ArticlePhilosophy & technology2021
Ethical Issues in Democratizing Digital Phenotypes and Machine Learning in the Next Generation of Digital Health Technologies.
Article in Philosophy & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
18 citing papers in PubMed.
- Towards Human-Centered Digital Health Interventions.The Psychiatric clinics of North America · 2026Review
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- Rethinking Ethics for an Era of Trusted Computational Tools.The Psychiatric clinics of North America · 2026Review
- Stakeholder Perspectives on Humanistic Implementation of Computer Perception in Health Care: Qualitative Study.JMIR mental health · 2026Article
- Article
- Developing Digital Mental Health Tools With Culturally Diverse Parents and Young People: Qualitative User-Centered Design Study.JMIR pediatrics and parenting · 2025Article
- Tracing Tomorrow: young people's preferences and values related to use of personal sensing to predict mental health, using a digital game methodology.BMJ mental health · 2024Article
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Returning Individual Research Results from Digital Phenotyping in Psychiatry.The American journal of bioethics : AJOB · 2024Article
- Ethical considerations for integrating multimodal computer perception and neurotechnology.Frontiers in human neuroscience · 2024Article
- Digital transformation of mental health services.Npj mental health research · 2023Review
- Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review.Sensors (Basel, Switzerland) · 2022Review
- "Democratizing" artificial intelligence in medicine and healthcare: Mapping the uses of an elusive term.Frontiers in genetics · 2022Article
- Centering Lived Experience in Developing Digital Interventions for Suicide and Self-injurious Behaviors: User-Centered Design Approach.JMIR mental health · 2021Article
- Article
- The Sociotechnical Ethics of Digital Health: A Critique and Extension of Approaches From Bioethics.Frontiers in digital health · 2021Article
- Digital Phenotyping: an Epistemic and Methodological Analysis.Philosophy & technology · 2021Article
- Smartphone use and personality: Their effects on sleep quality across groups using mediation analysis.Digital healthArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital phenotyping is the term given to the capturing and use of user log data from health and wellbeing technologies used in apps and cloud-based services. This paper explores ethical issues in making use of digital phenotype data in the arena of digital health interventions. Products and services based on digital wellbeing technologies typically include mobile device apps as well as browser-based apps to a lesser extent, and can include telephony-based services, text-based chatbots, and voice-activated chatbots. Many of these digital products and services are simultaneously available across many channels in order to maximize availability for users. Digital wellbeing technologies offer useful methods for real-time data capture of the interactions of users with the products and services. It is possible to design what data are recorded, how and where it may be stored, and, crucially, how it can be analyzed to reveal individual or collective usage patterns. The paper also examines digital phenotyping workflows, before enumerating the ethical concerns pertaining to different types of digital phenotype data, highlighting ethical considerations for collection, storage, and use of the data. A case study of a digital health app is used to illustrate the ethical issues. The case study explores the issues from a perspective of data prospecting and subsequent machine learning. The ethical use of machine learning and artificial intelligence on digital phenotype data and the broader issues in democratizing machine learning and artificial intelligence for digital phenotype data are then explored in detail.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.