2026· Emerging Trends in Personalized Medicines· 0 citations
TL;DR
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.
Abstract
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.
Alzheimer’s Disease (AD) is a neurodegenerative disease that causes significant clinical, social,
and economic burden worldwide. Despite improvements in understanding its multifaceted pathogenesis, current
treatments are mostly symptomatic and ineffective across varied patient populations. To overcome these
constraints, AI–driven precision medicine allows tailored risk assessment, treatment selection, and disease
monitoring. This review covers AI's role in AD precision medicine, focusing on drug repurposing, digital
therapies and clinical decision support systems. Machine and deep learning models are used to predict medication
response, integrate heterogeneous data sources such as genomics, transcriptomics, neuroimaging and
electronic health records, and uncover pharmacogenomic treatment success factors. The paper covers AIenabled
precision pharmacology, including tailored dosing algorithms, adaptive therapeutic monitoring, and
adverse drug reaction prediction. Bioinformatics-based target identification, network pharmacology, graphbased
AI models, virtual screening, and real-world and clinical data validation are emphasized in AI-driven
medication repurposing. AI-powered digital treatments like personalized cognitive training platforms, wearable-
derived digital biomarkers, virtual and mixed reality interventions, adherence monitoring, and digital
twins for therapy optimization have been discussed. AI-based clinical decision support systems are also thoroughly
assessed for clinical value, accuracy, and explainability in disease subtyping, trajectory prediction, and
risk stratification in preclinical and prodromal AD. Despite these promises, data heterogeneity, algorithmic
bias, legal barriers, and privacy concerns exist. Federated learning enables safe multi-center collaboration and
hybrid AI–human approaches, and it represents the future. AI's ability to alter AD care opens the door to precision
medicine paradigms that use repurposed medications, digital tools and intelligent decision-making to
improve patient outcomes.
Bhagawati Saxena, Neha Sisodiya, R. Khabiya et al.· Current pharmaceutical desig...· 0 citations
A clinically oriented, pipeline-based synthesis of contemporary AI applications in genomic medicine, focusing on factors that determine model robustness and clinical utility, and common sources of failure in real-world genomic AI systems.
Alexandra-Maria Blaga, Răzvan-Octavian Mihuț, A. Treteanu et al.· International Journal of Mol...· 0 citations
Personalized medicine is a transformative healthcare approach that designs prevention, diagnosis, and treatment to each individual’s unique genetic, molecular, and environmental profile. By integrating multi-omics technologies—genomics, transcriptomics, proteomics, and metabolomics—with advanced computational analytics, it enables early disease prediction, precise classification, and patient-specific therapy design. Advances in next-generation sequencing, artificial intelligence, and additive manufacturing, including 3D printing, have driven pharmacogenomic drug optimization, biomarker-guided targeted therapies, and on-demand production of customized dosage forms. Clinical applications span oncology, infectious, cardiovascular, neurological, and rare genetic diseases, improving therapeutic precision, reducing adverse effects, and enabling proactive disease management. Key benefits include more accurate drug-response prediction, fewer treatment failures, enhanced patient safety, and long-term economic savings through reduced healthcare utilization. However, widespread adoption faces challenges such as high cost, limited access in low-resource settings, and protection of sensitive genomic data. Ethical concerns—privacy, informed consent, and equitable benefit sharing—require robust regulation and public engagement. Future progress depends on expanding genomic diversity, building curated multi-omics repositories, and leveraging AI for biomarker discovery and predictive modeling. Emerging innovations in continuous manufacturing and flexible 3D printing will support individualized small-batch drug production, while adaptive policy frameworks must ensure safety, affordability, and accessibility. With coordinated scientific, technological, and ethical efforts, personalized medicine promises predictive, preventive, precise, and participatory healthcare worldwide.
