ArticleJournal of the American Medical Informatics Association : JAMIA2022
Developing machine learning-based models to help identify child abuse and neglect: key ethical challenges and recommended solutions.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 34 citations in OpenAlex.
- The Ethics of Leveraging Routinely Collected Patient Data for AI Development: Mixed Methods Study.Journal of medical Internet research · 2026Article
- Structural and functional atypicality in the temporal cortex are associated with auditory perception in maltreated children.Scientific reports · 2026Article
- Violent Legacies, New Threats: Protecting Black Motherhood in the Age of Artificial Womb Technology.Journal of bioethical inquiry · 2025Article
- Initial development of tools to identify child abuse and neglect in pediatric primary care.BMC medical informatics and decision making · 2023Article
- Artificial Intelligence and Child Abuse and Neglect: A Systematic Review.Children (Basel, Switzerland) · 2023Review
- The Role of Artificial Intelligence Model Documentation in Translational Science: Scoping Review.Interactive journal of medical research · 2023Article
- Black and Latinx Primary Caregiver Considerations for Developing and Implementing a Machine Learning-Based Model for Detecting Child Abuse and Neglect With Implications for Racial Bias Reduction: Qualitative Interview Study With Primary Caregivers.JMIR formative research · 2023Article
- Addressing Consequential Public Health Problems Through Informatics and Data Science.Journal of the American Medical Informatics Association : JAMIA · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Child abuse and neglect are public health issues impacting communities throughout the United States. The broad adoption of electronic health records (EHR) in health care supports the development of machine learning-based models to help identify child abuse and neglect. Employing EHR data for child abuse and neglect detection raises several critical ethical considerations. This article applied a phenomenological approach to discuss and provide recommendations for key ethical issues related to machine learning-based risk models development and evaluation: (1) biases in the data; (2) clinical documentation system design issues; (3) lack of centralized evidence base for child abuse and neglect; (4) lack of "gold standard "in assessment and diagnosis of child abuse and neglect; (5) challenges in evaluation of risk prediction performance; (6) challenges in testing predictive models in practice; and (7) challenges in presentation of machine learning-based prediction to clinicians and patients. We provide recommended solutions to each of the 7 ethical challenges and identify several areas for further policy and research.
Indexed as
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.