## 3 Enhancing Hospital Facilities with New Technologies  
Emerging technologies present opportunities to enhance the capabilities of hospital ATMP facilities within ever-present space, time, cost, and personnel constraints. Process analytical technology (PAT) is a pivotal advancement employing advanced in-line monitoring and process control to optimize manufacturing [22]. By continuously analyzing physicochemical parameters like pH, dissolved oxygen, and temperature--as well as metabolites and nutrient concentrations, such as glucose and glutamine, and byproduct concentrations, such as lactate and ammonia--bioreactors can be automatically controlled, decreasing manual operations [23,24]. This approach is complemented by the application of advanced sensory technologies, particularly fluorescent optical sensors, as highlighted in recent research [25]. These sensors are instrumental for real-time monitoring, enabling the quantification of analytes of interest during bioprocessing. This capability for early detection is crucial for identifying deviations from method parameters, facilitating timely countermeasures and ensuring consistency in individualized batches. The integration of PAT with state-of-the-art sensory technologies underscores the increase in biomanufacturing efficiency for enhanced control methods in the complex cellular manufacturing environments of cell and gene therapy. Automated cell counting and viability analysis and product concentration reduces delays when waiting for quality control results before proceeding to the next manufacturing step. PAT enables real-time process adjustments and consistency between individualized batches [26]. Building upon the principles of PAT, a recent study [27] made significant strides toward enhancing the capabilities of in-line monitoring and process optimization in biomanufacturing. This research introduced a pioneering automated method for creating Raman spectroscopy models using Python and Bayes optimization. This innovation represents a significant leap in the field, dramatically improving the accuracy of models and simplifying the complexities associated with monitoring a diverse range of compounds. The study's approach notably broadened the scope of detectable compounds in cell cultures. It successfully constructed models for almost every amino acid and extended this capability to components traditionally elusive to Raman spectroscopy, such as metal ions, oxygen, and carbon dioxide. This expansion in the range of detectable compounds is pivotal, as it enhances our understanding of cell culture environments and allows for more precise control over the manufacturing process. Highlighting the precision of this method, the study's models, particularly for glucose concentration, demonstrated remarkable accuracy, with a correlation coefficient (R\({}^{2}\)) of 0.93 and a root mean square error of 0.23. Such precision in monitoring is critical in the biomanufacturing sector, where the maintenance of optimal culture conditions directly influences product quality and yield. Moreover, the method's ability to model various compounds, including those indicative of stress markers like BiP and oxidative glutathione, opens up new possibilities for monitoring a wide array of culture characteristics. This capability is instrumental for real-time monitoring and feedback control, ensuring the continuous adaptation and optimization of culture conditions. It aligns directly with the objectives of PAT, whose fundamental focus is to maintain consistency and quality across batches through real-time process adjustments. The integration of this method with advanced AI technologies further underscores its potential. Exploiting the power of AI, this approach can create more comprehensive and precise models for a broad spectrum of parameters, extending beyond traditional medium components and metabolites.