Gene and cell therapy is entering a new phase, one where artificial intelligence is bringing precision and predictability to editing systems that were once largely trial-and-error. From CRISPR-Cas genome editing to emerging techniques like epigenome modulation and mitochondrial genome correction, machine learning and deep learning are increasingly guiding how these tools are designed and scaled. AI is helping minimize off-target effects, improve manufacturing predictability, and build in the kind of explainability and bias mitigation that safety and regulatory approval demand. As these technologies move closer to widespread clinical use, the field needs a framework that ties computational innovation directly to therapeutic application, rather than treating AI and biomedicine as separate conversations. This convergence of computational innovation and biological engineering marks one of the most consequential shifts in modern therapeutic science. AI-Driven Innovations in Genome, Epigenome, and Mitochondrial Therapies provides state-of-the-art, data-driven methodologies for designing, optimizing, and validating safer, more precise, and scalable gene and cell therapies. By connecting concrete biomedical applications with benchmarked computational pipelines and structured frameworks for transparent, accountable AI governance, this book equips researchers and practitioners to navigate discovery, preclinical validation, clinical translation, and lifecycle risk management. Covering topics such as genome-enabled precision wound care, mitochondrial mutation detection, and mitochondrial editing, this book is an excellent academic resource for graduate and doctoral students, biomedical researchers, AI and machine learning specialists, bioinformaticians, regulatory affairs professionals, policymakers, and more.
