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This breakthrough enabled targeted manipulations of DNA sequences with unprecedented versatility and accuracy, revolutionizing genetic research and therapeutic development . These innovations enabled the sequencing of entire genomes within hours, significantly reducing costs and accelerating both research and clinical applications . AI will help with risk prediction, assessing genetic variants in disease, designing more efficient and precise therapies and drug targets, and patient-specific tailored therapies, specifically AI-driven matching of patients to specific therapies. Uttarwar predicted that new multimodal GenAI techniques will improve the ability to integrate multiomics data by combining genomics, transcriptomics, proteomics and epigenomics to examine disease mechanisms. All existing AI systems are domain-specific and thus can be classified as weak AI . Artificial intelligence thrives on data, processing and learning from datasets with a degree of accuracy and efficiency that traditional computing algorithms can not achieve. By bridging computational innovation and biological insight, this collection highlights the potential of AI-powered genomic medicine to empower research and clinical practice and to enable broad access. This approach not only enhances the efficiency of data analysis but also ensures that insights are accessible for research and clinical applications. AI, particularly through XAI, can play a pivotal role in predicting and understanding enhancer regions. The characterization and identification of these regions are vital in understanding human disease causation and aiding in the development of novel drugs. In genomics, XAI can help uncover the molecular principles underlying disease states by enhancing the understanding of genomic regions, particularly non-coding regulatory regions like enhancers. This requires collaboration between computational scientists and biologists to ensure that AI-generated insights are biologically meaningful and actionable. This precision medicine approach is becoming a cornerstone of modern healthcare, promising improved patient outcomes and reduced adverse effects. AI improves data analysis, increases diagnostic accuracy, and speeds up genomic studies, revolutionizing healthcare. Incorporating artificial intelligence in genomics is transforming the domain, creating new possibilities for discovery, precision, and individualized therapies. It is crucial to guarantee that artificial intelligence in genomics complies with data privacy regulations and safeguards individuals’ genetic data. Artificial intelligence in human DNA research is highly intricate and demands substantial computational resources for analysis. The AI and genomics futures combine various omics data genomics, transcriptomics, proteomics, and metabolomics into cohesive, AI-driven systems. Scientists at Pfizer are able to rely on modeling and simulation to identify compounds that have the highest likelihood of being effective treatment candidates so they can narrow their efforts. These are some of the companies paving the way for healthcare innovation by applying AI technology. While the promise of AI in healthcare is substantial, balancing innovation with patient safety, privacy and ethical considerations will remain critical as the technology evolves. To mitigate these risks, AI developers are implementing data anonymization techniques, strict encryption standards and compliance measures aligned with HIPAA regulations. It equips users to make informed decisions and prevents complications by including AI-based features such as predictive alerts and health recommendations. There are tools for real-time glucose monitoring, medication tracking, and lifestyle management for an all-rounded approach to diabetes care. AI shortens cancer research timelines by identifying drug targets within vast genomic datasets, accelerating drug discovery. AI is revolutionizing personalized medicine by customizing therapies according to each person’s biological composition. For instance, Intel partners with biotech firms to enhance CRISPR’s accuracy by using AI to predict and minimize off-targeting effects, promoting targeted and efficient genetic modifications. Table 3 (Summary of Development Challenges and Future Prospects in AI Healthcare) outlines the key ethical, privacy, and governance challenges in AI healthcare implementation, along with their impacts and potential future directions. In practical applications, Li et al.’s (109) research shows that wearable devices integrated with AI algorithms continuously monitor patients and alert clinicians to early signs of deterioration, reducing hospital readmission rates and minimizing unnecessary emergency visits. For example, AI systems have been used in oncology to analyze genomic data, identifying specific tumor mutations and helping clinicians select the most effective targeted therapies for cancer patients (104). AI tailors treatment plans by integrating individual genetic information, biomarkers, and lifestyle factors, enabling more effective and customized treatments. AI applications in precision medicine and nursing are driving a significant transformation in healthcare systems, transitioning them from reactive to proactive care. Studies have shown that such integrated approaches can reduce hospital readmission rates for patients with chronic conditions by facilitating proactive and personalized care management (65, 95). In this sense, there are remarkable studies such as the one by Dente et al28 which employed machine learning approaches to identify predictive profiles of pneumonia bacteria; Dagliati et al29 who used AI to tackle diabetes complications or in predicting focal epilepsy outcomes and ischemic stroke and thromboembolism.30,31 One of the highest potentialities of AI in precision medicine is its use in preventing diseases using techniques that could assess the risk of disease development. Until recently, it has been difficult for scientists to understand a protein’s shape — how the long molecules fold and twist onto themselves in three dimensions. In addition, it could expand the pool of scientists who can effectively use gene editing technology — no experience required. In contrast, CNN models remain computationally more efficient and are often better suited for deployment in scenarios with limited data or computational resources, such as in primary care clinics or on portable imaging devices (23). However, their substantial computational cost and even greater data demands make them challenging to deploy in resource-constrained environments (40). This capability significantly enhances diagnostic accuracy and helps reduce human errors, including misdiagnosis and missed diagnosis. This section explores how AI enhances diagnostic accuracy across data modalities, while also examining the technical and practical challenges that influence its clinical adoption. Our world is experiencing a technological revolution that is being fueled by ever-increasing computational capacity. In the end, the discussion about whether patients prefer human geneticists to AI may be moot; economic arguments and market pressures will be a powerful impetus for AI to replace clinicians. However, I anticipate that full automation will bypass humans acting as a meaningful part of this loop, at least in the everyday sense of a clinician interacting with an individual patient. A notable example is the application of AI models to identify genetic markers for Alzheimer’s disease, facilitating early intervention strategies. By analyzing individual genetic variations, ML algorithms can predict susceptibility to specific diseases, thereby laying a crucial foundation for early diagnosis and prevention (49). AI technology, particularly ML, has become instrumental in mining genomic data to extract disease-related biomarkers from large-scale datasets. In this process, the advanced interpretation of genomics through artificial intelligence and machine learning approaches plays a crucial role in the search for solutions. In order to maximize individual therapy, it centers on the unique patient, taking into consideration genetic, biomarker, phenotypic, or psychological aspects . Recently, the use of precision medicine has gained much support. 31 The field has since evolved to recognize how the intersection of multi‐omic data combined with medical history, social/behavioral determinants, and environmental knowledge precisely characterizes health states, disease states, and therapeutic options for affected individuals. The field of precision medicine is similarly experiencing rapid growth. A version of the Friedman’s fundamental theorem of informatics describing the impact of augmented intelligence. A version of augmented intelligence, described in the literature in Friedman’s fundamental theorem of biomedical informatics, 24 relates strongly to the role of AI in health care (depicted in Figure 1 ). Li et al. used erlotinib to create drug reactivity patterns from cancer cell lines. The superiority of gene expression profile datasets may make it challenging to understand prediction models, but this can be reduced using TANDEM, a two-stage method . Using multi-omics data, ML-made computational models are being developed to anticipate drug response using response-predictive biomarkers . The precision oncology approach requires the detection of a panel of biomarkers linked to therapy responses. The researchers discovered biomarkers and smaller groups of patients with specific microenvironment dysregulation, cell proliferation, metabolic reprogramming, and possible treatments (Table 1). Similarly, combining the copy number of DNA variations with breast cancer patients' gene expression helped researchers who learn the disease's mechanism and developed new treatment strategies .