# Mapping How Artificial Intelligence Blends with Healthcare: Insights from a Bibliometric Analysis
## 4 Discussion
### Advanced Medical Imaging and Diagnosis Systems: Algorithms and Automation
The development of the VIA-RAD system, as documented by [PERSON] [91] in 1995, exemplifies the evolution of diagnostic radiology tools. VIA-RAD, a blackboard-based system, integrated computer-displayed radiological images with cooperative computerized assistance for decision-making, representing an advanced version of the image interpretation systems first introduced in the previous decade. This system utilized extensive data collection and cognitive modeling to improve the interaction between perception and problem-solving in radiological assessments, indicating a sophisticated leap forward in how AI could enhance the accuracy and efficiency of radiology departments. In the same vein, significant progress was made in the field of neuroimaging. The research by [PERSON] et al. [92], on a model-based assessment of lung structures, showcased the use of a sophisticated inferencing and control system that identified major lung structures from medical images. This technology advanced the applications seen in earlier MRI innovations like PUPA [89], by adding a level of automated feature recognition and diagnostic support that was not previously available. During this decade, new advancements further refined diagnostic capabilities in medicine. One notable development was the QUAWDS system, introduced by [PERSON] et al. [93], which utilized advanced pattern recognition and an abductive hypothesis assembler to analyze human gait dynamically, marking a shift from traditional static image analysis to more complex, motion-oriented diagnostics. Also, [PERSON] et al. [94] further advanced the field by developing an intelligent interactive segmentation method that employed a piece-wise deformable model for analyzing complex medical images, such as those used in diagnosing osteoarthritiskles. This method required minimal user intervention and significantly improved the efficiency and accuracy of segmenting intricate anatomical structures, demonstrating the decade's push towards more sophisticated and user-friendly diagnostic tools. Continuing into the 2000s, the integration of AI in medical imaging and diagnostics further evolved with groundbreaking advancements that built upon the technological momentum of the previous decades. In the realm of medical imaging, we observed a leap forward with the work of [PERSON] and [PERSON] [95] in 2004, who developed an object segmentation algorithm that utilized an extended gradient vector flow field model. This technology advanced the image segmentation processes that were fundamental in the 1980s and 1990s by introducing a system that required no human interaction, signifying a substantial progression toward full automation in medical image analysis. Another key development was the system presented by [PERSON] et al. [96] for the segmentation of thrombus in abdominal aortic aneurysms from computed tomography angiography scans. This system showcased a novel application of nonparametric statistical grey level modeling in the medical imaging domain, a marked advancement from the texture analysis and pattern recognition methodologies that began emerging in the 1990s. It represented a refined approach to dealing with the complexity of medical image data by providing a robust automated segmentation with minimal user input. Progress in diagnostic capabilities was also evident in the improved understanding of image data. The Medical Imaging Interaction Toolkit, introduced by [PERSON] et al. [97], extended beyond the algorithmic capacities of medical imaging to include interaction and visualization. This toolkit was a notable enhancement over the foundational imaging technologies from previous decades as it integrated algorithms with visualization, allowing for more interactive applications in medical image analysis. Furthermore, the period saw significant advancements in ultrasound imaging technology, both in methodology and application. In 2008, [PERSON] et al.