## 4 Discussion
### Application of the Predictive Model
\begin{table}
\begin{tabular}{p{34.1 pt}} \hline \hline Challenges & Challenges \\ Clinical: Addressing the complexity and variability of clinical conditions while ensuring patient safety and treatment efficacy. Data: Managing large volumes of heterogeneous data, ensuring integrity, privacy, and protection. \\ Financial: Overcoming budget limitations, maximizing the efficiency of available resources for research and development. Technology: Keeping pace with the rapid evolution of technologies and the integration of new tools and platforms into the research process. \\ General: Addressing ethical and regulatory challenges, ensuring research is conducted responsibly and in compliance with global standards. \\ Regulation: Ensuring adherence to regulatory requirements and standards governing research practices, including data protection laws, ethical guidelines, and industry regulations. \\ FEATURES & Results \\ Progress in data management: Implementation of new methods and technologies to optimize data collection, analysis, and storage. \\ Clinical and medical insights: Generation of new knowledge and understandings that can guide improvements in clinical and medical practices. \\ Technological advancements: Development of new technologies or enhancement of existing ones to address specific research or practical needs. \\ Specific outcomes: Delivery of discoveries or results that address specific research questions, contributing to the body of knowledge in a particular field. \\ Advancements in security: Strengthening security measures, both in terms of data and operational practices, to protect against risks and vulnerabilities. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._was low, with a percentage of 0.0%, while for technological innovation, the model achieved a percentage of 50.0%. This suggests that despite difficulties in precisely attributing some research to a specific category, technological innovation remains a powerful and pervasive driver of progress in healthcare. Figure 4, a key graphical element in the study, displays the logistic regression ROC curves for each of the five target variable classes, providing a visual assessment of the model's ability to correctly classify observations within each specific category by showing the trade-off between true positive rates and false positive rates at various decision thresholds. The area under the ROC curve (AUC) value for each category further evaluates and compares model performance in terms of sensitivity and specificity. To delve deeper into our predictive model's predictions, we utilized the SHAP (SHap-ley Additive exPlanations) algorithm, an advanced tool for interpreting machine-learning model decisions. This algorithm's application has enabled us to understand the impact of each feature on the model's decisions, providing a transparent and easily interpretable view of the dynamics underlying the predictions. The results of this analysis for the five target variable classes are shown in Figure 5, highlighting the contribution of each independent variable to the model's predicted outcome. The SHAP numerical values are represented in Table 8.  
Based on the updated SHAP values provided for each instance and for each column, related to the five topics of our systematic review on the impact of artificial intelligence on healthcare management, we can interpret the results as follows:  
* **Clinical and Medical Insights:** These have a moderate positive impact on COVID-19, Quality, and Resource, with SHAP values of 0.00558636, 0.00117473, and 0.00446783, respectively. This suggests that clinical insights are slightly more influential in research related to COVID-19 and resource management. However, they have a significant negative impact on Technnovation (\(-\)0.0194271), indicating that for technological innovations, other factors may be more relevant.