# Mapping How Artificial Intelligence Blends with Healthcare: Insights from a Bibliometric Analysis
## 4 Discussion
### Advanced Medical Imaging and Diagnosis Systems: Algorithms and Automation
[98] introduced an algorithm that greatly improved the automatic recognition of the common carotid artery in longitudinal ultrasound B-mode scans, showcasing a move away from the labor-intensive manual processes of the past. This was indicative of the burgeoning trend towards automation within medical imaging analysis. Complementing this trend, [PERSON] et al. [99] made a parallel leap in the same year with their development of a method for automatic computed tomography (CT)-ultrasound registration for diagnostic imaging and image-guided interventions. Their system, which utilized a novel real-time simulation of medical ultrasound from CT data coupled with a robust similarity measure, enabled the alignment of 3D ultrasound sweeps with corresponding tomographic modalities without the need for manual input. The period between 2011 and the COVID-19 outbreak was characterized by significant improvements and the emergence of new technologies that further propelled the capabilities of AI in medicine, refining the accuracy and efficiency of diagnostic processes. For instance, in 2011, [PERSON] et al. [100] introduced an innovative, fully automated system for the segmentation of bones from knee MRI images, which was particularly impactful for osteoarthritis research. Their system utilized graph-cut-based segmentation algorithms to identify imaging biomarkers for this debilitating joint disease, which demonstrated the potential of AI in automating and improving the diagnostic workflow. [PERSON] et al. [101] in the same year made strides in the field with their development of a surface-region context in optimal multi-object graph-based segmentation for the robust delineation of pulmonary tumors, enhancing the precision of lung cancer treatment planning. Furthermore, by 2014, [PERSON] et al. [102] had contributed significantly to the field of content-based image retrieval systems for 3D medical datasets. Their work demonstrated the potential for such systems to provide a rapid and accurate retrieval of medical images, aiding radiologists in their diagnostic tasks and enhancing the efficiency of medical workflows. In 2017, [PERSON] et al. [103] showcased the versatility of image-based diagnostic tools with their deployment on Android devices for plant species identification based on leaf images, suggesting potential applications of medical image-based diagnostic tools beyond traditional healthcare settings. During the COVID-19 pandemic, the application of AI in medical imaging and diagnosis saw significant innovation and deployment, as detailed in the works of [PERSON] et al. [104] and [PERSON] et al. [105]. The rapid spread of the virus and the urgent need for efficient diagnostic protocols meant that traditional methods needed to be augmented with smarter, faster technology. [PERSON] et al. [104] discuss the integration of smart city technologies to monitor and control the epidemic spread, harnessing the power of the Internet of Things (IoT) to quickly diagnose COVID-19, thereby reducing human-to-human interaction, and enhancing response times to contain outbreaks. [PERSON] et al. [105] highlight the potential of AI-driven segmentation methods, particularly for acute ischemic stroke lesions on non-contrast CT scans, which may have parallels in imaging techniques for viral infections, showing how AI helped to address the challenges of low contrast and artifacts common in rapid, high-volume testing environments. In the contemporary post-pandemic landscape, research by [PERSON] et al. (2016) and [PERSON] et al. (2017), among others, has shown a trend towards more integrated, intelligent, and user-centric medical diagnostic systems. [PERSON] et al. presented the CARTIER-IA platform, which enhances the usability of medical data management by integrating various types of data and enabling the application of AI algorithms through a web application, catering to diverse roles in medical research and practice. [PERSON] et al.