The healthcare industry is increasingly adopting computer vision for a variety of applications, from protecting public health to advanced medical imaging analysis, from precise tracking of medical procedures to accelerating research. The adoption of AI for healthcare is a drive to improve human outcomes by replicating visual recognition with AI to identify objects, images and actions. Now, the speed, accuracy and flexibility of the Chooch AI Platform delivers improved results across a growing array of applications and deployment options.
How are healthcare organizations using computer vision to improve their internal processes and patient care? Keep reading to learn how this subfield of artificial intelligence is revolutionizing the healthcare industry.
Computer vision seeks to help computers “see” images and videos at a high level, in a way that approaches human understanding. In recent years, computer vision has made significant advances thanks to developments in the domains of machine learning and deep learning.
Often the first assumption about the confluence of healthcare and AI is medical imaging. It’s true that AI can identify objects in medical imaging far more efficiently than people, but there are more than simply diagnostic computer vision healthcare applications.
Artificial intelligence has radically transformed a wide variety of industries, and healthcare is no exception. According to a 2020 survey by KPMG, more than half of healthcare executives say that their industry is ahead of the curve when it comes to AI. What’s more, roughly 90 percent say that AI is already showing advantages such as improving efficiencies and increasing patient access to care.
Healthcare AI offers the possibility to improve, streamline, and automate both administrative and clinical tasks, reaping benefits for the entire practice. IBM, for example, is currently using its Watson supercomputer for multiple research projects to address healthcare challenges, including cancer care, diabetes management, and faster discovery of new pharmaceuticals.
In the past decade, technological advances in deep neural networks (and the GPUs on which they run) have enabled massive breakthroughs in computers’ ability to do object recognition tasks.
The potential applications of computer vision for healthcare AI extend beyond diagnosis. For example, computer vision can be used to implement facial recognition systems in a healthcare setting. Doctors, nurses, and other medical staff can scan their face to enter a restricted area of the hospital, while patients can verify their identity and avoid cases of misrecognition, which can lead to many adverse outcomes. Computer vision systems can also monitor security videos to enforce compliance with healthcare safety protocols (e.g. wearing masks and washing hands).
When it comes to diagnoses, many computer vision models are now able to equal or even beat the performance of human medical professionals. In the past few years alone we’ve seen:
The benefits of computer vision for healthcare AI lie not just in its accuracy, but in its rapidity. The Association of American Medical Colleges (AAMC) predicts that by 2033, there will be a shortage of between 50,000 and 140,000 physicians in the U.S. By processing medical images and data at speeds that are impossible for humans to achieve, computer vision systems help healthcare organizations become dramatically more efficient. Computers can quickly identify straightforward cases so that doctors can begin the appropriate treatments, while more uncertain cases can be flagged for human review.
Computer vision remains a highly technical topic that requires deep knowledge and expertise. So how can healthcare leaders successfully bring computer vision into their organizations?
Chooch is here to help. We offer a user-friendly, all-in-one AI platform that enables fast and flexible deployment of AI models. Ready to get started? Check out our page of valuable computer vision resources, and learn how you can start applying computer vision or request a demo to get a conversation started.