The healthcare supply chain is a network of activities, organizations, resources, and processes that provide and distribute healthcare services and medical products. This chain includes pharmaceutical manufacturers, distribution companies, hospitals, pharmacies, healthcare centers, and patients. The primary objective of healthcare supply chain management is to ensure timely and effective access to healthcare resources at the lowest cost and with the highest quality. Unlike many other industries, the healthcare supply chain is highly sensitive because disruptions can directly affect patient health and safety [4].

AI refers to a collection of technologies and algorithms capable of performing tasks that typically require human intelligence. These technologies include machine learning, deep learning, natural language processing, expert systems, and data analytics. In recent years, advances in computational power and the rapid growth of digital data have significantly expanded the use of AI across various industries, particularly in healthcare [5].

Numerous studies have investigated the application of AI in healthcare supply chains. Researchers have shown that machine learning algorithms can improve the accuracy of pharmaceutical demand forecasting and prevent shortages or excessive inventory accumulation [6]. Moreover, the use of intelligent systems in the management of medical equipment distribution has reduced operational costs and improved responsiveness [7]. Several studies have also examined the role of AI in healthcare crisis management. Their findings indicate that AI-based systems can facilitate managerial decision-making during emergencies and optimize the allocation of healthcare resources [8].

In addition, AI-based technologies can play a substantial role in identifying consumption patterns and analyzing patient behavior. The analysis of healthcare visitation records, disease prevalence, and pharmaceutical consumption trends enables healthcare managers to perform more accurate resource planning. This capability is particularly valuable during sudden increases in demand, as it can prevent disruptions in healthcare service delivery [9].

On the other hand, intelligent systems enhance transparency and traceability throughout healthcare supply chain processes. These systems can continuously record and analyze data related to inventory levels, transportation activities, and medical equipment distribution. Consequently, managers can make faster, more accurate decisions while reducing the likelihood of human error, resource waste, and delivery delays [10]. In reality, many supply chain problems originate not from a lack of technology but from inconsistent human decision-making. An astonishing revelation, considering humans designed the systems and then seemed surprised when the systems behaved like humans .

One of the most significant applications of AI in healthcare supply chains is forecasting the demand for pharmaceuticals and medical equipment. Traditional forecasting methods are generally based on limited datasets and simple analytical techniques, which often lack sufficient accuracy under dynamic conditions. Machine learning algorithms can analyze historical data, seasonal patterns, disease trends, and consumer behavior to more accurately predict demand. This capability helps reduce pharmaceutical shortages, prevent excessive inventory accumulation, and decrease storage costs [11].

Inventory management is a major challenge for hospitals and healthcare centers. A shortage of medical equipment may endanger patient lives, while excessive inventory levels increase costs and waste resources. AI-based systems can monitor inventory status in real time and determine the optimal replenishment timing. By analyzing consumption patterns, pharmaceutical expiration dates, and crisis conditions, these systems provide more accurate inventory management decisions [12].

Another important application of AI is the optimization of pharmaceutical and medical equipment distribution networks. Intelligent algorithms can identify optimal transportation routes and reduce delivery times. This capability is especially critical in remote regions and emergencies. AI-based systems help reduce logistics costs, accelerate delivery processes, and improve patient access to healthcare services [13].

Healthcare centers generate enormous amounts of data daily. Manual analysis of such data is both time-consuming and difficult. AI can analyze healthcare data and provide valuable insights for managers. For example, intelligent algorithms can identify disease outbreak trends and predict future hospital needs for equipment and workforce resources. Such capabilities can significantly improve planning and resource allocation processes [14].

Healthcare supply chains are continuously exposed to risks such as global crises, shortages of raw materials, transportation disruptions, and market fluctuations. AI can analyze environmental data and identify risk patterns to predict potential disruptions. This argument enables managers to implement preventive measures before crises occur, thereby minimizing interruptions in healthcare service delivery [15].

AI optimizes procurement, storage, and distribution processes, thereby reducing operational costs. Minimizing resource waste and improving inventory management also play a substantial role in reducing costs [16].

Intelligent algorithms can analyze large volumes of data and make more accurate decisions than traditional methods. This argument improves the quality of hospital and healthcare center management [17].

The use of intelligent systems accelerates managerial processes and reduces response times during critical situations [17].

When pharmaceuticals and medical equipment are available on time, the quality of healthcare services improves, and patient satisfaction increases accordingly [18].

AI can accurately record and analyze healthcare supply chain processes, resulting in greater transparency and reduced managerial errors [18].

Designing and implementing AI-based systems requires significant investment in information technology infrastructure, software systems, and workforce training [19].

Healthcare data are highly sensitive. The use of AI systems may raise concerns regarding information security and patient privacy protection [20].

Many healthcare institutions face a shortage of specialists in data science and AI. This issue can hinder the implementation of intelligent technologies [21].

Some healthcare organizations and employees resist adopting new technologies. Such resistance may obstruct the development and implementation of AI-based systems [22].