ArticleNEJM AI2024
Co-creating Consent for Data Use - AI-Powered Ethics for Biomedical AI.
Article in NEJM AI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis 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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-generated videos in medical education: systematic review.BMJ open quality · 2025Pooled it
- Safeguarding Neural Data.Neurology · 2026Review
- Review
- Progress in educational theory and translational implementation of simulation-based education for healthcare professionals: an integrative analysis from theory to effectiveness.Frontiers in medicine · 2026Review
- Artificial intelligence and physician burnout: A productivity paradox.Learning health systems · 2025Article
- Ethical Oversight and Social Licensing of Portable MRI Research.The Journal of law, medicine & ethics : a journal of the American Society of Law, Medicine & Ethics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
As nations design regulatory frameworks for medical AI, research and pilot projects are urgently needed to harness AI as a tool to enhance today's regulatory and ethical oversight processes. Under pressure to regulate AI, policy makers may think it expedient to repurpose existing regulatory institutions to tackle the novel challenges AI presents. However, the profusion of new AI applications in biomedicine - combined with the scope, scale, complexity, and pace of innovation - threatens to overwhelm human regulators, diminishing public trust and inviting backlash. This article explores the challenge of protecting privacy while ensuring access to large, inclusive data resources to fuel safe, effective, and equitable medical AI. Informed consent for data use, as conceived in the 1970s, seems dead, and it cannot ensure strong privacy protection in today's large-scale data environments. Informed consent has an ongoing role but must evolve to nurture privacy, equity, and trust. It is crucial to develop and test alternative solutions, including those using AI itself, to help human regulators oversee safe, ethical use of biomedical AI and give people a voice in co-creating privacy standards that might make them comfortable contributing their data. Biomedical AI demands AI-powered oversight processes that let ethicists and regulators hear directly and at scale from the public they are trying to protect. Nations are not yet investing in AI tools to enhance human oversight of AI. Without such investments, there is a rush toward a future in which AI assists everyone except regulators and bioethicists, leaving them behind.
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.