic)	978-981-95-2070-1
ISBN (Print)	978-981-95-2069-5
DOIs	https://doi.org/10.1007/978-981-95-2070-1_9
Publication status	Published online - 1 Feb 2026

Funding

European Union’s INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB; Centre for Personalised Medicine, IVA 5036), Northern Ireland Functional Brain Mapping Project Facility (1303/101154803) and Invest Northern Ireland, Alzheimer’s Research UK (ARUK) NI Pump Priming, Ulster University Research Challenge Fund, IRSF (3507- 207417 grant), Meath Foundation Research award 2019, Science Foundation Ireland (SFI) under Grant Number 16/RC/3948 and co- funded under the European Regional Development Fund and by FutureNeuro industry partners, and Trinity College Dublin School of Medicine Seed Award

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 5 Gender Equality
SDG 9 Industry, Innovation, and Infrastructure
SDG 10 Reduced Inequalities

Keywords

dementia
Alzheimer's disease
artificial intelligence (AI)
practical machine learning
decision support system

Access to Document

10.1007/978-981-95-2070-1_9

Cite this

Wong-Lin, K., McClean, P., & Tropea, D. (2026). Machine Learning Applications for Brain Disorders: Towards Inclusivity and Equity. In H. Lee, N. Kim, & S. Oertelt-Prigione (Eds.), Sex, Gender, and Emerging Technology in Healthcare: Mitigating Bias and Fostering Equity: From Biology to Care: Sex and Gender Impacts on Health and Medicine (1 ed., pp. 181-196). Springer Nature, Singapore. Advance online publication. https://doi.org/10.1007/978-981-95-2070-1_9

Wong-Lin, KongFatt ; McClean, Paula ; Tropea, Daniela. / Machine Learning Applications for Brain Disorders: Towards Inclusivity and Equity. Sex, Gender, and Emerging Technology in Healthcare: Mitigating Bias and Fostering Equity: From Biology to Care: Sex and Gender Impacts on Health and Medicine. editor / Heisook Lee ; Nayoung Kim ; Sabine Oertelt-Prigione. 1. ed. Singapore : Springer Nature, Singapore, 2026. pp. 181-196

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Wong-Lin, K , McClean, P & Tropea, D 2026, Machine Learning Applications for Brain Disorders: Towards Inclusivity and Equity. in H Lee, N Kim & S Oertelt-Prigione (eds), Sex, Gender, and Emerging Technology in Healthcare: Mitigating Bias and Fostering Equity: From Biology to Care: Sex and Gender Impacts on Health and Medicine. 1 edn, Springer Nature, Singapore, Singapore, pp. 181-196. https://doi.org/10.1007/978-981-95-2070-1_9

Machine Learning Applications for Brain Disorders: Towards Inclusivity and Equity. / Wong-Lin, KongFatt ; McClean, Paula; Tropea, Daniela.
Sex, Gender, and Emerging Technology in Healthcare: Mitigating Bias and Fostering Equity: From Biology to Care: Sex and Gender Impacts on Health and Medicine. ed. / Heisook Lee; Nayoung Kim; Sabine Oertelt-Prigione. 1. ed. Singapore: Springer Nature, Singapore, 2026. p. 181-196.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AB - Brain disorders are generally complex and are associated with many challenges, including inclusivity and equity issues. Here, we discuss some of the challenges, and show, with dementia as case study, that machine learning, especially in its more practical manifestations, can offer more standardised, consistent, objective, inclusive, equitable, and efficient assessments and decision-making.

KW - dementia

KW - Alzheimer's disease

KW - artificial intelligence (AI)

KW - practical machine learning

KW - decision support system

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Wong-Lin K , McClean P, Tropea D. Machine Learning Applications for Brain Disorders: Towards Inclusivity and Equity. In Lee H, Kim N, Oertelt-Prigione S, editors, Sex, Gender, and Emerging Technology in Healthcare: Mitigating Bias and Fostering Equity: From Biology to Care: Sex and Gender Impacts on Health and Medicine. 1 ed. Singapore: Springer Nature, Singapore. 2026. p. 181-196 Epub 2026 Feb 1. doi: 10.1007/978-981-95-2070-1_9

Machine Learning Applications for Brain Disorders: Towards Inclusivity and Equity

Abstract

Funding

UN SDGs

Keywords

Access to Document

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