## 3 Enhancing Hospital Facilities with New Technologies
This synergy between Raman spectroscopy modelling and AI technologies provides sophisticated and efficient monitoring and control mechanisms in biopharmaceutical manufacturing, aligning with the industry's move towards more automated, precise, and intelligent process management systems. Continuous manufacturing, producing therapies in an uninterrupted flow rather than individual batches, shows promise for integration into hospital facilities. One example that has been demonstrated is interrupted cell culture production of viral vectors [28; 29], as well as perfusion systems that enable constant harvesting of cellular products [30]. Ongoing downstream processing steps are also being integrated, to develop end-to-end production. Compared to batch manufacturing, continuous processes require smaller equipment footprints, allowing hospitals to increase productivity within the given space constraints. The verification and validation methods enabled by PAT support can ensure quality control in real time. The research by [PERSON] et al. [31] exemplified the profound impact of PAT in biopharmaceutical manufacturing, particularly in the optimization of cell and gene therapy processes. Their study employed the Ranger system, a novel refractometry-based PAT, which demonstrated its efficacy in real-time monitoring of metabolic activity within HEK293T cell cultures during the production of lentiviral vectors. This system's ability to rapidly discern the relationship between bioreactor pH and culture metabolic activity, and to adjust the pH accordingly, resulted in a notable 1.8-fold increase in metabolic activity compared to an unoptimized process. However, it also revealed a particular aspect of cellular response: increased metabolic activity did not correspond to increased lentiviral vector production, highlighting the complexity of these biological systems and the importance of sophisticated process control. Furthermore, through metabolic flux modelling, [PERSON] et al. uncovered how low-pH environments caused a significant metabolic shift in cells, redirecting cellular resources from growth towards managing environmental stress and adverse conditions. These insights underscore the capacity of PAT, not only in enhancing process efficiency, but also for enriching our understanding of the underlying biological mechanisms, thus contributing to a continuous improvement in quality control and process optimization in real time [31]. Artificial intelligence (AI) and machine learning (ML) have the potential to facilitate and enhance the analysis of the vast amount of data generated during PAT monitoring and throughout manufacturing steps [20; 32]. By detecting patterns and correlations, AI models can identify critical bioprocess parameters, predict deviations, and determine potential actions, aiding biotechnological personnel. Intelligent algorithms can also support the automation of elements of batch record reviewing, equipment maintenance, prevention campaigns, and contamination risk analysis. AI-based automation of cell and gene therapy manufacturing, sample tracking, and environmental monitoring of the cleanroom will increase hospital facilities' consistency and oversight. Recent research has exemplified AI's aptitude for tackling personalized medicine's intricacies. [PERSON] et al. 's study described predictive machine learning algorithms that can anticipate individual patient-specific therapy response rates based on biomarker and genomic data analysis [19]. Meanwhile, [PERSON] et al., [33] discussed an AI digital platform that automates cell therapy production track ing and shows enhanced consistency between autologous batches. This concept is in line with advanced AI-powered microfluidic technology, which has significantly enhanced both fundamental biological research and translational clinical diagnosis. The implementation of AI in microfluidic technology, as highlighted in a recent study, enables the processing of the large-scale data obtained in high-throughput assays.