Artificial Intelligence in Precision Medicine: Enhancing Chronic Disease Management and Genomic Drug Discovery through Predictive Modeling

Antu Roy, Md. Ashik, Nirupam Khan, Delwar Karim, Amit Kumar — Fri, 13 Jun 2025 00:00:00 +0000

This paper explores the transformative role of Explainable Artificial Intelligence (XAI) in precision medicine, focusing on its application in chronic disease management and genomic drug discovery. Through two detailed workflow diagrams, the study highlights the integration of XAI within the clinical decision-making pipeline and biomedical research domains. Figure 1 illustrates a comprehensive process encompassing data acquisition, preprocessing, predictive modeling, and clinician feedback, all underpinned by XAI techniques such as SHAP, LIME, and attention mechanisms. This workflow enhances trust and transparency in AI-driven predictions, empowering clinicians to interpret and act on machine-generated insights. Figure 2 extends this understanding by mapping XAI applications to chronic disease monitoring and genomic analysis. In chronic care, XAI enables risk stratification and personalized interventions, while in genomic drug discovery, it facilitates the identification of potential targets through interpretable machine learning models. Together, these figures underscore XAI’s critical role in translating complex data into actionable healthcare outcomes. By promoting accountability, user trust, and informed decision-making, XAI emerges as a cornerstone for the ethical and effective deployment of artificial intelligence in precision medicine. The paper concludes that integrating explainability into AI models is not only a technical necessity but also a fundamental step toward safer, smarter, and more inclusive healthcare systems.

Accelerating Drug Discovery: The Role of Generative AI and Big Data Analytics

Ramchorn Gharami, Delwar Karim, Jhon Kabir, Rashid Khan — Thu, 19 Jun 2025 00:00:00 +0000

Drug discovery has long been characterized by extensive timelines, high costs, and significant risks, often taking more than a decade and billions of dollars to bring a single drug to market. However, the convergence of generative artificial intelligence (AI) and big data analytics is fundamentally reshaping this landscape. This paper provides an in-depth analysis of generative AI especially models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and transformer-based architectures combined with vast biological and chemical datasets, is transforming molecular design, target identification, and compound optimization. Through a systematic review of literature, comparative model evaluation, and real-world case studies including AlphaFold, the paper explores the efficacy of these technologies in accelerating drug discovery. A hybrid methodology combining data mining, model testing, and bioinformatics simulation is employed. The results demonstrate significant improvements in candidate molecule generation, predictive modeling accuracy, and time-to-market for new drugs. Future challenges such as data interoperability, ethical considerations, and regulatory compliance are also discussed. The study concludes by highlighting the immense potential of AI and big data in ushering a new era of precision medicine and personalized therapeutics.

The Eastasouth Journal of Information System and Computer Science

Artificial Intelligence in Precision Medicine: Enhancing Chronic Disease Management and Genomic Drug Discovery through Predictive Modeling

Accelerating Drug Discovery: The Role of Generative AI and Big Data Analytics