## 4 Discussion
### Summary of Evidence
However, the determination of the most appropriate prediction horizon should be a collaborative effort involving clinicians, researchers and relevant stakeholders, to ensure that it considers both the clinical needs and the practical realities of healthcare delivery. In addition to assessing the methods and outcomes of the reviewed studies, an additional analysis was conducted to evaluate the clinical relevance of the different models. A significant concern arises from the lack of emphasis on clinical intelligibility in research efforts. Considering the potential value it holds, it is reasonable to suggest machine learning will play a larger role in healthcare. As a result, establishing trust through intelligibility in the initial phase (we suggest the idea that intelligibility becomes of less importance as confidence increases, similarly to how we rarely question the directions generated by maps on our phone) between machine learning products and healthcare professionals becomes crucial, in order to encourage adoption and leverage possible potential benefits. Within this context, it is crucial to consider the trade-off between the slightly lower performance yet higher interpretability offered by white box models versus the potential for higher performance but reduced interpretability associated with black box models. This trade-off is evident in this review, where [PERSON] et al. employed an LSTM model, considered a black box model, while [PERSON] et al. utilized a white box approach using Logistic Regression. White box models refer to models that provide a clear understanding of how they arrive at their predictions. These models are often based on well-defined rules that can be easily interpreted by humans. In the case of predicting ICP crises, white box models may offer valuable insights into the decision making process, allowing clinicians to understand the variables that contribute most significantly to the predictions. The greater interpretability can cultivate confidence, aiding in the collaboration between clinicians and machine learning tools. Conversely, black box models may provide greater prediction accuracy; however, their internal workings are often opaque, making it challenging to understand why a particular prediction was made. Given this lack of interpretability, in a clinical setting --where decisions hold vital implications for patients--clinicians may hesitate to adopt tools leveraging black box models if the reasoning behind the predictions is unclear. It is therefore essential to strike a balance between model performance and interpretability in a clinical setting. White box models can offer a clearer understanding of the decision making process, but they may sacrifice some predictive performance compared to black box models. While the reviewed studies showcased the effectiveness of machine learning algorithms in predicting ICP crises, there is a paucity of investigation into the interpretability of these algorithms. Ensuring clinical intelligibility should be a priority in research endeavours, to maximise trust and increase the possibility of effective use in real-world scenarios.