Why the healthcare industry is hacking graphics technology to power machine intelligence
Artificial intelligence has attracted significant attention recently, and yet many of the most popular examples we’ve seen demonstrating its potential benefits have been esoteric proof-of-concepts, such as mastering chess or finding cat videos on the internet.
While these developments have helped pave the way for further breakthroughs, they’ve also left many people asking where the tangible benefits are and what the era of machine intelligence really means to the real world.
At last, we’re reaching the tipping point where machine intelligence efforts are beginning to move past these preliminary examples into life-changing breakthroughs that can solve heretofore unsolvable problems. Nowhere is this more evident than in healthcare.
Healthcare is one of the most data-rich industries in the world. Record-keeping is an integral practice, and one that’s been made infinitely more accessible as health systems around the world have moved to electronic records. Diagnostic images, X-rays, CT scans, and MRI results are being stored digitally. While all these efforts have been made to reduce cost and increase the ease and effectiveness of patient care, in the era of machine intelligence they now create deep data lakes for analysis. This enables new research and pattern-finding that vastly exceeds the capabilities of human beings.
Healthcare data alone isn’t driving new breakthroughs. In fact, much of this data has been digital for years. However, the algorithms that were used to analyze the data couldn’t be run fast enough to provide valuable information in a timely manner. Now that’s changing owing to an unlikely application of graphics technology.
Graphics Processing Units (GPU) have been traditionally used to render graphics and video. GPUs are used to power everything from TV screens to immersive gaming experiences. However, the healthcare industry is now harnessing the power of these GPUs in machine intelligence applications.
Recent advances in GPU technology have made parallel processing fast, inexpensive, and powerful. Coupled with the expanding open-source software platforms, compute performance can finally keep pace with the needs of highly demanding machine intelligence algorithms. The ability to decipher the mystifying amount of data will have a profound impact on our health and healthcare systems, including the prediction and treatment of diseases.
Machine intelligence platforms are just beginning to prove their value for enhancing preventative medicine and stopping disease before it starts, a vital component of any healthcare strategy. Seven out of 10 deaths among Americans each year are due to chronic diseases (such as cancer and heart disease), and almost one out of every two adults suffer from at least one chronic illness, many of which are preventable.
Researchers have recently created an artificially intelligent diagnosis algorithm by programming a GPU to act as a neural network. By applying “deep learning” using the GPU, the team trained the neural network to identify and differentiate between malignant and benign skin lesions. The study’s result showed the algorithm to be as reliable as a human dermatologist is at detecting skin cancer, albeit with the potential to provide diagnoses at much greater speed and at lower cost. With 5.
