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Emerging Trends in Personalized Medicines Review Article

Explainable Artificial Intelligence in Personalized Medicine: Emerging Clinical Perspectives

  1. Rashmi Singh Department of Pharmacy, S. N. College of Pharmacy

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 Intelligence (XAI) has therefore emerged as a transformative paradigm that enables the interpretation of algorithmic decisions while maintaining predictive performance. In personalized medicine, XAI facilitates the understanding of patient-specific treatment pathways, biomarker identification, genomic interpretation, and individualized pharmacotherapy optimization.The present review aims to critically examine the role of explainable artificial intelligence in personalized medicine with special emphasis on clinical applications, pharmacological implications, computational methodologies, therapeutic optimization, regulatory concerns, and future translational opportunities. The review further discusses how XAI can bridge the gap between computational intelligence and clinical reliability in pharmaceutical sciences.Recent advances in healthcare digitization have generated massive biomedical datasets derived from genomics, proteomics, metabolomics, electronic health records (EHRs), radiomics, wearable biosensors, and pharmaceutical databases. AI systems are increasingly employed to analyze these multidimensional datasets for disease prediction, drug response assessment, toxicity profiling, and individualized therapeutic planning. Nevertheless, conventional deep learning models often lack transparency, thereby limiting clinician confidence and regulatory acceptance.

Keywords

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