Abstract
In the following chapter, the author describes various applications, challenges, and opportunities for machine earning, specifically computer vision in medical diagnostics. Medical diagnostics is one of the most interesting areas of application for computer vision, especially when the efficiency and accuracy of medical diagnostics can be vastly improved, which has a direct positive impact on the treatment outcomes for patients. There are various main areas of applications, ranging from cancer detection to cell counts and gait analysis, interestingly they mostly use the same algorithmic principles and therefore share some challenges and opportunities for improvement, which are discussed in this chapter. It is concluded that access to high-quality data and improving the principles of human–computer interaction are the most impactful improvements which can be made, and that it is important to optimize for human–machine collaboration, due to their respective strengths and weaknesses.
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Notes
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Udacity, founded by Sebastian Thrun. Coursera, founded by Daphne Koller and Andrew Ng.
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Weinberger, P.C. (2021). Computer Vision Applications in Medical Diagnostics. In: Glauner, P., Plugmann, P., Lerzynski, G. (eds) Digitalization in Healthcare. Future of Business and Finance. Springer, Cham. https://doi.org/10.1007/978-3-030-65896-0_11
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