How Artificial Intelligence is Revolutionizing Disease Diagnosis and Improving Healthcare Efficiency

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The computer algorithms today are performing incredible tasks with high accuracy at a massive scale using human-like intelligence. This intelligence of computers is often referred to as AI or artificial intelligence. AI is poised to make an incredible impact on our lives in the future. Today, however, we still face massive challenges in detecting and diagnosing several life-threatening illnesses such as infectious diseases and cancer. Thousands of patients every year lose their lives due to liver and oral cancer. A best way to help these patients is to perform early detection and diagnoses of these diseases. In patients who unfortunately are suspected of these diseases, an expert physician first orders very expensive medical imaging technologies such as fluorescent imaging, CTs, and MRIs to be performed. Once those images are collected, another expert physician then diagnoses those images and talks to the patient. As you can see, this is a very resource-intensive process requiring both expert physicians and expensive medical imaging technologies. It is not considered practical for the developing world and, in fact, in many industrialized nations as well. So can we solve this problem using artificial intelligence? Today, if I were to use traditional artificial intelligence architectures to solve this problem, I would require 10,000—on an order of 10,000—of these very expensive medical images first to be generated. After that, I would then go to an expert physician who would analyze those images for me, and using those two pieces of information I can train a standard deep neural network or a deep learning network to provide patient diagnosis. Similar to the first approach, traditional AI approaches suffer from the same problem: large amounts of data, expert physicians, and expert medical imaging technologies. So can we invent more scalable, effective, and valuable artificial intelligence architectures to solve these very important problems facing us today? And this is exactly what my group at the MIT Media Lab does. We have invented a variety of unorthodox AI architectures to solve some of the most important challenges facing us today in medical imaging and clinical trials. In the example I share with you today, we had two goals. Our first goal was to reduce the number of images required to train AI algorithms. Our second goal, more ambitious, was to reduce the use of expensive medical imaging technologies to screen patients. For our first goal, instead of starting with tens of thousands of these very expensive medical images like traditional AI, we started with a single medical image. From this image, my team and I figured out a very clever way to extract billions of information packets. These information packets included colors, pixels, geometry, and rendering of the disease on the medical image. In a sense, we converted one image into billions of training data points, massively reducing the amount of data needed for training. For our second goal, to reduce the use of expensive medical imaging technologies to screen patients, we started with a standard white light photograph acquired either from a DSLR camera or a mobile phone. Remember those billions of information packets? We overlaid those from the medical image onto this image, creating something we call a composite image. Much to our surprise, we only required 50—yes, only 50—of these composite images to train our algorithms to high efficiencies. To summarize our approach, instead of using 10,000 very expensive medical images, we can now train AI algorithms in an unorthodox way using only 50 of these high-resolution but standard photographs acquired from DSLR cameras and mobile phones and provide diagnosis. More importantly, our algorithms can accept in the future, and even right now, some very simple white light photographs from the patient instead of expensive medical imaging technologies. I believe that we are poised to enter an era where AI is going to make an incredible impact on the future. Thinking about traditional AI, which is data-rich but application-poor, we should also continue thinking about unorthodox AI architectures which can accept small amounts of data and solve some of the most important problems facing us today, especially in healthcare. Thank you very much.