AI-Driven Risk Management & Optimization in Healthcare Supply Chain: A Machine Learning Approach
The increasing complexity of healthcare supply chains (HSCs) has introduced significant risks, including cyber threats, disruptions, and inefficiencies. This paper explores the application of artificial intelligence (AI) and machine learning (ML) in risk management for healthcare supply chains. AI-driven models enhance predictive analytics, optimize inventory management, and mitigate disruptions by analyzing vast datasets in real time. This study reviews existing literature discusses AI-based risk assessment frameworks and presents a methodology for implementing machine learning techniques in HSC risk management. Future considerations highlight AI's role in enhancing security, resilience, and operational efficiency within healthcare logistics.
Authors
- Md Rezaul Karim (ORCID: https://orcid.org/0000-0002-9046-3759)
- Kazi Mazharul Islam (ORCID: https://orcid.org/0009-0003-4393-5607)
- Md Imran Khan (ORCID: https://orcid.org/0009-0004-9270-8782)
- Rasmila Lama
- Birbal Tamang
Institutions
- Master's College (US)
- Trine University (US)
- Lamar University (US)
Publication Details
- Journal
- Journal of Artificial Intelligence General science (JAIGS) ISSN 3006-4023
- Published
- 2025-08-10
- DOI
- https://doi.org/10.60087/jaigs.v8i02.394
- Primary Topic
- Supply Chain Resilience and Risk Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00