# Emerging Applications of Machine Learning in 3D Printing
## 3 Results
### ML in the Analysis of the Harmfulness of 3D Printing  
Modern software based on artificial intelligence has been created, which allows for the analysis of the level of pollution generated by 3D printing systems. Such tools allow for precise determination of the potential risks associated with the emission of harmful substances during the printing process. Based on input data such as printing technique, type of material, print mass, and expected safety parameters, the program assesses what precautions should be taken to minimize the negative impact on the environment and health of users. A key advantage of the software is the use of ML algorithms, which enable its continuous improvement. By introducing new data from real printing processes, the tool becomes increasingly precise and effective in predicting potential threats. This program does not replace existing metrics and tools for assessing emissions but complements them, offering more advanced and comprehensive analysis. In practice, this means that 3D printing users can make informed decisions regarding the choice of materials, printer settings, and protective measures. The software also allows for predicting the impact of different printing technologies on indoor air quality, which is particularly important in workplaces and educational spaces. This solution is particularly useful for companies involved in serial production and institutions conducting research on new materials for 3D printing. The program also offers the potential for further development toward the automatic generation of recommendations for users. This may include proposals for replacing materials with less harmful ones, optimizing printing parameters, or recommendations for ventilation of rooms. Thanks to this, the tool contributes to increased safety and reduces the negative impact of 3D printing technology on the environment. The long-term use of this software can also support the development of more ecological printing technologies, as its analyses can indicate key areas for improvement in production processes. Thanks to the integration of machine learning, this tool can become the foundation of future safety standards in the 3D printing industry [45]. In addition to the biocompatibility and cytotoxicity [46,47] obvious in medical applications, the identification of materials and potentially harmful exposures in the case of 3D-printed product end-of-life remains an important issue. This may involve different modes of impact, as, for example, in the U.S., among the residues of 3D-printed products,58% are incinerated, 33% are landfilled, and only 9% are recycled. This poses a series of parallel scenarios for environmental and health protection. It is necessary not only to identify the sources of the above pollution but, above all, to shape pollution prevention and worker safety strategies [48]. ML in the Optimization of the Use of 3D-Printed Products, Including a Therapy Program Using 3D-Printed Medical Devices  
Optimizing the use of 3D-printed products, including medical devices supporting therapeutic programs, is gaining importance thanks to the use of artificial intelligence. Various motor deficits of the hands resulting from injuries and neurological or neurodegenerative diseases require an individualized diagnostic and therapeutic approach. In this context, remote monitoring of the patient's condition is becoming an invaluable tool, enabling early detection of changes, preventive intervention, and monitoring of the effectiveness of therapy. The use of AI in the analysis of data from exoskeletons or other medical devices allows for precise assessment of the patient's motor skills in home conditions. The integration of intelligent devices such as smartwatches, smart shoe inserts, or motion sensors allows for the collection of detailed data on the patient's activity in real time. This makes it possible to create diagnostic systems that not only analyze the current state of health but also predict its future condition based on historical data.