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Implementing Explainable AI for Early Detection of Chronic Kidney Disease: Strategic Insights for Health Information Systems Management

Author

Listed:
  • K M Shihab Hossain

    (Central Michigan University)

  • Jesmin Ul Zannat Kabir

    (Central Michigan University)

  • Fahimul Islam Shakil

    (Central Michigan University)

  • Md Imran Kabir Joy

    (Central Michigan University)

  • Ashrafur Rahman Nabil

    (St. Francis College)

Abstract

Early detection of Chronic Kidney Disease plays an essential role in achieving better medical results together with minimizing extended healthcare expenditures. Multiple factors regarding the complexity and opacity of artificial intelligence (AI) models prevent their use for clinical decision-making. This article analyzes Explainable AI (XAI) implementations for CKD early detection while discussing the essential role of Health Information Systems (HIS) management. The integration of interpretable machine learning models into current HIS systems allows healthcare providers to provide clearer diagnostics along with maintaining trust among healthcare staff and meeting existing regulatory standards. The research provides an implementation guide that links XAI technology frameworks to data protection systems and frontline training initiatives and clinical practice sequences for senior healthcare professionals who need ethical and effective artificial intelligence solutions. Healthcare system accountability and data-based operations combine to create a system that benefits both medical professionals and their patients in managing CKD.

Suggested Citation

  • K M Shihab Hossain & Jesmin Ul Zannat Kabir & Fahimul Islam Shakil & Md Imran Kabir Joy & Ashrafur Rahman Nabil, 2025. "Implementing Explainable AI for Early Detection of Chronic Kidney Disease: Strategic Insights for Health Information Systems Management," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 7(5), pages 140-153, May.
    • Handle: RePEc:alk:jcsts0:v:7:y:2025:i:5:id:9470
    DOI: 10.32996/jcsts.2025.7.5.19
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