When we talk about Computer Vision, we are indeed referring to a specialized realm within the field of artificial intelligence and machine learning. It empowers computers to understand, interpret, and interact with the visual world. Computer Vision plays a significant role in various industries, and the healthcare sector is no exception.

In recent years, there has been massive growth in the application of computer vision technology in medical imaging, offering immense potential for diagnosing diseases, planning surgeries, and managing patient's health. With the capacity to process high-quality images and videos swiftly and precisely, computer vision for medical imaging steps up to dramatically improve accuracy and efficiency in healthcare.

• Focused Precision: Computer vision technology achieves impressive precision in processing and analyzing medical images, much necessary for identifying specific disease markers.
• Automated Analysis: Given the large volumes of imaging data generated in healthcare settings today, automated analysis by computer vision solutions allows for faster and more efficient processing, a crucial edge in cases requiring urgent medical attention.
• Objectivity: Unlike human practitioners who can be influenced by multiple factors, computer vision technology delivers a consistent, objective analysis, thereby reducing the scope of diagnostic errors.
• Availability: Medical professionals can leverage computer vision technology across different platforms and devices, including those employed for telemedicine.
• Advancements: Continuous research in the AI community leads to progressive improvements and innovations in computer vision technology.

• Computer Vision Improved Diagnostic Accuracy: Many healthcare organizations are finding computer vision indispensable because of its ability to analyze vast amounts of medical imaging data with high accuracy and speed. It can aid in the early detection of diseases like cancer by identifying abnormal growths in medical images, potentially saving lives through early intervention.
• Cost Savings: With computer vision, healthcare organizations can largely automate diagnostic processes, which reduces costs typically associated with manual analysis.
• Real-Time analysis: Computer vision technology can analyze medical images in real-time, providing immediate assistance to medical practitioners during procedures such as surgeries.
• Deep Learning Capabilities: By employing deep learning algorithms, computer vision can continually improve its diagnostic precision by learning from the data it processes.

• Data Privacy: Given the highly confidential nature of medical information, any computer vision system employed in medical imaging must adhere to stringent data privacy norms.
• Intellectual Property: There are continuous debates and discussions regarding the intellectual rights over the algorithms and AI technology employed in computer vision systems in medical imaging.
• Dependency on Quality Data: The accuracy and relevance of the results delivered by computer vision systems greatly depend on the quality of the data.
• Incompatibility and Integration: There might be challenges when it comes to integrating new computer vision technology with existing health system infrastructure.

The initial stage of computer vision implementation in medical imaging involves identifying the specific applications and benchmarking the desired outcomes. This is followed by selecting an appropriate AI platform and training the model using relevant medical imagery. Waiting for 100% accuracy should not delay implementation as constant learning and adaptations will improve the accuracy over time.

Subsequently, it is essential to run pilot tests and validate the results against benchmarks before complete integration into existing systems. A comprehensive training program for health professionals will guarantee the successful use of computer vision technology.

The advent of computer vision in medical imaging has undoubtedly revolutionized healthcare services globally. Although it might pose some complexities and challenges, the extensive benefits offered by this technology establish it as an indispensable tool in enhancing the future of medical imaging.