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Updated: Jul 3, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Operationalizing Digital Health Equity in Artificial Intelligence-Enabled Patient Decision Aids for Older Adults:

Cindy Yue Tian^1,2, Xiaochen Yang¹, Kailu Wang^1,2

¹JC School of Public Health and Primary Care, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China (Hong Kong).

Journal of Medical Internet Research

|June 29, 2026

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Summary

This study offers practical strategies for designing equitable artificial intelligence-enabled patient decision aids (AI-PDAs) for older adults with chronic diseases. It integrates user needs and evidence to ensure AI tools support, rather than hinder, equitable care participation.

Area of Science:

Digital Health
Health Equity
Artificial Intelligence in Healthcare

Background:

Artificial intelligence-enabled patient decision aids (AI-PDAs) show potential for older adults with chronic conditions.
Ensuring equity in AI-PDA design requires understanding complex health and digital contexts.
The Digital Health Equity Framework (DHEF) offers a conceptual basis, but practical application strategies are needed.

Purpose of the Study:

To identify equity determinants for AI-PDAs tailored to older adults.
To develop actionable design strategies for applying the DHEF to AI-PDAs.
To bridge the gap between conceptual frameworks and practical AI-PDA development.

Main Methods:

A mixed-methods approach combining semistructured interviews with older adults, healthcare providers, and students.

Keywords:

artificial intelligence co-design digital health equity older adult care patient decision aids umbrella review

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Last Updated: Jul 3, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

An umbrella review synthesizing evidence on addressing equity determinants in digital health tools.

Integration of interview and review findings through iterative mapping and expert consultation.

Main Results:

Identified equity determinants across individual, interpersonal, community, and societal levels, including healthcare and digital environments.
Highlighted cross-level concerns regarding algorithmic fairness in AI-PDAs.
Generated five key recommendations for equitable AI-PDA development, focusing on co-design, relationship-centered approaches, community resources, AI governance, and algorithmic fairness.

Conclusions:

The study operationalizes the DHEF into a practical approach for AI-PDAs for older adults with chronic diseases.
Healthcare settings are critical sociotechnical contexts influencing equitable care participation via AI tools.
Emphasizes the need for interdisciplinary collaboration to align AI innovation with equity-oriented design principles.