Acknowledgment Is Not Enforcement: Closing Commercial AI Recruitment's Transparency Gap.
This article critically examines the ethical framework for AI-enabled clinical trial recruitment proposed by Rentzepis et al., highlighting a key structural vulnerability related to the lack of enforceable transparency for commercial AI recruitment tools. It emphasizes that while Rentzepis et al. id...
| Publicado en: | American Journal of Bioethics Vol. 26; no. 8; pp. 67 - 70 |
|---|---|
| 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=195895390&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195895390 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: 195895390 195895390 195895390 10.1080/15265161.2026.2690948 195895390 ppf: 67 ppct: 3 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Acknowledgment Is Not Enforcement: Closing Commercial AI Recruitment's Transparency Gap. aug: au: Ahiafor, Maxwell Zhong, Wang Zhou, Boda affil: Tsinghua University sug: subj: Artificial Intelligence Ethical Issues Patient Selection Ethical Issues Clinical Trials Research Ethics Research Subject Recruitment Ethical Issues Audit Consent Data Security Health Services Accessibility Electronic Health Records Algorithms Quality Assurance ab: This article critically examines the ethical framework for AI-enabled clinical trial recruitment proposed by Rentzepis et al., highlighting a key structural vulnerability related to the lack of enforceable transparency for commercial AI recruitment tools. It emphasizes that while Rentzepis et al. identify transparency challenges and recommend academic reporting standards, they do not specify mechanisms to ensure commercial vendors disclose algorithmic details, which are often protected as trade secrets. The authors propose a concrete solution: conditional regulatory approval requiring post-market, third-party audits of recruitment algorithms to assess model architecture, demographic performance, stability, and agreement with human eligibility determinations. This approach aims to uphold ethical principles of respect, beneficence, nonmaleficence, and justice by enabling verification of AI tools used in regulated trials, addressing bias, and ensuring equitable participant selection. pubtype: Academic Journal doctype: commentary Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|