Ethical and Epistemic Considerations for the Use of Artificial Intelligence in Ultra-Rare Disease Clinical Trials.
This article examines the ethical and epistemic implications of using artificial intelligence (AI) in research on ultra-rare diseases, where patient populations are extremely small and conventional clinical trials are often infeasible. It highlights AI’s role as an infrastructure of visibility that...
| Publicado en: | American Journal of Bioethics Vol. 26; no. 8; pp. 37 - 40 |
|---|---|
| Autores principales: | , , |
| Formato: | commentary Journal Article |
| Publicado: |
Taylor & Francis Ltd
Aug2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195895385&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195895385 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15265161 FKZ jtl: American Journal of Bioethics issn: 15265161 maglogo: N pubinfo: dt: Aug2026 vid: 26 iid: 8 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 195895385 195895385 195895385 10.1080/15265161.2026.2690940 195895385 ppf: 37 ppct: 3 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Ethical and Epistemic Considerations for the Use of Artificial Intelligence in Ultra-Rare Disease Clinical Trials. aug: au: Owens, Kellie Kearns, Lisa Bateman-House, Alison affil: NYU Grossman School of Medicine sug: subj: Artificial Intelligence Ethical Issues Clinical Trials Ethical Issues Epistemology Rare Diseases Implementation Science Patient Selection United States ab: This article examines the ethical and epistemic implications of using artificial intelligence (AI) in research on ultra-rare diseases, where patient populations are extremely small and conventional clinical trials are often infeasible. It highlights AI’s role as an infrastructure of visibility that can identify eligible patients across fragmented data sources, potentially improving diagnosis and access to experimental therapies, but also risks reinforcing existing inequities due to uneven data representation. The article discusses AI-generated synthetic evidence, such as digital twins and synthetic control arms, as emerging tools for knowledge generation that raise questions about reliability and standards of evidence in ultra-rare disease research. It emphasizes the importance of patient and community involvement in governing AI use, advocating for transparency and stewardship to ensure equitable distribution of research opportunities and trust in AI-enabled systems. pubtype: Academic Journal doctype: commentary Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|