Magnetic Resonance Imaging (MRI), a powerful medical diagnostic tool, is the largest commercial application of superconductivity. The superconducting magnet is the largest and most expensive component of an MRI system. The magnet configuration is determined by competing requirements including optimized functional performance, patient comfort, ease of siting in a hospital environment, minimum acquisition and lifecycle cost including service. In this paper, we analyze conductor requirements for commercial MRI magnets beyond traditional NbTi conductors, while avoiding links to a particular magnet configuration or design decisions. Potential conductor candidates include MgB2, ReBCO and BSCCO options. The analysis shows that no MRI-ready non-NbTi conductor is commercially available at the moment. For some conductors, MRI specifications will be difficult to achieve in principle. For others, cost is a key barrier. In some cases, the prospects for developing an MRI-ready conductor are more favorable, but significant developments are still needed. The key needs include the development of, or significant improvements in: (a) conductors specifically designed for MRI applications, with form-fit-and-function readily integratable into the present MRI magnet technology with minimum modifications. Preferably, similar conductors should be available from multiple vendors; (b) conductors with improved quench characteristics, i.e. the ability to carry significant current without damage while in the resistive state; (c) insulation which is compatible with manufacturing and refrigeration technologies; (d) dramatic increases in production and long-length quality control, including large-volume conductor manufacturing technology. In-situ MgB2 is, perhaps, the closest to meeting commercial and technical requirements to become suitable for commercial MRI. Conductor technology is an important, but not the only, issue in introduction of HTS / MgB2 conductor into commercial MRI magnets. These new conductors, even when they meet the above requirements, will likely require numerous modifications and developments in the associated magnet technology.

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More than a century after the discovery of superconductors (SCs), numerous studies have been accomplished to take the advantage of SCs in physics, power engineering, quantum computing, electronics, communications, aviation, health care, and defence-related applications. However, there are still challenges that hinder the full-scale commercialization of SCs, such as the high cost of superconducting wires/tapes, technical issues related to AC losses, the structure of superconducting devices, the complexity and high cost of the cooling systems, the critical temperature, and manufacturing related issues. In the current century, massive advancements have been achieved on artificial intelligence (AI) techniques by offering disruptive solutions to handle engineering problems. Consequently, AI techniques can be implemented to tackle those challenges facing superconductivity and act as a shortcut towards full commercialization of SCs and their applications. AI approaches are capable of providing fast, efficient, and accurate solutions for the technical, manufacturing, and economic problems with a high level of complexity and nonlinearity in the field of superconductivity. In this paper, concept of AI and the widely used algorithms are first given. Then a critical topical review is presented for those conducted studies that used AI methods for improvement, design, condition monitoring, fault detection and location of superconducting apparatuses in large scale power applications, as well as the prediction of critical temperature and the structure of new SCs, and any other related applications. The topical review are presented in three main categories, AI for large-scale superconducting applications, AI for superconducting materials, and AI for physics of superconductors. In addition, the challenges to apply AI techniques to the superconductivity and its applications are given. Eventually, future trends on how to integrate AI techniques with superconductivity towards the commercialization are discussed.

This paper presents a roadmap to the application of AI techniques and big data for different modelling, design, monitoring, manufacturing and operation purposes of different superconducting applications. To help superconductivity researchers, engineers, and manufacturers understand the viability of using AI and big data techniques as future solutions for challenges in superconductivity, a series of short articles are presented to outline some of the potential applications and solutions. These potential futuristic routes and their materials/technologies are considered for a 10-20 years time-frame.

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