Abstract
The application of machine learning and deep learning in additive manufacturing, also called 3D printing, is expected in industrial fields to be an effective method to optimize the manufacturing process, to control the quality of 3D printed objects, to detect defects in the objects, and to predict material properties. In the pharmaceutical field, 3D printed medicine has been approved by the United States Food and Drug Administration, and since then, 3D printing technology has been attracting attention, even creating a new model of tailored medicine. The 3D printing of pharmaceutical products needs a trial-and-error process due to the complex printing parameters as well as the physical properties of the printer ink, which is the drug formulation in this case. Machine learning may hold promise in solving the complex problems of drug manufacturing using 3D printers. This review introduces recent articles about 3D printed medicine and the application of machine learning. We also include recent articles about 3D printed medicine that use statistical approaches in the experimental methods. Finally, we discuss a possible future where “artificial intelligence pharmacists” will regularly use 3D printers in a clinical setting.
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Tagami, T., Ogawa, K., Ozeki, T. (2023). Machine Learning in Additive Manufacturing of Pharmaceuticals. In: Banerjee, S. (eds) Additive Manufacturing in Pharmaceuticals. Springer, Singapore. https://doi.org/10.1007/978-981-99-2404-2_11
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