Ethical Considerations and Algorithmic Bias in AI-Driven Genetic Diagnosis: A Systematic Review
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Abstract
The integration of artificial intelligence (AI) into genetic diagnosis has enabled the rapid analysis of complex genomic data, thereby transforming clinical decision-making. However, because many AI models are trained on datasets predominantly derived from populations of European ancestry significant ethical concerns related to algorithmic bias have emerged. This systematic review aims to: (1) identify the major ethical issues and impacts of algorithmic bias in AI-driven genetic diagnosis, (2) examine existing ethical frameworks and proposed mitigation strategies and (3) outline critical success factors for ensuring fairness, accountability and transparency. A systematic literature search was conducted in accordance with PRISMA guidelines across PubMed, Scopus, Web of Science, IEEE Xplore and the ACM Digital Library. Search terms related to algorithmic bias, genetic diagnosis, artificial intelligence, and ethics were combined. Study selection, data extraction and thematic synthesis were performed. Following screening and eligibility assessment, 46 peer-reviewed studies published between 2014 and 2025 were included for qualitative synthesis.
The findings indicate that algorithmic bias poses a substantial risk to health equity, particularly through reduced diagnostic accuracy and higher rates of variants of uncertain significance among individuals from
underrepresented populations. Additional ethical challenges related to transparency, accountability, informed consent and patient autonomy were identified. Four critical success factors emerged from the synthesis: robust
regulatory and accountability frameworks, meaningful multi-stakeholder engagement, mandatory transparency and explainability and equity-centered lifecycle management. Addressing algorithmic bias in AI-driven genetic diagnosis is essential to achieving ethical and equitable
precision medicine. The implementation of the identified critical success factors is necessary to ensure that AI applications in genomics benefit all populations fairly.
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