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Ethical integration of patient-reported outcomes and digital biomarkers in AI healthcare models

2026Artigo científico Acesso aberto
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dc.contributor.authorSeringa, Joana
dc.contributor.authorCordeiro, João V.
dc.contributor.authorSantana, Rui
dc.contributor.authorMagalhães, Teresa
dc.contributor.institutionCentro de Investigação em Saúde Pública (CISP/PHRC)
dc.contributor.institutionComprehensive Health Research Centre (CHRC) - Pólo ENSP
dc.contributor.institutionEscola Nacional de Saúde Pública (ENSP)
dc.contributor.pblFrontiers Media
dc.date.accessioned2026-04-27T15:03:01Z
dc.date.available2026-04-27T15:03:01Z
dc.date.issued2026
dc.descriptionPublisher Copyright: 2026 Seringa, Cordeiro, Santana and Magalhães.
dc.description.abstractBackground: Alongside expected benefits, several ethical concerns arise from Artificial Intelligence (AI) based models. From the design to the implementation and subsequent evaluation, it is crucial to map potential ethical concerns regarding the use of AI models in healthcare. Patient-Reported Outcomes (PROs) and Digital Biomarkers (DBs) are being increasingly collected to improve patient-centered healthcare systems. However, due to the sensitive nature of this data, its processing into AI models may raise ethical concerns that should be considered. While general AI ethics frameworks exist, no expert consensus has specifically addressed the unique ethical challenges of integrating PROs and DBs in AI healthcare models. Objective: This study aims to address this gap by establishing expert consensus on ethical, legal, and social considerations for integrating PROs and DBs into AI-driven healthcare models. Methods: A mixed-method study was performed. Phase 1 consisted of a narrative review to map the ethical landscape and generate an initial pool of recommendations. Phase 2 involved a two-round modified e-Delphi survey to validate and refine these recommendations among a multidisciplinary panel of experts (n = 27). The panel included experts in AI, bioethics, clinical research, and data protection, primarily from Southern Europe. Results: The findings of the two complementary components of this study (narrative review and modified e-Delphi study) were organized around five core ethical principles: autonomy, beneficence, non-maleficence, justice, and transparency and accountability. The modified e-Delphi study achieved high consensus (≥80%) on 55 specific recommendations across these principles. Key recommendations included implementing dynamic consent models, establishing continuous model validation protocols, conducting regular impact assessments, ensuring diverse stakeholder engagement to mitigate biases, and maintaining human oversight within AI systems. Conclusion: This study provides the first comprehensive expert-validated ethical framework specifically designed for PROs and DBs integration in AI healthcare models, filling a gap in the literature that has primarily focused on general AI ethics rather than the unique challenges posed by patient-generated health data.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent842207
dc.identifier.doi10.3389/fdgth.2026.1781497
dc.identifier.issn2673-253X
dc.identifier.otherPURE: 161465257
dc.identifier.otherPURE UUID: 7eda7fa0-f67b-414a-8871-6cf0ab5b5c26
dc.identifier.otherScopus: 105034440357
dc.identifier.otherPubMed: 41884556
dc.identifier.otherPubMedCentral: PMC13008942
dc.identifier.otherWOS: 001721636900001
dc.identifier.otherORCID: /0000-0003-3794-1659/work/213040121
dc.identifier.urihttp://hdl.handle.net/10362/202601
dc.identifier.urlhttps://www.scopus.com/pages/publications/105034440357
dc.language.isoeng
dc.peerreviewedyes
dc.subjectartificial intelligence
dc.subjectdigital biomarkers
dc.subjecte-Delphi study
dc.subjectethics
dc.subjectnarrative review
dc.subjectpatient-generated data
dc.subjectpatient-reported outcomes
dc.subjectMedicine (miscellaneous)
dc.subjectBiomedical Engineering
dc.subjectHealth Informatics
dc.subjectComputer Science Applications
dc.subjectSDG 3 - Good Health and Well-being
dc.titleEthical integration of patient-reported outcomes and digital biomarkers in AI healthcare modelsen
dc.title.subtitlean expert consensus frameworken
dc.typejournal article
degois.publication.titleFrontiers in Digital Health
degois.publication.volume8
dspace.entity.typePublication
rcaap.rightsopenAccess

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