5 Key Algorithms for Artificial Intelligence to Improve on Human Limitations in Cancer Care

I'm currently working my way through the book Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian & Tom Griffiths, which deconstructs many key life decisions into algorithms that can lead to optimal decision-making. As it cogently reviews many basic concepts using a wide range of life examples, I can see many ways in which machine learning techniques could sift through mountains of clinical data on cancer patients to help guide our management decisions in ways that elude the limitations of human brains. Oncologists work with patients to weigh decisions about whether a treatment with partial benefit (limited shrinkage of a cancer or even modest progression) is good enough to continue treatment and when a stronger choice is to change treatment approaches. When is is the expected benefit of more of the same, likely with a discount from diminishing returns, less than the anticipated or unknown benefits of the next alternative therapy? A well honed algorithm should be able to follow the growth kinetics of a cancer on scans and predict when it's time to change horses.