How computer vision is transforming diagnostics in healthcare
Updated: Jan 3
By offering multiple avenues for transforming healthcare processes, the application of computer vision in healthcare reengineers the diagnostics efficiency, thereby tremendously enhancing patient care and the entire associated healthcare activities.
Artificial Intelligence (AI) continues to evolve in healthcare diagnostics, with the advancements only taking the efficiency of the healthcare performance parameters further. Indeed, the benefits of tech-enabled processes are not just cost-saving, but most importantly, they are life-saving.
Earlier, medical imaging in healthcare predominantly consisted of technologies like x-rays, CT scans, MRI scans and ultrasound technology. However, with time visual AI evolved into computer vision, which, now, has been fast changing the landscape of healthcare diagnostics.
What is computer vision?
A subset of Artificial Intelligence (AI), computer vision interprets complex patterns from digital media – images, videos etc, and offers suitable insight-based recommendations. So, this means that the technology can significantly revolutionize the field of medical imaging, thereby taking the diagnostics in healthcare to the next level.
Why to use AI-based computer vision for diagnostics in healthcare?
By producing multi-dimensional images of body parts with physical irregularities or disorders in quick succession, the AI-based computer vision can optimize physicians’ time.
Since the healthcare industry has already started witnessing a huge inflow of data, having computer vision systems in place can detect and comprehend complex patterns from large volumes of structured as well as unstructured data. This can significantly boost the predictive capabilities in the existing diagnostics framework and improve the efficiency of treatment procedures.
How AI-based computer vision transforms diagnostics in healthcare?
The efficiency of healthcare services is gauged by the precision of diagnostics. Computer vision facilitates healthcare stakeholders to implement regimes for not just improving patient care but also for reducing the financial burden on patients and their families. By reducing the proportion of false positives, as against the traditional imaging techniques, computer vision-led diagnostics optimizes treatment sequences. Below we discuss how computer vision technology can truly act as a transformational agent in healthcare diagnostics.
Advanced image-based diagnostics
By streamlining the entire sequence of object identification, image formation, image tagging, image classification and image segmentation, computer vision can help unearth patterns that denote a critical medical condition.
The technology, therefore, doesn’t just optimize the existing practices rather reengineers them. For instance, while an ordinary CT scan process can have physicians grappling with issues in getting an exact understanding of a disorder, machine vision-enabled CT scan procedures can significantly reduce the time to arrive at the most appropriate diagnostics.
Next-gen multi-dimensional medical imaging
Coupled with the advanced capabilities of deep learning models, AI-based computer vision can seamlessly convert 2D images into highly interactive 3D models, allowing physicians to understand the complexities better and in more detail. Advanced computer vision AI algorithms deploying 3D models fully expose niceties of the object under consideration, offering a comprehensive view.
To give examples, the advancements can be crucial for medical professionals in accurately determining the rate of spread of cancer cells. Or it can enable neurologists to get deepest into understanding how far the tumor has penetrated a patient’s brain. Thus, by giving insights into the subtleties of the disorder, computer vision can enable fruitful scrutiny of a given medical condition.
Improved accuracy of imaging intelligence
Operating over a set of deep learning and machine learning models, computer vision facilitates accurate analytics and intelligence. Thus, critical surgical parameters and procedures can be monitored and tracked by the healthcare stakeholders in real-time. A typical application of AI-based computer vision in surgery involves the determination of blood loss rate during surgeries, allowing surgeons to prevent blood loss beyond thresholds.
Measuring parameters associated with chronic conditions using AI-based computer vision allows physicians to intervene at right time and prescribe the most perfect drug. Similarly, through its advanced technological capabilities, AI-based computer vision enables a search function that can enable medical professionals to refer to historical records quickly.
Automated healthcare reporting
Taking input from images generated by medical imaging technologies like CT Scan and MRI scan, AI-based computer vision easily automates the process to generate requisite reports. Trained over reports generated by different sources, deep learning models can create customized report layouts and feed them with concerned indicative figures.
While imaging technologies are not currently incapable of quantifying parameters based on image analysis, they are yet to attain maturity. And it is AI-based computer vision that can bridge this gap.
Computer vision for healthcare is still in its nascent stage, with only a quantum of its capabilities being currently used. However, giving serious consideration to exploiting the full potential of the technology can significantly benefit healthcare providers in the long run.
By investing quality efforts, healthcare providers can bring in radical changes in their existing medical setups through the application of computer vision. Instead of spending their time building in-house resources and capabilities, healthcare enterprises should seek the assistance of AI specialists to have the edge over the competitors.
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