Computer vision, the technology enabling machines to interpret and analyze visual data, is rapidly transforming healthcare. According to one estimate from Global Market Insights, the global computer vision in healthcare market will reach $2.4 billion by 2026, up from just $262 million in 2019 – a massive 8x increase in only 7 years.
This growth is being fueled by healthcare providers implementing computer vision to cut costs, enhance patient care, improve outcomes, and leverage data like never before. In this article, we explore the top 7 computer vision use cases that can benefit healthcare in 2024 and beyond.
1. Medical Imaging Analysis
Interpreting medical scans and identifying abnormalities is one of the most valuable current applications of computer vision in healthcare. By analyzing patterns and textures in images, computer vision algorithms can pinpoint potential tumors, lesions, fractures, and other critical findings faster and more accurately than the human eye.
For example, scientists have developed AI systems that can analyze mammograms and detect breast cancers with over 99% accuracy. One study in Nature found an AI model detected breast cancers in mammograms with 100% sensitivity, reducing false negatives, while also reducing false positives by 5.7% compared to human radiologists. With over 264,000 new cases of invasive breast cancer diagnosed in the US annually, AI-enabled screening could save countless lives through early detection.
By automating the analysis of CT scans, ultrasounds, and X-rays for insights human clinicians may miss, computer vision can improve diagnostic confidence, reduce late-stage cancer detection, cut unnecessary biopsies, and get patients on targeted treatment plans sooner. This technology can also quantify changes over time to track disease progression.
The impact goes beyond cancer, with applications like spotting early warning signs of heart disease, predicting stroke risk, and assessing injury extent and healing. For instance, researchers at Mount Sinai hospital developed an AI system called DeepHeart to interpret echocardiogram videos and predict the severity of COVID-19 patients‘ heart conditions.
Overall, computer vision applied to medical imaging analysis promises earlier intervention, improved outcomes, and potentially reduced costs for health systems. According to Accenture, AI has the potential to reduce imaging-related costs by up to 80%.
Sub-Use Cases
Cancer Detection – Identifying cancerous tumors and tracking their progression using X-rays, MRI scans, pathology slides, mammograms, etc. Key players like Paige and Enlitic offer AI-based cancer detection solutions.
Neurological Disorders – Finding early signs of neurological conditions like Alzheimer‘s, multiple sclerosis, and autism spectrum disorder in brain scans. For instance, Oakland University researchers use computer vision for autism detection.
Musculoskeletal Imaging – Diagnosing orthopedic injuries, arthritis, fractures, and more in X-rays, CT scans and MRIs. Applications like Zebra Medical‘s HealthCXR highlighting fractures in chest X-rays.
Emergency Room Prioritization – Quickly identifying critical, life-threatening conditions from scans to prioritize urgent cases. Hospitals like Massachusetts General use AI to triage brain CT scans.
2. Patient Monitoring and Safety
With its ability to unobtrusively analyze video feeds, computer vision shows immense potential for contactless patient monitoring and boosting safety. Computer vision systems can track patient mobility, detect falls or changes in vital signs, and identify high-risk interactions.
For example, thousands of patient falls occur in hospitals every year. The CDC estimates 100,000 to 200,000 falls happen in US hospitals annually. Computer vision technology enables round-the-clock automated fall monitoring without additional staffing costs. Companies like Care Innovations offer computer vision-powered solutions to detect falls and immediately alert nurses.
Computer vision also aids infection control by identifying potentially high-risk interactions between patients, visitors and staff. This allows hospitals to enforce social distancing and isolation protocols. For instance, the Habicus platform uses real-time video analytics to monitor distancing in hospital common areas.
By continuously monitoring patients unobtrusively, this technology promotes safety and wellbeing while optimizing resource utilization. According to Care Innovations, their fall prevention solution decreased falls by 40% while producing savings of over $1 million annually across four hospitals.
3. Robot-Assisted Surgery
In robot-assisted surgery, computer vision gives autonomous surgical robots enhanced perception and navigation capabilities. Computer vision systems can guide surgical robots by tracking tools and anatomical structures. This expands the potential of robotics for highly precise, minimally invasive procedures.
