Yes / This comprehensive review paper examines bone fracture detection techniques based on time-domain low-frequency and microwave radiofrequency (RF). Early and accurate diagnosis of bone fractures remains critical in healthcare, as it can significantly improve patient outcomes. This review focuses on the potential of low-frequency and microwave RF methods, particularly their combination and application of time-domain analysis for enhanced fracture detection. We begin by providing an overview of the fundamental concepts of RF techniques and then by examining biological tissues' dielectric properties. We then compare the advantages and limitations of various bone fracture detection techniques, such as low-frequency RF methods, microwave RF methods, ultrasonography, X-ray, and CT scans. The discussion then shifts to hybrid approaches that combine low-frequency and microwave techniques, emphasising the advantages of such combinations in fracture detection. Machine learning techniques, their applications in bone fracture detection, and the role of time-domain analysis in hybrid approaches are also investigated. Finally, we examine the accuracy and reliability of simulated models for bone fracture detection. We discuss recent advancements and future directions, such as novel sensor technologies, improved signal processing techniques, integration with medical imaging modalities, and personalised fracture detection approaches. This review aims to comprehensively understand the landscape and future potential of time-domain analysis in low-frequency and microwave RF techniques for bone fracture detection. / EU Horizon Europe H2020-MSCA-RISE-2022-2027 (ID: 101086492) and H2020-MSCA-RISE-2019-2024 (ID 872878), Marie Skłodowska-Curie, Research and Innovation Staff Exchange (RISE), and the financial support from the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/ X039366/1.