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Education of the clinician in data science and artificial intelligence: it is not about the programming

“If you want to build a ship, don’t drum up the men to gather wood, divide the work, and give orders. Instead, teach them to yearn for the vast and endless sea.”

Antoine de Saint-Exupery, French pilot and author of Le Petit Prince

One of the most common questions I get from clinicians is how does one get started in the domain of data science and artificial intelligence that is relevant for clinical medicine and healthcare. I usually reply that it is not about learning the programming languages but much more about appreciating the tenets of data science and artificial intelligence and how these disciplines are appropriately applied to clinical medicine or healthcare. I draw the analogy of clinicians being string players in a string ensemble, and now an orchestra is being formed with new sections: woodwinds, brass, and percussion. This transition does not mean that all string players need to learn to play new instruments, but the violinists and cellists do need to accommodate these new musicians and learn the new dynamics of these new instruments in musical compositions. Exciting nascent areas of AI in medicine include: natural language processing in some communication tools, unsupervised learning for discovery of new diseases and subtypes, deep learning for drug discovery and repurposing, self-supervised learning for training of biomedical data, and embedded AI in wearable devices.

Having stated this philosophy, however, I do think some clinicians who wish to learn data science and artificial intelligence at a deeper level will benefit from learning programming. The biggest takeaway of the very different domain of computer programming is that one’s mode of thinking as a clinician is influenced for the better (improved Kahneman’s system 1 to system 2 balance). I also feel that some clinicians will also earn valuable dividends by being in a structured educational program in data science and artificial intelligence. While less formal and structured learning based on courses on the Internet can be better suited for some clinicians, a more structured program can offer more opportunities of collaborating with others in class projects as well as networking with many after completion of the program. The effort to pursue a degree demands sacrifices in the midst of busy clinical training or hectic clinical schedules, but the reward is also of immense value for the long term.

All of these strategies to learn data science and artificial intelligence is ultimately a set of very personal decisions based on individual assessment and preference. The most important suggestion that I have is: jump in and not be afraid to be out of your comfort zone!

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