https://esj.eastasouth-institute.com/index.php/esiscs <p><strong>ESISCS - The Eastasouth Journal of Information System and Computer Science</strong></p> <p><a href="https://portal.issn.org/resource/ISSN/3025-566X">ISSN International Centre</a> | <a href="https://issn.brin.go.id/terbit/detail/20230906471615916">ISSN: 3025-566X (online)</a> | <a href="https://issn.brin.go.id/terbit/detail/20231102111504538">ISSN: 3026-6041 (Print)</a></p> <p>ESISCS - The Eastasouth Journal of Information System and Computer Science is a peer-reviewed journal and open access three times a year (April, August, December) published by <a href="https://eastasouth-institute.com/jurnal/">Eastasouth Institute</a>. ESISCS aims to publish articles in the field of <strong>Enterprise systems and applications, Database management systems, Decision support systems, Knowledge management systems, E-commerce and e-business systems, Business intelligence and analytics, Information system security and privacy, Human-computer interaction, Algorithms and data structures, Artificial intelligence and machine learning, Computer vision and image processing, Computer networks and communications, Distributed and parallel computing, Software engineering and development, Information retrieval and web mining, Cloud computing and big data</strong>. ESISCS accepts manuscripts of both quantitative and qualitative research. ESISCS publishes papers: 1) review papers, 2) basic research papers, and 3) case study papers.</p> <p>ESISCS has been indexed in, <a href="https://crossref.org">Crossref</a>, and others indexing.</p> <p>All submissions should be formatted in accordance with <a href="https://raw.githubusercontent.com/upileasta/Paper-Template-EI/main/Paper%20Template%20The%20Eastasouth%20Journal%20of%20Information%20System%20and%20Computer%20Science.docx">ESISCS template</a> and through Open Journal System (OJS) only.</p> Eastasouth Institute en-US The Eastasouth Journal of Information System and Computer Science 3026-6041 https://esj.eastasouth-institute.com/index.php/esiscs/article/view/594 <p>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.</p> Antu Roy Md. Ashik Nirupam Khan Delwar Karim Amit Kumar Copyright (c) 2025 Antu Roy, Md. Ashik, Nirupam Khan, Delwar Karim, Amit Kumar https://creativecommons.org/licenses/by-sa/4.0 2025-06-13 2025-06-13 3 01 1 10 10.58812/esiscs.v3i01.594