ArticleBMC medical ethics2023
Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care.
Article in BMC medical ethics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Proactive vs. passive algorithmic ethics practices in healthcare: the moderating role of healthcare engagement type in patients' responses.BMC medical ethics · 2025Trial
- Exploring young adult cancer survivors' perspectives on generative AI chatbots for symptom support.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Generative AI Engagement and Perceived Nutrition-Oriented Feeding Practices Among Urban Indonesian Mothers: Mixed Methods Study.JMIR pediatrics and parenting · 2026Article
- Trusting Generative AI for Health Advice: Preregistered Survey Experiment.Journal of medical Internet research · 2026Article
- Machine learning-based fetal health prediction and development of smart web application.Frontiers in artificial intelligence · 2026Article
- Compliance with Clinical Guidelines and AI-Based Clinical Decision Support Systems: Implications for Ethics and Trust.Science and engineering ethics · 2025Article
- Beyond Cancer Detection: An AI Framework for Multidimensional Risk Profiling on Contrast-Enhanced Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- Advocating for trust in and trustworthy AI to transform evidence synthesis.Environmental evidence · 2025Article
- Navigating promise and perils: applying artificial intelligence to the perinatal mental health care cascade.npj health systems · 2025Review
- Knowledge, Attitudes, and Perceptions of Chronic Patients in Saudi Arabia Regarding the Use of Artificial Intelligence to Improve Medication Adherence.Patient preference and adherence · 2025Article
- Article
- Public perceptions of artificial intelligence in healthcare: ethical concerns and opportunities for patient-centered care.BMC medical ethics · 2024Article
- Implementing AI in healthcare-the relevance of trust: a scoping review.Frontiers in health services · 2023Article
- Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundDespite the recognition that developing artificial intelligence (AI) that is trustworthy is necessary for public acceptability and the successful implementation of AI in healthcare contexts, perspectives from key stakeholders are often absent from discourse on the ethical design, development, and deployment of AI. This study explores the perspectives of birth parents and mothers on the introduction of AI-based cardiotocography (CTG) in the context of intrapartum care, focusing on issues pertaining to trust and trustworthiness.
methodsSeventeen semi-structured interviews were conducted with birth parents and mothers based on a speculative case study. Interviewees were based in England and were pregnant and/or had given birth in the last two years. Thematic analysis was used to analyze transcribed interviews with the use of NVivo. Major recurring themes acted as the basis for identifying the values most important to this population group for evaluating the trustworthiness of AI.
resultsThree themes pertaining to the perceived trustworthiness of AI emerged from interviews: (1) trustworthy AI-developing institutions, (2) trustworthy data from which AI is built, and (3) trustworthy decisions made with the assistance of AI. We found that birth parents and mothers trusted public institutions over private companies to develop AI, that they evaluated the trustworthiness of data by how representative it is of all population groups, and that they perceived trustworthy decisions as being mediated by humans even when supported by AI.
conclusionsThe ethical values that underscore birth parents and mothers' perceptions of trustworthy AI include fairness and reliability, as well as practices like patient-centered care, the promotion of publicly funded healthcare, holistic care, and personalized medicine. Ultimately, these are also the ethical values that people want to protect in the healthcare system. Therefore, trustworthy AI is best understood not as a list of design features but in relation to how it undermines or promotes the ethical values that matter most to its end users. An ethical commitment to these values when creating AI in healthcare contexts opens up new challenges and possibilities for the design and deployment of AI.
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.