AI-BASED PREDICTIVE LOGISTICS SYSTEM FOR MANAGING DRUG CONSUMPTION IN HOSPITALS

• Amirkulova Sug‘diyona

  Faculty of General Medicine No.1, Student of Group 112 Tashkent State Medical University, Tashkent, Uzbekistan

  Author

• Amirkulov Rakhmatilla

  Faculty of General Medicine No.1, Student of Group 113 Tashkent State Medical University, Tashkent, Uzbekistan

  Author

• Fazliddin Arzikulov

  Assistant of the Department of Biomedical Engineering, Informatics, and Biophysics at Tashkent State Medical University

  Author

Efficient drug consumption management is a critical challenge for modern healthcare systems, particularly in hospitals where inaccurate forecasting often leads to medication shortages, wastage, and increased operational costs. The integration of Artificial Intelligence (AI) into hospital logistics has emerged as a transformative solution to these challenges. This article explores the design, implementation, and benefits of an AI-based predictive logistics system for managing drug consumption in hospitals. By leveraging machine learning algorithms, historical consumption data, patient flow patterns, and seasonal disease trends, such systems can accurately predict future medication demand. The study highlights how AI-driven predictive logistics improves inventory optimization, reduces waste, enhances patient safety, and supports data-driven decision-making in healthcare institutions. The findings demonstrate that AI-based systems represent a sustainable and scalable approach to modern hospital drug supply management.

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