Janhavi Patil, Diya H. Aga, Sneha Yadav et al.· Fabad journal of pharmaceuti...· 0 citations
The increasing availability of heterogeneous biomedical data, including genomics, transcriptomics, proteomics, metabolomics, medical imaging, electronic health records, digital pathology, and wearable sensor data, has accelerated the development of multimodal artificial intelligence (AI) approaches for precision therapeutics. By integrating complementary information across multiple data modalities, multimodal AI aims to improve disease characterization, risk stratification, biomarker discovery, therapeutic target identification, and individualized treatment selection beyond what can be achieved using single‐modality analyses. This narrative review critically examines the current landscape of multimodal AI in medical biotechnology and precision therapeutics. Major biomedical data modalities, multimodal integration strategies, and emerging computational architectures are discussed, including deep‐learning frameworks, graph neural networks, biomedical foundation models, and multimodal large language models. Particular attention is given to the comparative strengths and limitations of early, late, and hybrid fusion approaches, as well as challenges associated with missing modalities, data heterogeneity, class imbalance, model calibration, and external validation. The review further evaluates representative applications in oncology, rare diseases, cardiovascular medicine, infectious diseases, neurodegenerative disorders, drug discovery, and companion diagnostics. Clinical translation remains constrained by limited prospective validation, inconsistent reporting standards, interoperability barriers, regulatory uncertainty, and concerns regarding fairness, privacy, and explainability. Emerging approaches such as federated learning and foundation‐model‐based architectures may help address some of these limitations, although their real‐world performance and governance requirements remain under active investigation. Overall, multimodal AI represents an important computational framework for integrating diverse biomedical data within precision therapeutics. Future progress will depend not only on methodological innovation but also on the development of robust validation frameworks, interoperable data ecosystems, equitable datasets, and clinically meaningful implementation studies capable of demonstrating improvements in patient outcomes.
Gedion Mengistu Dejen· Precision Medical Sciences· 1 citation
Conventional pharmacogenomic markers, including polymorphisms
in CYP or DPYD genes, often fail to accurately predict drug metabolism within solid tumors.
This discrepancy occurs because drug metabolism is an adaptive phenotype influenced
by the tumor microenvironment and host factors rather than a static inherited trait.
Therefore, a transition from variant-focused models to a dynamic systems pharmacology
framework using artificial intelligence (AI) is proposed in this review.
This review evaluates AI architectures, including graph convolutional networks
(GCNs), transformers, and reinforcement learning, for their ability to synthesize highdimensional
data. These models process multi-omic and spatially resolved metabolomic
data to track the shifting nature of tumor biology. We emphasize explainable AI (XAI) for
causal consistency and federated learning for privacy-preserving, multi-institutional collaboration.
AI models can effectively reverse-engineer metabolic states, forecast therapy resistance,
and map evolutionary pathways in tumors. By integrating diverse data streams,
these systems reveal the regulatory networks governing individual patient responses.
The findings emphasize that tumor drug metabolism is governed by dynamic
biological interactions rather than static genetic variants alone. AI-based multi-omics integration
therefore provides a promising strategy to capture the complex regulatory networks
linking tumor metabolism, genetic alterations, and therapeutic response. Such approaches
may improve the predictive accuracy of precision oncology models and support
more individualized treatment strategies.
AI models can effectively reverse-engineer metabolic states, forecast therapy
resistance, and map evolutionary pathways in tumors. By integrating diverse data streams,
these systems reveal the regulatory networks governing individual patient responses.
Anjali Ashok Thumbarambil, Senthil Madasamy· Current Pharmacogenomics and...· 0 citations
This review critically evaluates how advanced technologies operating in an integrated manner facilitate precision oncology by supporting fields such as genomics, transcriptomics, proteomics, metabolomics, and radiomics and provides an integrative roadmap for modern, multiomics-driven biomarker approaches in precision oncology practice.
Ujwal Havelikar, Atharva A. Shinde, Hrushikesh Mhaismale et al.· Omics· 0 citations
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