Developing AI algorithms to analyse genomic data for disease diagnosis, personalised medicine and genome editing

The advent of artificial intelligence (AI) has ushered in a transformative era in genomic medicine, enabling the analysis of vast and complex genomic datasets for disease diagnosis, personalized medicine and genome editing. This paper explores the development and application of AI algorithms—spanning machine learning, deep learning and generative models—in interpreting genomic sequences, classifying variants, predicting phenotypes and guiding precision therapies. We review the foundational technologies, map current methodologies and present a hypothetical dataset illustrating algorithmic workflow and outcomes. The results highlight improvements in diagnostic yield, stratification for personalized treatment and identification of editing targets, while also outlining persistent challenges such as data bias, interpretability, regulatory hurdles and ethical concerns. The discussion underscores how AI-driven genomics is transitioning from research to clinical utility, and identifies future perspectives including multimodal data integration, real-time genome editing feedback loops and equitable deployment across populations. In conclusion, while significant barriers remain, the synergy of AI and genomics offers unprecedented promise for earlier diagnosis, tailored treatments and refined genome editing applications—if guided by robust methodology, transparency and ethical frameworks.