Ethical Ramifications of Utilizing AI-Driven Facial Recognition Technology in Pain Assessment of Nonverbal Patients.
Accurate and appropriate pain assessment is crucial for effective pain treatment and management. When patients who are nonverbal cannot communicate, observational pain assessment carried out by health providers is riddled with issues of bias and subjectivity. Artificial intelligence-driven facial re...
| Publicado en: | Journal of Hospital Ethics Vol. 10; no. 1; pp. 12 - 18 |
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| Autor principal: | |
| Formato: | Journal Article |
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MedStar Washington Hospital Center, Center for Ethics
May2024
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| 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=177823737&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177823737 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19384955 FNKI jtl: Journal of Hospital Ethics issn: 19384955 maglogo: N pubinfo: dt: May2024 vid: 10 iid: 1 pid: 82867 pub: MedStar Washington Hospital Center, Center for Ethics place: Washington, District of Columbia artinfo: ui: 177823737 177823737 ppf: 12 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Ethical Ramifications of Utilizing AI-Driven Facial Recognition Technology in Pain Assessment of Nonverbal Patients. aug: au: Dyachim, Joshua sug: subj: Artificial Intelligence Utilization Artificial Intelligence Ethical Issues Biometrics Ethical Issues Pain Measurement Ethical Issues Nonverbal Communication Bioethics Health Personnel Quality of Health Care Facial Expression Ethics, Medical Automation Self Report ab: Accurate and appropriate pain assessment is crucial for effective pain treatment and management. When patients who are nonverbal cannot communicate, observational pain assessment carried out by health providers is riddled with issues of bias and subjectivity. Artificial intelligence-driven facial recognition technologies (FRTs) are proposed as the solution to the challenges prevalent in observational pain assessment of nonverbal patients by care providers. It promises to streamline, objectify, and promote accuracy to mitigate inaccurate pain assessment in nonverbal patients. Inaccurate pain assessment could lead to undertreatment or overtreatment of pain in nonverbal patients, with far-reaching and unintended consequences. This paper argues that close ethical scrutiny is pertinent for the deployment of AI-driven FRT for pain assessment to mitigate bias and unequal outcomes in nonverbal patients. A concerted effort by medical professionals is expedient to assure the integration of transparent, explainable, and robust data-driven FRT for pain assessment in the clinical setting to promote quality care and equitable outcomes in nonverbal patients. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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