ArticleThe American journal of bioethics : AJOB2020
Identifying Ethical Considerations for Machine Learning Healthcare Applications.
Article in The American journal of bioethics : AJOB, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers, 3 of them syntheses that pooled it.
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
112 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Ethical concerns and strategies for implementing artificial intelligence in healthcare: a review of empirical studies.BMC medical ethics · 2026Pooled it
- Non-empirical methods for ethics research on digital technologies in medicine, health care and public health: a systematic journal review.Medicine, health care, and philosophy · 2024Pooled it
- Machine Learning in Medical Emergencies: a Systematic Review and Analysis.Journal of medical systems · 2021Pooled it
- Proactive vs. passive algorithmic ethics practices in healthcare: the moderating role of healthcare engagement type in patients' responses.BMC medical ethics · 2025Trial
- Navigating Healthcare AI Governance: the Comprehensive Algorithmic Oversight and Stewardship Framework for Risk and Equity.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- Pediatrics 4.0: the Transformative Impacts of the Latest Industrial Revolution on Pediatrics.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- Artificial Intelligence Applications in Mental Health: A Systematic Review of Clinical Practice, Educational Transformation, and Ethical Governance.Healthcare (Basel, Switzerland) · 2026Review
- The Continuity Trap in Data Science Health Research.Journal of medical Internet research · 2026Article
- Why do AI projects fail in drug development and pharma? Insights from multi-year Pistoia Alliance studies.Journal of computer-aided molecular design · 2026Article
- Ethical and Governance Challenges of AI in Medical Imaging and Diagnostics: A Systematic Survey and Policy Framework Recommendations.Healthcare (Basel, Switzerland) · 2026Review
- Operationalizing Digital Health Equity in Artificial Intelligence-Enabled Patient Decision Aids for Older Adults: Mixed Methods Study.Journal of medical Internet research · 2026Article
- The Use of Deep Learning in RNA Therapeutic Development.ACS nano · 2026Review
- Governance for safe and responsible AI in healthcare organisations: a scoping review of frameworks.NPJ digital medicine · 2026Article
- Article
- The ethical challenge of AI in medicine: Who owns the mistakes?Journal of public health research · 2026Article
- A proposed ethical framework for informed consent in elective robotic telesurgery.Journal of robotic surgery · 2026Article
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- A multi-branch ensemble learning framework for detection of non-small cell lung cancer via T-cell receptor sequencing.BMC cancer · 2026Article
- Predicting non-emergency healthcare use in Australia using machine learning on longitudinal household data.Scientific reports · 2026Article
- Self-driving bioprinting laboratories.Biofabrication · 2026Review
52 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
Along with potential benefits to healthcare delivery, machine learning healthcare applications (ML-HCAs) raise a number of ethical concerns. Ethical evaluations of ML-HCAs will need to structure the overall problem of evaluating these technologies, especially for a diverse group of stakeholders. This paper outlines a systematic approach to identifying ML-HCA ethical concerns, starting with a conceptual model of the pipeline of the conception, development, implementation of ML-HCAs, and the parallel pipeline of evaluation and oversight tasks at each stage. Over this model, we layer key questions that raise value-based issues, along with ethical considerations identified in large part by a literature review, but also identifying some ethical considerations that have yet to receive attention. This pipeline model framework will be useful for systematic ethical appraisals of ML-HCA from development through implementation, and for interdisciplinary collaboration of diverse stakeholders that will be required to understand and subsequently manage the ethical implications of ML-HCAs.
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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.