Asian Journal of Research in Computer Science

Background: Rare genetic diseases affect over 300 million individuals worldwide and are often marked by prolonged diagnostic delays and scarce treatment options. The integration of artificial intelligence (AI) and cloud computing presents transformative potential for accelerating therapeutic molecule design and early diagnosis. However, this progress also raises critical concerns about data privacy, ethical use, and cybersecurity in handling sensitive genomic information.

Objective: This systematic review explores the current landscape of AI-driven therapeutic molecule design for rare genetic diseases and evaluates the role and effectiveness of integrated cloud-based cybersecurity frameworks in protecting healthcare data.

Methods: A systematic search was conducted across PubMed, Scopus, IEEE Xplore, SpringerLink, and Web of Science for peer-reviewed articles published between 2015 and 2025. Search terms included “AI in rare genetic diseases,” “therapeutic molecule design,” “cloud cybersecurity,” and “genomic data protection.” The review followed PRISMA guidelines, initially identifying 1,008 articles. After screening for relevance, duplicates, and applying inclusion/exclusion criteria, 208 articles were selected for final analysis.

Results: Key AI techniques identified include deep learning for phenotype-genotype mapping, generative adversarial networks (GANs) for molecule generation, natural language processing (NLP) for mining biomedical literature, and federated learning for decentralized, privacy-preserving model training. Facial recognition tools such as Face2Gene demonstrated higher accuracy than clinical assessments in diagnosing genetic syndromes from 2D/3D images. However, these AI models remain vulnerable to adversarial attacks, model inversion, and data poisoning. Ethical concerns such as informed consent, algorithmic bias, data ownership, and compliance with privacy regulations like GDPR were frequently highlighted. The review also noted a surge in interdisciplinary publications, with over 230,000 related studies emerging in 2025 alone.

Conclusions: AI-enabled solutions hold strong potential to revolutionize the diagnosis and treatment of rare genetic diseases through precision medicine. Yet, their integration into clinical practice demands robust cloud cybersecurity frameworks employing differential privacy, homomorphic encryption, and adversarial training to ensure data integrity and patient trust. Ethical governance must guide the responsible deployment of these technologies to ensure equity, transparency, and privacy in the era of AI-driven healthcare.