4 Examples of Computer Vision and NLP in Healthcare
Artificial intelligence is transforming healthcare. AI healthcare companies are using machine learning algorithms, computer vision and NLP in their healthcare technologies to understand everything from drug chemistry to genetic markers. They’re offering online consultations using predictive analytics, and they’re incorporating test results and sensor data to give real-time patient status updates to medical practitioners.
The most exciting areas for AI in healthcare, are around computer vision and natural language processing (NLP). Recently, we had the opportunity to attend and exhibit at the ReWork Deep Learning Summit and Deep Learning in Healthcare event in London at the end of September 2018. While we were there, we heard presentations from Mark Gooding, Miranda Medical; Sarak Culkin, NHS; Ahmed Serag, Phillips; and Trevor Back, DeepMind Health.
We were impressed with the real current applications of computer vision and natural language processing in healthcare. What the presenters shared made us even more excited for the near future where how computer vision and NLP ing an increasingly important role in helping doctors, patients, and researchers alike discover and fight disease and injury. In this article, we’ll share the top current healthcare applications of computer vision and NLP and what you can expect in the near future.
AI is good at identifying patterns, making predictions, and analysing complex situations. Healthcare is perhaps the ultimate combination of those three disciplines. Well implemented AI algorithms can literally save lives when they help a doctor notice something, point out a mistake, improve drug delivery, or help train medical experts. Natural language processing and computer vision are the cutting edge of AI with the greatest potential in healthcare.
NLP helps computers interpret and respond to human language. Aside from visual observation, one of the key inputs a doctor relies on to make a diagnosis or narrow down possibilities is the patient’s description of their symptoms, therefore Natural Language Processing in Healthcare can have major benefits. If NLP algorithms can help with initial screening questions, doctors can spend less time triaging and asking background information. Instead, they can get right to ordering tests and investigating specific concerns.
Healthcare also relies heavily on various types of images and scans for everything from diagnosis to new drug discovery, this is where Computer Vision in Healthcare comes into its own. Often, these images are grainy, hard to distinguish, or require recognising very small, specific patterns. Computers can assist and often exceed human capabilities in these types of image analysis tasks. Using computer vision in healthcare, this artificial intelligence technology can help doctors and researchers get faster, more accurate results from tests, scans, and screenings.
Computer vision has shown major promise is in identifying cancerous cells and tumours from images and biopsy results.
So far, the biggest breakthroughs have come in dermatology, where a computer can analyse an image of a person’s skin much more quickly and thoroughly than a dermatologist doing an in-person exam. Recently, computer vision algorithms have proven themselves more effective at identifying potential skin cancer tumours than doctors.
Similar breakthroughs have come in the field of breast cancer screenings. Computer vision can be applied to mammogram images to accurately identify tumors in the breast. Moreover, lung CT scan images processed through computer vision algorithms have shown promise at identifying lung cancer, as well.
One of the presenters we saw at ReWork, from DeepMind Health, shared some of the success they’ve had identifying head and neck cancer in collaboration with the Radiotherapy Department at University College London Hospitals.
