The first industrial revolution led to mechanical production and steam power; the second, mass production and electrical power; and the third, electronics and computers. Today, as most sectors of the world move forward into the fourth industrial revolution, one centered around data and artificial intelligence (AI), biomedicine finds itself still in the third, lagging behind the rest [1]. Only recently, the exponential growth of technology has facilitated the widespread integration of computers into the biomedical domain from electronization of medical data analysis to automated detection of biomedical images [2–3]. Rather than merely automating time-consuming processes within healthcare, AI stands to reduce medical errors, expand upon the relationships between basic science and clinical medicine, and improve our analysis of existing datasets too large and complex for traditional statistics [3]. Despite these potential benefits, many biomedical facilities are hesitant to incorporate such systems into their practices due to the liability associated with AI making decisions that impact the health of patients [4], such as misdiagnosis (see Rule 8). Additionally, there exists a computational “black box,” a phenomenon describing the difficulty of understanding how AI algorithms arrive at a particular result (see Rule 3). Without a clear means of understanding how these machines generate their output, biomedical facilities are often skeptical of incorporating these “black boxes” into their work practices. As such, the “explainability” issue is an important barrier to overcome before applying these powerful technologies in biomedicine [5]. The lack of understanding around AI and the tantalizing benefits of this new wave of technology suggest the need for professionals in biomedical fields to acquire a basic understanding of AI and its medical applications in order to understand its clinical utility and engage with cutting-edge research. As such, there is a clear need for literature that explains AI in a way that is digestible to professionals in other fields [5]. Without a fundamental understanding of data science models and AI methods, modern biomedical experts who are not well versed in these fundamentals may be intimidated. Introduction to the basics of AI, such as big data analysis, data mining, machine and deep learning, and computer vision, would allow for the expansion of innovative designs in biomedicine.
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