Integrating Internet of Things (IoT) technology inside the cold supply chain can enhance transparency, efficiency, and quality, optimizing operating procedures and increasing productivity. The integration of IoT in this complicated setting is hindered by specific barriers that need a thorough examination. Prominent barriers to IoT implementation in the cold supply chain are identified using a two-stage model. After reviewing the available literature on the topic of IoT implementation, a total of 13 barriers were found. The survey data was cross-validated for quality, and Cronbach's alpha test was employed to ensure validity. This research applies the interpretative structural modeling technique in the first phase to identify the main barriers. Among those barriers, "regularity compliance" and "cold chain networks" are key drivers for IoT adoption strategies. MICMAC's driving and dependence power element categorization helps evaluate the barrier interactions. In the second phase of this research, a decision-making trial and evaluation laboratory methodology was employed to identify causal relationships between barriers and evaluate them according to their relative importance. Each cause is a potential drive, and if its efficiency can be enhanced, the system as a whole benefits. The research findings provide industry stakeholders, governments, and organizations with significant drivers of IoT adoption to overcome these barriers and optimize the utilization of IoT technology to improve the effectiveness and reliability of the cold supply chain.

Artificial intelligence (AI) will transform the pharmaceutical cold chain -- not in the distant, hypothetical future, but in the next few years. As the president of a company that has been actively involved in the creation of an application that will utilize machine learning to generate predictive data on environmental hazards in the biopharmaceutical cold chain cycle, I've seen firsthand the promise of this technology. When coupled with machine learning and predictive analytics, the AI transformation goes much deeper than smarter search functions. It holds the potential to address some of the biggest challenges in pharmaceutical cold chain management. By aggregating and analyzing data from multiple sources -- a drug order and weather data along a delivery route, for example -- AI-based systems can provide complete visibility with predictive data throughout the cold chain.

Given its wide and growing range of capabilities and applications, Artificial Intelligence (AI) can be effectively used to improve the state of public healthcare in many parts of the world. And while AI is being increasingly used in clinics to treat individual patients, its application in public health systems has been far less in comparison. From my experience of studying the social impact of computing in developing worlds and multiple areas of public health, I strongly believe that AI can strengthen public health systems and transform medical logistics. AI can, for instance, supplement the ongoing worldwide efforts to improve immunization logistics and ensure adequate supply of vaccines for children at every local health centre. It can also address many other issues that surround the vaccine cold chain; for instance, the challenge of storing the vaccines at the right temperature in fully operational refrigerators.