An article in the Harvard Business Review asks us to “imagine that the next time you see your doctor, she says you have a life-threatening disease. The catch? A computer has performed your diagnosis, which is too complex for humans to understand entirely. What your doctor can explain, however, is that the computer is almost always right”.
The article points out that, while this might sound like science fiction, it is already happening. Advances in machine learning offer more insights from ever-growing amounts of data.
Complex algorithms will soon help doctors to make incredibly accurate diagnoses from large amounts of information, premised on largely unexplainable correlations in that data.
While the prospect of a diagnosis from a computer rather than a doctor might sound alarming, the article notes that it’s a future that “we must prepare for — and embrace — because of the impact these new methods will have and the lives we can potentially save”.
A Study from a group of researchers from the University of Chicago, Stanford University, the University of California, San Francisco, and Google has been published in the prestigious journal Nature. The researchers input de-identified data on hundreds of thousands of patients into a series of machine learning algorithms.
With extraordinary accuracy, these algorithms were able to predict and diagnose diseases, from cardiovascular illnesses to cancer, and predict related things such as the likelihood of death, the length of hospital stay, and the chance of hospital readmission.
The catch is that these predictions were based on patterns in the data that the researchers could not fully explain. This presents a challenge: getting an accurate diagnosis is great, but, patients will want to know how that diagnosis was reached.
It is important that the machine learning algorithms can be explained in relatively simple terms. “The computer says you have cancer,” is unlikely to be an acceptable outcome.
As the article’s author concludes, “challenges to using machine learning in health care abound. But these challenges pale in comparison with the benefits these advances will bring.”
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