Translating Artificial Intelligence Into Clinical Care.
Artificial intelligence has become a frequent topic in the news cycle, with reports of breakthroughs in speech recognition, computer vision, and textual understanding that have made their way into a bevy of products and services that are used every day. In contrast, clinical care has yet to reach the much lower bar of automating health care information transactions in the form of electronic health records. Medical leaders in the 1960s and 1970s were already speculating about the opportunities to bring automated inference methods to patient care,1 but the methods and data had not yet reached the critical mass needed to achieve those goals. The intellectual roots of "deep learning," which power the commodity and consumer implementations of presentday artificial intelligence, were planted even earlier in the 1940s and 1950s with the development of "artificial neural network" algorithms.2,3 These algorithms, as their name suggests, are very loosely based on the way in which the brain's web of neurons adaptively becomes rewired in response to external stimuli to perform learning and pattern recognition. Even though these methods have had many success stories over the past 70 years, their performance and adoption in medicine in the past 5 years has seen a quantum leap. The catalyzing event occurred in 2012 when a team of researchers from the University of Toronto reduced the error rate in half on a well known computer vision challenge using a deep learning algorithm.4 This work rapidly accelerated research and development in deep learning and propelled the field forward at a staggering pace. With the increased availability of digital clinical data, it remains to be seen how these deep learning models might be applied to the medical domain. In this issue of JAMA, Gulshan and colleagues5 present findings from a study evaluating the use of deep learning for detection of diabetic retinopathy and macular edema. To build their model, the authors collected 128 175 annotated images from the EyePACs database. Each image was rated by 3 to 7 clinicians for referable diabetic retinopathy, diabetic macular edema, and overall image quality. Each rater was selected from a panel of 54 board certified ophthalmologists and senior ophthalmology residents. Using this data set, the algorithm learned to predict the consensus grade of the raters along each clinical attribute: referable diabetic retinopathy, diabetic macular edema, and image quality. To validate their algorithm, the authors assessed its performance on 2 separate and nonoverlapping data sets consisting of 9963 and 1748 images. On the validation data, the algorithm had high sensitivity and specificity. Only one of these values (sensitivity on the second validation data set) failed to be superior at a statistically significant level. The other performance metrics (eg, area under the receiver operating characteristic curve, negative predictive value, positive predictive value) were likewise impressive, giving the authors confidence that this algorithm could be of clinical utility. This work closely mirrors a recent "Kaggle" contest in which 661 teams competed to build an algorithm to predict the grade of diabetic retinopathy, albeit on a smaller data set with fewer grades per image. Kaggle is a website that hosts machine learning and data science contests. Companies and researchers can post their data to Kaggle and have contestants from around the world build predictive models. In the diabetic retinopathy contest, nearly all of the top teams used some form of deep learning and had little to no knowledge of the eye or ophthalmology. The first place team6 and secondplace team7 both used standard deep learning models and were data science practitioners, not medical professionals. Gulshan et al correctly pointed out that a prerequisite for a successful deep learning model is access to a large database of images with high quality annotations. Accordingly, the investigators increased both the number of images available and the number of ratings per image, which allowed them to improve on the existing state of the art with respect to both Kaggle and the existing scientific literature. To build their algorithm, Gulshan et al leveraged a workhorse model in deep learning known as a convolutional neural network that has been critically important to recent advances in automatic image recognition. The convolutional neural network model used by the authors is known as the Inception V3 network,8 which was developed by Google for entry in the Large Scale Visual Recognition Challenge, which it won in 2014. In this contest, known as ImageNet,9 researchers were given 1.2 million images that involve 1000 different categories that cover a wide variety of everyday objects, such as cats, dogs, automobiles, and different kinds of food. The goal of the contest was to build a classifier that could automatically recognize which object was present in an image and to identify which region of the image contained the object. This challenge was broad so that it covered many types of objects that a computer vision system could encounter in the real world. As a result of this contest, several techniques10 12 have been pioneered that improved the accuracy of these models immensely. As with the study by Gulshan et al, these improvements are beginning to trickle into other areas of computer vision, including medical image processing. For example, Gulshan et al not only used the same network that was originally built for ImageNet, they also used that network Editorial and Viewpoint
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