As a dedicated advocate for the transformative power of artificial intelligence (AI) technology, Susan Sly is deeply passionate about how technological advancements can reshape our world for the better. In terms of using technology to improve healthcare outcomes, computer vision in healthcare – a specific subset of artificial intelligence – is proving to be extremely promising.
Let’s take a closer look at computer vision applications in healthcare, including how Susan contributes to the efforts to promote ongoing innovation.
Deep learning has helped drive several remarkable breakthroughs in computer vision in healthcare, providing an effective means by which computers can accurately interpret visual data.
Whereas traditional machine learning requires human engineering and domain expertise to develop feature extractors and algorithms, deep learning differs.
Deep learning involves computers participating in a type of representation that is learning based on multiple, sequentially organized layers of representations. Essentially, the machine is equipped with raw data, which it then utilizes to create its own representations for pattern recognition.
Deep learning algorithms allow computers to master highly complex functions, thus achieving excellent accuracy regarding image recognition. What’s more, deep learning technology has set the stage for new possibilities in computer vision applications.
When you consider that 90% of all medical data is completely image-based, it makes sense that computer vision can play a pivotal role in healthcare. Medical imaging can significantly benefit from recent advances in computer technology that allows for the practical implementation of computer vision.
Medical computer vision can offer a multitude of advantages in healthcare, such as:
Computer vision has been identified as an effective tool for improving diagnostic accuracy – compared to its highly-trained human counterparts – while simultaneously increasing the speed with which accurate diagnoses can be made. In tandem with AI systems, healthcare professionals can make faster patient health decisions and reduce workloads by 90%.
Whereas a standard operating room requires extensive manual effort for documentation, computer vision systems may be able to reduce – or even completely eliminate – the need for human-directed input. This system can directly observe and document as needed, allowing medical professionals to focus more energy and time on patient care.
Errors in patient identification are not uncommon, though most are caught fairly quickly. However, the results can be serious in cases with a mistaken identity that slipped through the cracks. Computer vision systems can be designed with facial recognition technology, identifying patients within a fraction of a second while adhering to HIPAA compliance regulations.
Workplace injuries are common in the healthcare industry, despite most facilities implementing detailed expectations for safety policies and procedures. Computer vision technology could pinpoint failures in safety practices, such as improperly used equipment, processing failures, and the like.
Another outstanding application of healthcare computer vision is in the field of medical research. For example, faster cell counting and typing and rapid cohort identification are two ways in which computer vision and medical research can be advantageous.
The benefits above create opportunities for better outcomes for providers and patients in the short and longer terms. Some of the “big picture” advantages of computer vision in healthcare applications are reduced insurance costs, decreased labor costs, and improved efficiency in the healthcare sector.
As in many tech-driven fields, computer vision is largely dominated by men. However, as the number of women in STEM is steadily increasing, the technology industry as a whole is beginning to become more diverse.
Industry thought leaders and organizations alike are working to support equality in computer vision and other AI fields. For example, RSIP Vision has produced a series of interviews with women in computer vision, providing a fresh perspective on what it means to be a woman in AI today.
Through her own work in the technology field, Susan Sly is constantly striving to open doors for female innovators.
Through her experiences as a woman leader in tech, Susan Sly has discovered a passion for motivating, helping, and empowering other women in the field.
Susan Sly firmly believes that amazing things can happen when women support and empower other women. As such, she is constantly searching for new ways to provide essential education, tools, resources, and support to women interested in a career in technology.
Learn more about how Susan is helping reshape the tech industry of the future today!
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