The artificial Intelligence-driven healthcare innovations market is currently at $6.9 billion and projected to grow to;$67.4 billion by 2028. One area of AI, Computer Vision (Computer Vision) or visual-AI is rapidly changing healthcare engineering. In the following segments let us take an in-depth look at this emerging technology spectrum. Explore the ways it is altering established axioms of medical diagnostics, drug therapy programs, and changes in protocols in advanced research.

We begin with identifying applications that use computer vision…

What are “Computer Vision” applications?

Computer Vision is bringing new solutions that AI-development providers are fast adapting for everyday use across the healthcare industry.

The purpose of Computer Vision -driven solutions…

• Cut down costs as healthcare becomes more expensive
• Cost cuts can be achieved only with safe, secure care delivery and clinical outcomes
• Technology-driven products deliver the needed patient care
• Analysis of complex medical images of patients
• Support clinicians

Applications that run on Computer Vision are automated human-dependent Medical Imaging analysis, to drive consistency, accuracy into most healthcare services such as Diagnostics, enhanced Cancer Screening and Surgery, Clinical Trial Retention, and Training, apart from advanced Research Activities.

While these are just a few of the key areas Computer Vision is being used, the science behind this powerful technology should be understood to discover the true impact of this medical engineering area.

Computer Vision is automated “seeing”!

Computer Vision (CV) is a specialized form of Artificial Intelligence (AI), where machines are trained to “see” images and videos faster than humans and be more accurate than specialists like medical practitioners. Where AI makes a computer think, Computer Visionmakes the computer see, observe and understand contexts.

How does Computer vision work?

Computer Vision capability is built by iterative procedures (Machine Language algorithmic models for teaching context of visual data, Convolutional Neural Networks(CNN) for pixel-based image tagging-labeling, Recurrent Neural Network (RNN) for video applications) to teach the system to identify anomalies in a given dataset of images for diagnostic conclusions.

Computer Vision used in the healthcare process begins with Acquiring Image, Processing Image Data, concluding with Extracting Multidimensional Data. By the end of the process, the “pattern” in the data is identified for different objects and trends in the anomaly images provided.

Science behind Computer Vision

Computer vision works in the same way as humans’ sight, identifying an object by its context. Computer vision trains machines to perform functions within biological parameters such as the visual cortex, optic nerves through inspection of products and analyzing millions of processes, differentiation of images, and recognizing imperceptible defects and issues far greater and faster than human capabilities. 

Providers of Computer Vision solutions are able to add analytic capabilities to healthcare applications. The most popular Computer Vision-driven applications are:

1. Image Analysis: Accuracy and efficiency

• Traditional medical imaging applications can be enhanced
• 2D scan images can be converted into interactive 3D for practitioners to interpret the condition of patients accurately

2. Improving Diagnostic Capabilities

• During diagnosis, visual AI is a powerful replacement for invasive surgeries
• Software installed on MRI can generate 3D images of organs for accurate detection of abnormalities not visible to normal eyesight

3. Medical Processes become efficient

• Lowers workloads by over 88%
• Time taken for analysis of images and reports is minimized

4. Cancer Screening using Computer Vision helps in

• Detection of cancer, especially in the early stages
• Computer Vision applications can differentiate cancerous and non-cancerous lesions in skin cancer  
• Computer Vision discerns cancer, based on ML patterns for timely detection and cure treatment

5. Screening for disease progress

• Computer Vision applications are very useful in the detection of progression of a number of diseases
• Patient’s previous reports are researched to identify the progress of some of the diseases
• Response to particular medications is also detected
• Researching and identifying patterns or trends is simplified and accurate

6. Clinical Trial Tracking

• Computer Vision becomes a sophisticated tracking system during clinical trials
• App-based facial recognition technology allows detection of the participant taking the medication
• Reminds participants to take their medication as per the scheduled time

7. Advance medical research

• Health organizations are leveraging Computer Vision or Visual AI for medical research

• Cell counting and typing is faster and accurate by 100x times 
• Aids in eliminating user bias
• Eliminates inaccuracies in results
• Fast-tracks research activities goading faster and cheaper medications

8. Computer Vision simulation Training

• Critical training of critical surgeries are effective due to immersive and interactive simulations of surgeries
• Anonymized videos of surgeries
• Uploading to the cloud
• The software analyzes, compare and quickens time for medical procedures
• Health Monitoring using Computer Vision
• Doctors are analyzing health and fitness metrics using Computer Vision
• Faster and enhanced medical decision making is possible
• Blood lost in surgeries or in C-section procedures is measured using apps. Emergency measures are rapidly deployed based on the loss of blood quantity and hence protecting critical obstetric and natal care.

        

Artificial intelligence applications are driving healthcare engineering - Machine pattern recognition for accurate reading of medical reports, identifying skin lesions in mammograms, small polyps when doing colonoscopy; synthesizing notes by using natural language processing and speech synthesis - has evolved substantially in the past decade. While artificial intelligence enables computers to think computer vision brings the ability to see, observe and understand. This is brought about by enabling computers and systems to derive meaningful information from digital inputs such as images videos and other visual stimuli.  

Thus Computer Vision drives capabilities to take action and make a recommendation based on the visual inputs.

Why should you use Computer Vision systems?

Computer Vision is differentiating the crowded healthcare industry. Computer Vision is overcoming the traditional limitations of ‘medical professional’-dependent imaging analysis of patient data, by automating the “visual” analysis. It adds accuracy, speed, and cost-effectiveness to healthcare solutions. It combines the human-like multi-sensory interpretation of contexts with powerful computing for accurate, consistent healthcare delivery systems.

Hence, when your healthcare solutions platform leverages Computer Vision tech, market domination becomes organic and extensive. Reach us at drive-analytics to help you develop AI-first solutions for your organization.