Emerging Trends in Personalized Medicines
Volume 3, Issue 2 (2026)
Table of contents
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Personalized Medicine: the impact of ferrofluids, clinical procedures, strategies, applications,challenges, current trends, and reseach opportunities.
Abstract: Personalized medicine has shown progressive development and evolution in health care systems. It is considered as transformative emerging medicine encorporating advanced technologies including genomics, AI as thinking partner, and nanotechnology in a greater extent. The quest for painless injection, lesstoxicity, rapid cell separation, and gene sequencing make ferrofluid the focus of personalized medicine. Currently, magnetic nanoparicles and ferrofluides are becoming driving force of personalized medicine. However, contronlling and managing nanoscale …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 2, 2026 Read article
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AI-Driven Pharmacogenomics and Precision Medicine: Future of Personalized Therapy
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 …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 2, 2026 · pp. 1–12 Read article
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Explainable Artificial Intelligence in Personalized Medicine: Emerging Clinical Perspectives
Abstract: The convergence of artificial intelligence (AI) and precision medicine has transformed contemporary healthcare by enabling data-driven clinical decision-making, individualized therapeutic interventions, and predictive diagnostics. However, despite remarkable advances in machine learning (ML) and deep learning (DL), the widespread adoption of AI in healthcare remains constrained by the “black-box” nature of many computational systems. Clinicians, regulatory agencies, and patients increasingly demand transparency, interpretability, and trustworthiness in AI-guided medical recommendations. Explainable Artificial …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 2, 2026 · pp. 13–29 Read article