## 4 Advanced Algorithms in Real-Time Diagnostics
### Real-Time Diagnosis of the Cell Responses to CAP Treatment  
Collecting real-time data on cancer cell responses to different CAP treatment modalities is challenging. Studies discussed in Sections 3.1 and 3.2 have utilized real-time EIS measurements [119, 125] (Table 2) and have focused on incorporating automated feedback control systems into adaptive plasma systems to optimize performance and safety (Figure 7). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Selected Parameters of the CAP** & **Input Data Obtained From** & **ML and Computational Techniques** & **Reference** \\
**Sources for Real-Time Diagnostics** & **Input Data Obtained From** & **Employed** & **[127]** \\ \hline Rotational and vibrational & & & \\ temperatures & & & \\ \hline Substrate characteristics & & & \\  & & & \\ \hline Separation distance between & & & \\ the electrodes & Electro-acoustic Emission & Gaussian process regression & \\  & & & \\  & & & \\ \hline Electron energy distribution function & & & \\ (EEDF) & & & \\  & & & \\ \hline EEDF & \begin{tabular}{c} OES, momentum-transfer \\ cross-section \\ \end{tabular} & \begin{tabular}{c} Visible Bremmsstrahlung inversion \\ (supervised ML) \\ \end{tabular} & [133, 134] \\ \hline Time-series current signals from APPJ & & & \\ (discharge type and working gas) & & & \\ \hline Plasma plume length & \begin{tabular}{c} Video frames of the plasma \\ plume captured using a \\ camera (iPhone 11) \\ \end{tabular} & Computer vision algorithms & [136] \\ \hline Temperature setpoint & \
\begin{tabular}{c} Simulated data from thermal \\ dynamics model of \\ plasma–substrate interactions \\ \end{tabular} & Reinforcement learning & [130] \\ \hline Self-adaptive plasma chemistry & & & \\ Gas input densities and energy levels & & & \\ \hline Pulse discharge characteristics &
\begin{tabular}{c} Simulated fluid model data of \\ time and pulse rise rate \\ \end{tabular} & Deep neural networks (DL) & [131] \\ \hline Plasma chemistry (tokamak) & FTIR & Physics-informed neural networks & [137] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Selected parameters of the CAP sources for real-time diagnostics and techniques employed. Yet, advanced real-time diagnostic systems are still needed to provide sensitive, accurate, and reproducible data for these feedback control systems. To achieve optimal results, a sophisticated diagnostic system with high sensitivity, accuracy, and reproducibility is imperative. These systems should seamlessly integrate with the biological target and have minimal interference with the plasma treatment process. By fulfilling these criteria, the feedback control loop can make well-informed and precise adjustments, optimizing treatment outcomes for diverse cancer cells and individual patients. Developing electronic and optical sensors suitable for addressing this technological gap and integrating these sensors into autonomous plasma systems must be focused on [140]. Bridging this gap could lead to the development of next-generation medical plasma technologies, offering superior healthcare outcomes. The applications of ML in modeling and simulating different aspects of CAP processing are well reviewed by [PERSON] et al. [141]. Future advancements in CAP modeling and simulation promise precise control, pattern discovery, transformative insights, and advancing plasma science and technology. Open data access and collaboration are pivotal for breakthroughs [141].