Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine

AI-Driven Pharmacogenomics and Precision Medicine: Future of Personalized Therapy

Pharmacogenomics and artificial intelligence (AI) are emerging as important drivers of precision medicine, enabling healthcare systems to adopt individualized therapeutic approaches. Pharmacogenomics examines how genetic variations influence drug response, efficacy, metabolism, and toxicity, while AI provides advanced computational tools for analyzing complex genomic and clinical data. This review highlights the integration of AI-driven pharmacogenomics in personalized therapy and its potential to improve treatment outcomes. Machine learning, deep learning, natural language processing, and big data analytics are increasingly used to identify genetic variants, predict drug responses, optimize medication selection and dosage, and reduce adverse drug reactions. These technologies support the interpretation of large-scale genomic information and facilitate evidence-based clinical decision-making. Significant applications have been demonstrated in oncology, cardiovascular diseases, neurological and psychiatric disorders, and rare genetic diseases, where personalized treatments can enhance therapeutic efficacy and patient safety. Recent advances in genomic sequencing, multi-omics integration, digital health technologies, explainable AI, and real-time patient monitoring have further expanded the scope of precision medicine. However, challenges related to data privacy, algorithm bias, regulatory frameworks, and clinical implementation remain. Future developments in explainable AI, predictive analytics, and AI-powered personalized therapy are expected to improve treatment precision and accelerate the realization of truly individualized healthcare. Overall, the convergence of AI and pharmacogenomics represents a transformative approach to modern medicine with substantial potential to improve patient outcomes and optimize therapeutic interventions.

Artificial Intelligence in Clinical Genetics: Current Applications and Challenges

Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions

Machine learning (ML) is transforming cancer research and care by enabling analysis of complex, high-dimensional datasets spanning genomics, transcriptomics, proteomics, imaging, and clinical records. By improving risk stratification, accelerating detection and diagnosis, and supporting treatment selection, ML has the potential to enhance survival outcomes while increasing efficiency across oncology workflows. This review synthesizes key developments in ML for oncology, covering foundational algorithms alongside emerging approaches. We highlight major application areas including early cancer detection, tumor classification, molecular subtyping, biomarker discovery, prognosis estimation, multi-omics integration, computational pathology, pharmacogenomics, and clinical decision support. We also summarize commonly used datasets, discuss the importance of interpretability for clinical trust, and outline barriers to translation such as data heterogeneity, bias, and regulatory constraints. Finally, we describe future directions, including federated learning, graph neural networks, longitudinal modeling, and integration of real-world and wearable data to support precision oncology. This review is intended for cancer researchers, clinicians, and data scientists seeking a practical overview of ML methods, opportunities, and translational considerations in oncology.

AI-based multimodal integration of genomics and electronic health records.

Integrating Artificial Intelligence with Global Genomic Resources: A Narrative Review of Implications for Precision Medicine

Purpose This review aimed to explore how artificial intelligence can be integrated with global genomic resources and to examine its implications for advancing precision medicine, with particular attention to population diversity, predictive modeling, and clinical translation. Methods A narrative review design was adopted, drawing on literature from major databases and supplementary search sources including PubMed, Scopus, Web of Science, IEEE Xplore, Embase, and Google Scholar. Studies were selected based on relevance to artificial intelligence applications in genomic data analysis and precision medicine. Key information from included studies was extracted using a structured narrative extraction framework and synthesized thematically to identify key patterns and emerging insights. Results A total of 54 studies were included in this review. The synthesis identified five recurring application areas: genomic data integration, variant and disease association detection, disease susceptibility and risk prediction, treatment response prediction, and clinical decision support. Findings indicate that artificial intelligence can support the integration and analysis of multi-omics data, support the identification of genetic variants and disease associations, and improve predictive modeling for precision medicine. Incorporating diverse population data was also reported to improve model generalizability and reduce bias. However, challenges related to data standardization, interoperability, ethical governance, overfitting in small or biased cohorts, limited prospective clinical validation, reproducibility, and clinical implementation remain significant barriers. Conclusion The integration of artificial intelligence with global genomic resources holds substantial promise for advancing precision medicine by enabling more accurate, inclusive, and individualized precision medicine. However, this promise should be interpreted cautiously because many AI genomic models remain dependent on retrospective datasets, limited external validation, and variable reproducibility.

ARTIFICIAL INTELLIGENCE–ENABLED COMPUTATIONAL PATHOLOGY: FOUNDATION MODELS FOR NEXT-GENERATION PRECISION CANCER DIAGNOSTICS

Computational pathology has emerged as one of the most transformative applications of artificial intelligence (AI) in modern oncology by enabling automated interpretation of whole-slide images, quantitative characterization of tumor biology, and integration of histopathological information with multimodal biomedical data for precision cancer diagnostics. Conventional pathology relies heavily on expert visual interpretation, which, despite its indispensable role in cancer diagnosis, is susceptible to interobserver variability, increasing workload, and limitations in detecting subtle morphological patterns associated with molecular alterations and clinical outcomes. Recent advances in foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence have enabled generalized representation learning across digital pathology, radiological imaging, genomics, transcriptomics, proteomics, metabolomics, epigenomics, spatial biology, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support precision diagnosis, molecular characterization, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen computational pathology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological advances, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, interoperability, explainability, cybersecurity, clinical validation, and equitable implementation. This review provides a comprehensive overview of AI-enabled computational pathology, emphasizing foundation models as transformative technologies for next-generation precision cancer diagnostics.