For example, scientists at Johns Hopkins developed an AI system called Smart Tissue Autonomous Robot (STAR) that can differentiate between tissues and continuously adjust its movements during surgery. Researchers have also created an AI model that can identify organs, vessels, and tissues during colonoscopy procedures with over 97% accuracy. This assists navigation and decision making.
By powering smarter surgical robots of the future, computer vision techniques like segmentation, registration, and tracking hold the potential to enhance precision, reduce variability, and expand access to life-saving minimally invasive treatment options.
4. Automated Inventory Management
Managing medical supplies and inventory is a monumental undertaking for hospitals and clinics. Computer vision is streamlining these workflows through automated monitoring of stock levels, product locations, and usage trends.
Instead of error-prone manual counting, video analytics can identify when specific items are low in stock or missing from shelves. This provides visibility into real-time inventory usage so hospitals can optimize reordering. Computer vision can also catch fraudulent product diversion or theft.
For instance, McKesson uses computer vision-enabled robots to automate monitoring of hospital pharmacies. This improves workflow efficiency and maximizes utilization of high-value supplies. According to McKesson, their pharmacy automation solutions have reduced costs by over $300 million annually across US hospitals.
5. Patient Identification
Mistaken patient identities can have catastrophic consequences in healthcare administration and lead to the wrong treatments or procedures being performed. But computer vision enables highly accurate patient identification through facial recognition. This prevents misidentification issues that can jeopardize patient safety.
While manual ID verification is prone to human error, facial recognition matches patients to their medical records quickly and hands-free. Leading healthcare systems like Terrebonne General Medical Center, Cayuga Medical Center, and Houston Methodist Hospital deploy computer vision for watertight patient identification.
As an example, RightPatient‘s facial recognition system is implemented at over 100 healthcare facilities to reinforce patient safety. Their technology identifies patients with 99.9% accuracy and has prevented thousands of medical errors.
6. Clinical Workflow Assistance
Beyond robotic surgery, computer vision is being embedded in clinical settings more broadly to assist with routine tasks and optimize workflows.
"Smart" vision systems can track hygiene compliance, notify staff when supplies are running low, monitor proper equipment use, or guide standard procedures through augmented reality. This simplifies workflows, reduces variability in care delivery, and boosts productivity.
As an example, Philips Healthcare introduced IntelliSpace AI Workflow Suite which harnesses computer vision for workflow orchestration and clinical decision support. Features like bedside object detection and patient / staff tracking enhance efficiency.
Augmedics has also developed xvision, an AR headset for surgeons that integrates patient scans and overlays critical data during procedures. This improves precision and situational awareness. By dynamically assisting clinical workflows, these innovations maximize resources and standardize processes.
7. Accelerating Healthcare Research
In medical research, computer vision enables rapid analysis of images, videos, sensor feeds and tissue samples to accelerate discovery. For instance, AI-powered microscopes count cells and detect minute patterns unnoticeable to humans, facilitating more experiments in less time.
Pathologists also rely on computer vision to quickly analyze tissue morphology for cancer staging and grading. Startups like PathAI and Paige have developed AI-based platforms that can automatically detect cancer cells in tissue slides and estimate their concentration.
According to PathAI’s CEO, their technology can [shortens analysis](https:// Forbes Feb 2019
https://www.forbes.com/sites/leahrosenbaum/2019/02/12/this-startups-ai-promises-earlier-cancer-detection-by-analyzing-pathology-slides/) of prostate cancer biopsy slides from 30 minutes to under 5 minutes. Accelerated tissue review means clinicians can diagnose and prescribe treatment plans faster.
By extracting insights from images quicker than human analysis, computer vision will continue driving breakthroughs in healthcare research and drug development. This technology holds immense potential to unlock medical innovations and cures.
As these 7 high-impact use cases demonstrate, computer vision and AI techniques like machine learning are transforming healthcare by enabling more confident diagnoses, safer treatments, improved patient monitoring, and transformative workflow efficiencies.
Based on current adoption trends, I expect these applications to rapidly proliferate across health systems in the coming 5 years. In particular, "smart" surgical robots and AI-assisted imaging analysis for diseases like cancer will see massive growth.
However, realizing the full potential will require addressing valid concerns around data privacy, security, and algorithmic bias while fostering trust and acceptance among clinicians through training. With thoughtful implementation, computer vision can usher healthcare into a new era of data-driven, personalized and proactive treatment.