Technology is driving huge disruption and innovation within healthcare. Big data, machine learning, artificial intelligence led applications are benefiting the ways in which medical professionals diagnose and treat patients.
Google Brain has recently made a significant breakthrough in computer vision. In 2011, the error rate was 26% for computers analyzing images, but now it’s been revealed that computers are able to recognize and analyze images better than humans, with an error rate of only 3% compared to a 5% error rate of humans. This opens up huge opportunities for this technology in an array of sectors including healthcare. Medical imaging accounts for a huge part of medicine, from image registration and annotation, image-guided therapy through to computer diagnostics.
Unfortunately, human error is no stranger to the healthcare sector and can put lives at risk. Whilst medical professions strive to deliver the best care they can, is the time for AI - led healthcare here? And is computer vision really capable of providing more thorough results than doctors?
Accenture’s recent report (snippet above) suggests that the AI health market size is set to grow to a huge $6.6billion by 2021, at an annual rate of 40%. This growth is enormous and signals a radical change in healthcare that will ultimately benefit the lives of millions.
Computer vision in healthcare is playing a vital role in medical image analysis, allowing for noninvasive diagnosis of many conditions and diseases and other areas such as image-guided radiotherapy and even medication adherence using facial recognition.
We’re seeing massive leaps in diagnosis through computer vision. Pathologists diagnose cancers through using slides and tests to detect the presence of cancerous tumors, however, research has shown that the error rates for oncology are between 1 - 5%, and as reported in Silicon Angle, pathologists only agree on some cancer diagnoses 42% of the time. It’s been shown that when using technologies such as computer vision and deep learning that successful diagnoses have higher accuracy, Google Brain reached almost 90% success rate at diagnosing cancer and it’s also been seen in skin cancer detection with IBM’s Watson, which achieved a success rate of 91% earlier this year. Nvidia has also had success in deep learning assisted pathology in the past too, in which their team managed to identify cancer 92% of the time, just below the human pathologists at 96% success rate in the project. When used together, both human and deep learning, they achieved a 99.5% success rate. This clearly cements the much-needed presence of AI in diagnostics.
When applied to real-world medical treatment, the application of this technology will undoubtedly aid faster diagnoses and more effective treatments for patients. Right now, especially in the UK, we’re seeing immense pressure put on our health services. Long waiting times for appointments and for the following results are unfortunately a norm for many. AI-led technology is set to eliminate these often fatal waits as it is expected that diseases such as cancer will soon be diagnosed within a matter of minutes with the use of computer vision and 3D imaging.
AI-led diagnoses will constantly be improving, machine learning and pattern recognition systems will get more powerful and faster at processing images. With the pairing of medical practitioners’ input, this will have a significant impact on global healthcare systems.
Matt Reaney, Founder of Big Cloud