ArticleHarvard public health review (Cambridge, Mass.)2024
Bioethics Principles in Machine Learning-Healthcare Application Design: Achieving Health Justice and Health Equity.
Article in Harvard public health review (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- A Community-in-the-Loop Approach to Smart Home Monitoring for Aging in Place: Mixed Methods Evaluation of a Co-Designed Prototype.JMIR aging · 2026Article
- The Design-Driven Innovation Path of Human-Centered Artificial Intelligence in the Field of Healthcare: Theory, Practice, and Future Prospects.Healthcare (Basel, Switzerland) · 2026Review
- Detecting Older Adults' Behavior Changes During Adverse External Events Using Ambient Sensing: Longitudinal Observational Study.JMIR nursing · 2025Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Health technologies featuring artificial intelligence (AI) are becoming more common. Some healthcare AIs are exhibiting bias towards underrepresented persons and populations. Although many computer scientists and healthcare professionals agree that eliminating or mitigating bias in healthcare AIs is needed, little information exists regarding how to operationalize bioethics principles like autonomy in product design and implementation. This short course is framed with a Social Determinants of Health lens and a health justice and health equity stance to support computer scientists and healthcare professionals in building and deploying ethical healthcare AI. In this short course we introduce the bioethics principle of autonomy in the context of human-centered design (Module 1) and share options for design thinking models, suggesting four activities to embed ethics principles during design (Module 2). We then discuss the importance of gaining the perspectives of diverse groups to minimize harm and support the fundamental human values of underrepresented persons in support of health equity and health justice ideals (Module 3).
Identifiers
What OpenQuestion holds
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.