**5. Limitations and challenges of AI on intracranial aneurysm**

The use of artificial intelligence in the analysis of intracranial aneurysms has been expanding rapidly. While numerous algorithms and techniques have been developed for managing these aneurysms, certain challenges and limitations must be addressed.

Kim and colleagues [51] suggested certain standards to assess the clinical effectiveness of AI algorithms. These include obtaining external validation, conducting a diagnostic cohort study, involving multiple institutions, and performing prospective studies. However, most of the studies on AI in managing intracranial aneurysms lack external validation and are retrospectively designed, which can lead to selection bias and variability. To achieve reliable results, it is necessary to conduct prospective studies and externally validate the available algorithms for their clinical feasibility [5].

DL-based algorithms have exhibited positive outcomes, along with other AI techniques. However, the time taken to train them and their cost-effectiveness are still questionable. The intricate structure of neural network algorithms poses a challenge known as the "black box" problem, where the process of data processing within the layers is not completely understood. This leads to skepticism regarding the results generated from a "black box" [19].

In addition, these systems may introduce new kinds of errors, particularly automation bias, which is defined as the inclination to use automated cues as a substitute for vigilant information-seeking and processing [52]. Automation bias has been highlighted as one of the potential drawbacks and ethical issues of AI-based applications. It reflects the dependence of the user on the machine, ignoring the contradictory information that may exist without automation, leading to decreased self-confidence and loss of human input [5].

Nowadays, a legal consensus is lacking regarding AI regulations, and no clear guidelines are available regarding the independent mathematical interrogation and validation of outputs generated by AI systems [52].

### **6. Future perspectives**

AI has promising potential in the management of intracranial aneurysms in the future, including prescreening triage systems for emergency medicine physicians to prioritize high-risk patients, automated detection and intelligent outcome prediction, prediction of treatment strategies during follow-up, automated detection of recurrence after treatment, and prediction of rupture risk [1].

For an AI tool to effectively manage aneurysms, it must accurately identify true-positive cases with high confidence. This level of reliability can only be achieved through a significant number of annotated imaging studies, which are necessary before the tool can be widely implemented in real-world scenarios [53].

### **7. Conclusions**

In conclusion, the use of artificial intelligence in managing intracranial aneurysms offers higher accuracy and efficacy than manual measurements and can potentially augment the clinical performance of radiologists and shorten interpretation time. While some studies need to be validated in a clinical setting, AI-based applications should be viewed as a tool to assist and not replace human decisionmaking in health care. Although implementing new technology may initially be costly, the long-term cost-effectiveness of AI can potentially reduce the cost of unnecessary diagnostic testing. Further studies are required to explore other AI applications in intracranial aneurysms and to validate the findings in a real-world clinical setting.

### **Acknowledgements**

This work would not have been possible without the financial support of Fundación Amigos de la Juventud, A.C. (FUNDAJU).

*The Use of Artificial Intelligence in the Management of Intracranial Aneurysms DOI: http://dx.doi.org/10.5772/intechopen.110772*
