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Technological and material issues in 3D printing technologies should take into account sustainable development, use of materials, energy, emitted particles, and waste. The aim of this paper is to investigate whether the sustainability of 3D printing processes can be supported by computational intelligence (CI) and artificial intelligence (AI) based solutions. We present a new AI-based software to evaluate the amount of pollution generated by 3D printing systems. We input the values: printing technology, material, print weight, etc., and the expected results (risk assessment) and determine if and what precautions should be taken. The study uses a self-learning program that will improve as more data are entered. This program does not replace but complements previously used 3D printing metrics and software.

Novel, easy-automation technologies such as three-dimensional (3D) printing and reverse engineering can improve the training of medical and allied health professionals and everyday clinical practice. This paper aims at the presentation of its own concept of the repository of medical images for education and everyday clinical practice purposes. Presented concept of the repository constitutes a relatively novel solution, but its further development may lead to the novel family of commercial initiatives aiming at joining common efforts toward optimization of 3D-based technologies in everyday clinical practice and online e-learning system.

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