Integrating Artificial Intelligence with Genomic and Biomarker Data for the Advancement of Personalized Therapeutics in a Human-Centric Framework

The landscape of modern therapeutics is rapidly evolving from a traditional "one-size-fits-all" approach to highly personalized interventions, driven by an explosion of genomic and biomarker data. However, translating this vast biological complexity into actionable, individualized treatments presents significant challenges, necessitating intelligent systems capable of identifying subtle patterns and predicting therapeutic responses with unprecedented precision. This abstract outlines a framework for integrating Artificial Intelligence (AI) with genomic and biomarker data to advance personalized therapeutics, emphasizing the crucial need for a human-centric approach to ensure ethical, equitable, and effective clinical integration. We delineate the foundational concepts of personalized therapeutics, genomic data (including pharmacogenomics), and various biomarker types, alongside the key AI paradigms (Machine Learning, Deep Learning, NLP, XAI) that enable their analysis. The abstract details specific AI applications across the entire therapeutic pipeline, from novel target identification and de novo drug design to virtual screening, drug repurposing, and the prediction of ADMET properties. Crucially, it highlights AI's role in patient stratification for clinical trials, predicting individual treatment response, identifying adverse drug reaction risks, and enabling real-time monitoring for dynamic dose optimization. The integration of AI into personalized therapeutics, however, introduces multifaceted ethical and societal challenges. These include safeguarding extreme data privacy and security of genomic information, mitigating algorithmic bias to ensure fair therapeutic outcomes across diverse populations, and addressing the "black box" problem to maintain transparency, interpretability, and accountability for AI-driven recommendations. Furthermore, complexities arise in obtaining truly informed consent, preserving patient autonomy, and navigating the evolving physician-patient relationship. Societal implications encompass ensuring equitable access to high-cost personalized therapies, managing economic burdens on healthcare systems, adapting regulatory frameworks to continuously learning AI, building public trust, and transforming the healthcare workforce. This work advocates for a human-centric integration, proposing strategies such as ethical AI by design, robust data governance (e.g., federated learning), prioritizing Explainable AI, fostering human-AI collaboration, ensuring equitable access, and developing adaptive regulatory frameworks. By proactively addressing these challenges, AI can responsibly unlock the full potential of genomic and biomarker data, ushering in an era of truly personalized, effective, and human-values-driven therapeutics.