As a medical student, many of my classmates breathed just one sigh of relief upon the start of the school year: at least we’re finally free of math! Or so they thought. Algebra and equations frequently popped up throughout the semester, ranging from acid-base equations we’ve seen since high school to some entry level fluid mechanics like viscosity and laminar flow. To say you don’t need any math in medical school is a gross oversimplification, but it’s true you won’t need to be calculating eigenvectors in your unit on reproductive physiology.
There’s a very rigid barrier between fields of study created in students' heads well before they even open their first semester physiology textbook, let alone learn about the MCAT. College and high school settings often introduce the sciences and math in completely isolated settings. I’d argue though that separating these concepts only leads to more confusion and a contempt for math: “When will we ever need this?!” is the most common question asked in math classrooms around this country.
I was fortunate enough to study applied mathematics in undergrad. It not only made me a stronger medical student (and hopefully, one day, physician), but it also made me appreciate the math that underpins everything physicians think about and work on in and around the human body. Learning math in the context it is used and most often applied to helps with both the rote memorization of material and in deepening a fundamental understanding of the interrelated concepts at play.
My experiences learning linear algebra in undergrad really epitomize the above. In its first iteration, I was taught linear algebra in a pure math context where I learned the barebones mechanics of matrix math, eigenvalues, eigenspaces, and vectors. While that one math nerd may be able to appreciate the deeper math theory at play, most students think of this as just a box to check for their degree and forget the material immediately afterwards. Later, most medical students dread or avoid linear algebra in its entirety. When linear was reintroduced in an applied context, however, I was able to reinforce that understanding while also seeing the powerful ways linear algebra can be applied to quantitative biological and clinical research.
Take, for example, Principal Component Analysis (PCA). Using the basic properties of eigenvalues and eigenvectors, we can take a highly multidimensional dataset and identify the X (typically less than 10) most significant dimensions within the set (or “component space”) to better sort data and examine relevant trends. I’ll spare you the specifics of covariance matrices and single value decomposition, but in essence, PCA is a cheat code in reducing noise in a dataset looking at too many genes, proteins, or hormones, and identifying what variables are actually contributing to changes (or variance) within the dataset.
The methods I was able to learn in these applied math courses, often within a biological context, helped make me more curious about the research I aspire to conduct while making me a stronger quantitative thinker. This isn’t a plea for every medical student to study applied math (though some part of me may feel that way), but I feel strongly that teaching students math in a way that actually connects to their interests, be it medicine, sociology, or even history, can help make these dreaded concepts a bit more interesting (or even fun).
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