Computer Vision’s Rising Role in Healthcare
Computer vision, a branch of artificial intelligence, mimics human sight to understand and interpret visual data. Unlike traditional AI, it processes images and videos, extracting information to perform specific tasks. This technology is rapidly reshaping how healthcare professionals deliver patient care.
The healthcare market for computer vision is on a steep growth trajectory. Projections from Precedence Research estimate a surge from $2.6 billion in 2024 to over $53 billion by 2034. But, like all AI, the reliability of computer vision hinges on the accuracy of the training data. Organizations must prioritize rigorous data quality and thorough testing to ensure dependable results.
Despite the need for careful implementation, experts anticipate computer vision will significantly enhance care quality and tackle persistent challenges like increasing patient loads and staff shortages. “Sight is our most powerful sensory capability, with up to 90% of our brains directly or indirectly participating in the processing of visual information. Similarly, computer vision is the most valuable form of AI-enabled perception,” says Dr. Andrew Gostine, CEO of Artisight and a critical care anesthesiologist. “High-bandwidth image processing with computer vision is the only way to drive healthcare automation at the scale required to fix many of healthcare’s access and efficiency problems.”
How Computer Vision AI Works in Healthcare
Computer vision AI is designed to analyze and react to situations much like a human clinician would. The technology’s potential applications span from improved patient monitoring and early disease detection to enhanced surgical precision. Computer systems are less prone to errors than humans, offering a consistent level of accuracy. However, practical application is crucial; “It’s important to recognize these tools aren’t magic wands or silver bullets,” cautions Dr. Christopher Longhurst, chief clinical and innovation officer at UC San Diego Health and executive director of the Jacobs Center for Health Innovation. “They only deliver outcomes when people use them effectively in workflows to deliver better patient care.”
Key Applications of Computer Vision in Healthcare
Medical images, diagnostics, surgical assistance, and real-time monitoring show computer vision’s wide array of applications.
Medical Images and Diagnostics
Radiology departments are at the forefront of adopting computer vision AI, using it to analyze medical images and identify anomalies more quickly. This leads to earlier interventions and better patient outcomes. Dr. Longhurst recounts how this technology helped UC San Diego Health identify COVID-19 pneumonia cases during the pandemic, even when patients had yet to display respiratory symptoms. The AI flagged a potential infection on a chest X-ray, prompting a COVID-19 test. “The AI helped make the diagnosis much earlier, and the patient was treated and went home without needing critical care.” Longhurst also notes that computer vision AI helps prioritize high-risk exams, pointing out its ability to identify potential strokes and expedite radiologist reviews.
Surgical Precision and Assistance
AI cameras are augmenting surgeons in minimally invasive procedures. The technology assists in identifying critical anatomy and tracking the movement of surgical tools. Computer vision is sometimes used to verify all surgical materials, such as sponges, are removed before closing the incision. Gostine notes that cameras can allow specialists to participate remotely in complex surgeries. “We put the hardware in the operating room and stream the video and audio feeds to a control desk. This reduces friction in communication,” says Gostine. “We then turn on the computer vision algorithms to drive data capture for use cases ranging from OR efficiency and waste reductions to quality improvement and patient safety.”
Real-Time Monitoring
Computer vision can also enhance patient monitoring and prevent issues such as falls, a significant cost driver in the healthcare system. Artisight’s Patient Room solution, for example, can detect when a patient attempts to leave their bed. The system sends automatic alerts and allows a ‘virtual nurse’ to communicate with the patient. “As a physician, I can maximally treat maybe 2,000 patients per year, but you can train an algorithm on hundreds of millions of patient encounters,” Gostine says. “It’s an incredible amount of insight built into a camera that costs less than a dollar per day.”
Considerations for Implementation
Healthcare organizations should aim to integrate computer vision AI into existing workflows for maximum efficiency. “Tools that are workflow-integrated usually perform the best,” says Longhurst. “That may mean integrating with the electronic health record, or with the PAC system for radiology imaging.” Also critical is the way the AI algorithm is trained. Gostine emphasizes the importance of real-world training environments over simulations. He explains that training computer vision AI in a real hospital allows the system to learn and adapt to the nuances of live clinical environments, and this approach also involves adhering to heightened patient privacy standards.
The Future of Computer Vision in Healthcare
Gostine is optimistic about the future capabilities of computer vision AI. He predicts a “one-thousand-fold increase in the intelligence of these algorithms as the amount of compute rises,” over the next eight years or less, foreseeing “computer vision…going to be the general-purpose sensor for almost every aspect of health care.” Longhurst expects expanding AI use in diagnostics, especially in fields such as ophthalmology and dermatology. “I think AI will have a bigger impact on healthcare than anything we’ve seen since antibiotics, but there needs to be more testing to measure outcomes and determine how the technology works in different environments,” Longhurst says. “Even if we’re using computer vision AI as clinical decision support, ultimately hospitals and doctors are still accountable for delivering standard of care.”
