AI and advanced machine learning promise to reinvent healthcare, a sea change heralded by numerous AI-driven innovations, pilot projects, products, and techniques. In fact, the use of AI in the life sciences sector is projected to explode from $600M in 2014 to over $6.5B by 2021. While Mark Zuckerberg, Elon Musk, and others may argue about AI’s ethical aspects, the innovations continue apace.
Here’s a quick view of some of the cutting-edge AI activities Cognizant is involved with in the life sciences space.
- Skin cancer detection: Cognizant has developed an AI-powered system that can analyze skin images and detect irregularities. By comparing a patient’s imaging results with a series of pre-stored images, the system helps to quickly and cost-effectively identify skin disorders. In our pilot trial, which covered five different disease tracks, the rate of detection accuracy was a little over 73%, thereby demonstrating great potential as a diagnostic aid.
- Neurological disorders: Fans of the new CBS series Bull have seen how reading micro-expressions can provide insights into understanding human behavior and be used to trigger specific reactions. But while legal dramas make for fine entertainment, an even more fascinating prospect is the neurological application of micro-expression detection to identify early signs of health issues. We’ve built a system that can detect tiny variations in facial expressions, gait patterns, and tremors by analyzing videos of individuals and detecting regions of interest in single video frames. The system compares the key areas underlying human facial expressions (e.g. eyebrow and chin movements, lips and nose position) against a large store of pre-fed images that correspond with a wide range of human expressions. From this, the system makes logical deductions about an individual’s neurological condition. While not yet an industrialized solution, the system has the potential to go far beyond human powers of discernment.
- Diabetic retinopathy (DR): DR is one of the major causes of blindness today, with nearly 422 million people at risk. Over time, elevated blood sugars can affect the retinas of patients with diabetes, causing anomalies and abnormal growth in the blood vessels. We’ve built a screening system that applies a combination of neural networks and machine learning. The system enables medical specialists to compare pictures of a diabetic patient’s retina to images of a healthy retina, and thus helping the doctor determine the presence or severity of DR. We are working to improve the accuracy of this automated DR screening method. Although our application delivered strong results during the pilot stage, the system cannot yet predict disease severity as accurately as an ophthalmologist can. However, its current level of accuracy is high enough for the system to assist ophthalmologists in diagnosis and then match solutions to the disease state.
- Oral cancer prescreening: Determining a patient’s likelihood of developing oral cancer generally requires consideration of a vast array of predictive variables – lifestyle, family history, etc. Even then, it is impossible to predict who will develop oral cancer and who will not. This is where machine learning comes in. Cognizant is in the early stages of applying our knowledge of image analytics coupled with risk-factor scores to assess risk levels for contracting oral cancer.
In Healthcare, AI Extends Human Touch
With all these advancements, there is a common misconception that embracing AI means removing the human touch from healthcare. On the contrary, AI’s uncanny ability to suggest the presence of human consciousness (Amazon’s suggestions are often right on the money, aren’t they?) makes it a welcome adjunct to the humans providing medical treatment. Far from replacing healthcare personnel, AI will imbue the human–technology healthcare continuum with a responsive quality. Ironically, AI and machine learning will ultimately offer the warm and compassionate care that patients seek, thereby boosting health outcomes.
Further, AI will play a vital role in reducing medical errors and misdiagnosis. Andrew Beck, Associate Professor of Pathology at Harvard Medical School, has shown that AI can reduce misdiagnosis by up to 85% in the case of cancer detection – a huge margin. In the absence of a specialist, AI can also empower general physicians by helping them choose the right treatment and supervise patients. By doing so, AI can enable healthcare professionals to focus more on what matters most: providing the best treatment.
Of course, AI also introduces some level of risk and challenge that can’t be overlooked. As a conservative and regulated sector, healthcare will require more time to adapt to AI- and ML-assisted care. Another big challenge is collecting the right data that will lead to the best decisions. The benefits of AI in healthcare, however, far outweigh the risks and challenges involved. In a world that’s in dire need of lower cost, higher-quality, and more accessible and personalized healthcare, it will soon be considered medical malpractice not to consider AI.