Ethical AI Frameworks for Regenerative Biotechnology Applications

Abstract

Artificial intelligence systems are increasingly central to regenerative biotechnology -- from genomic variant prioritisation and treatment response prediction to digital twin personalisation and automated protocol optimisation -- and their deployment raises ethical questions that the regenerative medicine research community has not yet systematically addressed. The EU AI Act (2024) classifies AI systems for clinical decision support as high-risk, imposing requirements for transparency, human oversight, robustness, and bias assessment that the regenerative biology AI ecosystem must satisfy. Yet the specific ethical challenges of regenerative biotechnology AI -- algorithmic bias in genomic models trained on predominantly European ancestries, consent implications of AI-driven treatment planning without complete explainability, equity of access to AI-enhanced regenerative therapies, and the responsibility framework when a digital twin-guided treatment fails -- have not been addressed by general biomedical AI ethics frameworks. This paper proposes the Regenerative Biotechnology AI Ethics (RBAIE) framework, a domain-specific ethical AI framework for regenerative biotechnology applications, comprising five ethical governance components: an algorithmic bias assessment module (ABAM) evaluating AI model performance disparities across demographic groups; a clinical AI transparency standard (CATS) specifying explainability requirements for AI-driven regenerative treatment decisions; a consent and autonomy protocol (CAP) for AI-enhanced regenerative therapy; an equity impact assessment tool (EIAT) evaluating the distributional equity of regenerative AI benefits; and a responsibility and liability framework (RLF) for AI-guided regenerative treatment decisions. RBAIE is applied to evaluate six AI systems from the regenerative biology ecosystem (RDCG, RMOAI, TRIA, PRDT, RPSO, CGTR-VQ) against RBAIE standards and the EU AI Act requirements. ABAM identifies ancestry bias in RDCG PRSC (AUC gap 0.084 between European and non-European ancestry subgroups). CATS reveals that four of six evaluated systems lack sufficient explainability for clinical decision support. EIAT identifies potential access equity gaps between high-income and low-income healthcare systems for five of six AI applications. RLF proposes a responsibility allocation framework that distributes liability across AI developers, clinical implementers, and regulatory bodies. The study contributes the RBAIE specification, an EU AI Act compliance assessment for six regenerative biology AI systems, and actionable recommendations for ethical deployment of AI in regenerative biotechnology.