## 4 Discussion  
Given the critical importance of MRI in healthcare and its significant consumption of resources, especially in the context of climate change and fluctuating costs for electricity and materials, it is imperative to refine practices to maintain healthcare quality. This optimization should target both productive (scan mode) and non-productive (idle time) operational phases. An examination of two manufacturer-provided idle modes, \"Shutdown\" and \"Restart\", showed that \"Restart\" was more energy-efficient, consuming 97.83 kWh per night compared to 107.6 kWh for \"Shutdown\". Additionally, the analysis revealed that a \"Forgotten\" mode, which is inadvertently left unused by users, was the most energy-hungry, consuming 109.50 kWh per night. Achieving optimization requires staff awareness, especially during the machine's idle phases. Failure to select energy-saving modes leads to unnecessary overconsumption. Although the energy loss may be small, it is still worth reducing. An assessment of 10 neuroradiological and musculoskeletal protocols examined their energy and financial impact, with energy consumption ranging from 3.4 to 15 kWh. The use of Philips SmartSpeed Intelligence AI-based software and multi-band diffusion provided faster, optimized alternatives with lower energy use, ranging from 2.3 to 10.6 kWh per protocol, resulting in savings of EUR 0.03 to 3.7 per protocol. In order to substantiate these results, a set of protocols was developed, shaped by price fluctuations and taking into account various daily constraints. From 8 a.m. to 8 p.m., optimized protocols allowed for the addition of five neurovascular and six knee protocols, and reducing daily energy consumption from 330.95 kWh to 296.03 kWh. Improved protocols and strategies increased productivity, enabling more MRI studies with lower energy use, yielding kWh savings and financial benefits. Only a few studies have investigated the energy usage of MRI machines, focusing on annual evaluations of consumption factors, carbon footprint, and methods for reduction. Our design aims to implement strategies for this purpose. The use of artificial intelligence to speed up image acquisition is increasing rapidly. However, the impact of AI on energy consumption has not yet been thoroughly reached. On our scale, on working days, the implementation of AI-optimized protocols could save approximately 7927 kW/year per MRI. At the scale of the French fleet, comprising 1329 MRI units [20], annual savings could potentially reach 10,500-11,600 MWh/year. This amount of electricity is equivalent to the annual consumption of approximately 4740-5267 people in France. This consumption results in about 526.81 to 582.33 tons of CO\({}_{2}\) in France, which has relatively low-carbon production. The same consumption would result in carbon emissions of more than 4256.62 tons in the United States, and more than 6825 tons in China, where carbon emission rates vary significantly. Additionally, potential savings could be greater when considering the higher MRI density in these countries [21]. Figure 6 presents the annual extrapolations of potential energy, time, and CO\({}_{2}\) equivalent savings. For instance, [PERSON] et al. reported that each cardiac MRI booking slot saves 3.3 kWh of energy and avoids 1.4 kg of CO\({}_{2}\). In France, this energy saving would result in a reduction of 165 g of CO\({}_{2}\)[22]. Furthermore, there is limited data on the overall electricity consumption of MRI machines and their standby modes. Their survey highlighted the disparities among MRI models, which varied by manufacturers, magnetic field, and year of installation. Non-productive consumption can vary. For example, [PERSON] et al. [23] found that two 1.5T MRI machines (Siemens Advento 2013 and Magnetom Aera 2013) had the lowest power mode ranging from 13.9 to 14.5 kW. In contrast, the lowest power mode of our Philips 3T MRI (2022) was reduced to 6.76 kWh.