What’s New in Cardiology
Demystifying Artificial Intelligence: Present and Future Applications to Medicine Geoff Tison, MD, MPH In the past couple of years, the term “Artificial Intelligence”
seems to be almost everywhere we look. Artificial Intelligence, or “AI,” is advertised as being used to help do everything from driving your car to selling your next pair of shoes. Even in healthcare, AI is frequently invoked with fanfare, but often accompanied by little insight into what it actually is, what is possible, or how soon it will come. Certainly, our daily lives have been infused by technology, from the internet to smartphones, and much of it does seem “intelligent,” so much so that sometimes we forget that there is no human on the other side. For example, Lyft tells you when your ride is coming and when you’ll arrive at your destination, interspersing several pickups and dropoffs along the way. Google not only gives you a list of webpages related to your query, which can be spoken or typed using natural language, but may answer your question directly or even recommend a book to further your understanding. But healthcare is complex, its data is nuanced and medicine is different — or is it? In this article, I will outline some of the basics underlying AI and begin to describe ways in which it may impact the medical field. So what does AI actually refer to and how “intelligent” is it really? In common parlance, the term AI is often used loosely. But from an algorithmic perspective, modern references to AI usually indicate efforts that use a collection of algorithms that fall under the category of machine-learning algorithms. What makes these types of algorithms different from those that came before them is that they possess the ability to learn patterns from large amounts of data without explicitly being given rules that describe how to interpret that data. Combined with recent advances in computing, these algorithms tend to be flexible enough to accept raw, unprocessed data as inputs, without the need to summarize the data or extract a hand-picked subset of the data for interpretation. This has opened the door to analyzing data in ways not even conceivable just over a decade ago, such as analyzing every pixel across an entire image (and the interactions between them) to identify a breed of dog, or interpreting the series of images in a video to self-drive a car.
Importantly, the way that these algorithms are developed usually requires large amounts of example data that have been annotated for the task of interest. For example, one could provide many pictures of different dogs that have been labeled with their specific breeds or hours of video with labels for street signs, delivery trucks or pedestrians. Thus, while these algorithms have become quite adept at increasingly complex tasks for which they are trained — spurring predictions that robots may soon replace doctors — current algorithms do less well for novel tasks involving data that are substantially different from those for which they were trained. For example, an algorithm that has been trained to interpret brain MRIs for specific tumors may not perform well for other types of tumors, or even for T2-weighted MRI images of the same tumors. This has led some to caution that there is no real intelligence in present artificial intelligence — at least not yet. So, while there are certainly limits to what can currently be achieved by applying AI techniques to medicine — robots will not be doing our jobs anytime soon— there are just as certainly many substantial gains that are solidly within reach given our current capabilities that can potentially improve the lives of our patients and our work as physicians. As physicians, we can help by working with AI experts (either researchers or in partnership with companies) to identify the applications of AI to medicine that can most impact patient care and to highlight the clinically relevant problems that these algorithms, even with their limitations, are best positioned to solve. Similar to other fields in which AI has made the largest strides, such as voice or image recognition, the large potential for AI in medicine is enabled in large part by the widespread digitization of medical data that has occurred over the past couple decades. From radiology “films” to electrocardiogram tracings, we have all witnessed the large-scale migration of medical data to digitized formats, and this provides the raw fuel with which to train AI algorithms. But just like other raw materials, this digitized medical data requires appropriate processing
We applied deep neural networks to data collected passively from
the Apple smartwatch, namely raw heart rate and step-count . . . [and] used this data to detect
atrial fibrillation with high accuracy when compared against electrophysiologist-confirmed ECGs.”
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SAN FRANCISCO MARIN MEDICINE JULY/AUGUST 2018
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