The Evolving Role of Artificial Intelligence in Medical Genetics: Advancing Healthcare, Research, and Biosafety Management
Clinical Snapshot
PICO Framework
| P — Population | Not applicable - this is a narrative review without defined population |
| I — Intervention | Not applicable - narrative review format |
| C — Comparator | Not applicable - narrative review format |
| O — Outcomes | Not applicable - narrative review examining AI applications in medical genetics |
Bottom Line
This narrative review provides a broad overview of artificial intelligence applications in medical genetics, covering diagnostic tools, therapeutic development, and biosecurity considerations. While comprehensive in scope, it lacks the systematic methodology required for evidence-based practice recommendations. The review highlights important ethical and regulatory challenges but provides insufficient quantitative evidence synthesis to guide clinical decision-making. Healthcare professionals should view this as an educational overview rather than a systematic evidence base for practice change. The discussion of governance frameworks and future directions may inform policy development, but specific clinical applications require evaluation through systematic reviews and primary research studies with appropriate methodological rigor.
Key Findings
P Value: Not applicable - narrative review
Effect Size: Not applicable - narrative review
Primary Outcome: Overview of AI applications in medical genetics across diagnostic, therapeutic, and research domains
Nnt Or Sensitivity: Not applicable - no quantitative synthesis provided
Confidence Interval: Not applicable - narrative review
Clinical Application
Limited by lack of systematic evidence synthesis and specific implementation guidance Relevant to Australian genomics initiatives and precision medicine programs, though no specific Australian data or guidelines referenced Healthcare professionals working in medical genetics, genomics, and precision medicine
Abstract
The integration of artificial intelligence (AI) with medical genetics is transforming healthcare by addressing the analytical challenges posed by the vast complexity of multi-omics data. This review explores the synergistic convergence of these fields, highlighting AI's transformative role in enhancing diagnostic precision, enabling non-invasive molecular profiling through imaging-genetics, and advancing predictive and personalized medicine via polygenic risk scores and pharmacogenomics. AI is also emerging as a powerful generative tool in therapeutic design, accelerating drug discovery, protein engineering, and precision gene editing. However, this powerful synergy introduces significant ethical, regulatory, and biosecurity challenges, including data privacy, algorithmic bias, and the dual-use risks of AI-enabled genetic engineering. The future envisions a responsible co-evolution, with multimodal AI and the concept of the Digital Twin driving precision medicine, underpinned by interdisciplinary collaboration to ensure fairness, transparency, and societal trust. This article charts the current landscape and proposes actionable directions, emphasizing the need for robust governance to harness AI's potential while mitigating its risks for the benefit of human health.
References
- 1.Wu, Y. C., Tuo, N., Shi, G., Li, K., Song, Z., & Li, Y. (2025). The evolving role of artificial intelligence in medical genetics: Advancing healthcare, research, and biosafety management. Genes, 16(1). https://doi.org/10.1136/bmj-2024-081554
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