Implantable Medical Devices (IMDs) are deployed in patients to treat a range of medical conditions. Technological advancements have enabled manufacturers to fit IMDs with specialized hardware that accelerates compute-intensive medical therapies next to a software-run host processor. However, mostly hardware acceleration is found in the form of ASIC peripherals next to a host processor in state-of-the-art IMDs, while low-power FPGAs could provide a comparable performance gain with the added benefit of the upgradability of functionality. Existing literature about low-power FPGAs focuses on new algorithms or performance improvements, while largely ignoring power and energy analysis, the latter being the most limiting factor in the IMD environment. This thesis investigates under what conditions FPGAs could be added to IMDs by developing two use cases: an FPGA securing wireless communication, and accelerating a neural network aiding medical therapies that depend on pattern detection. These cases are evaluated on FPGA, eFPGA and MCU with ASIC peripheral platforms, from which performance, energy usage and prospected IMD battery life is derived. On one end, it was found that AES encryption used 4.4 times the energy of an MCU hardware-accelerated implementation while being 17% slower. However, employing lightweight ciphers on the FPGA closes this gap. Furthermore, adding an FPGA results in only a 7.5% decrease in battery life when the FPGA is shut off during idling to combat its high static current draw. Running an FPGA-accelerated neural network is feasible if the active time is 6.5 minutes per day. With weekly recharging, continuous monitoring is possible. Using an eFPGA, which is an embedded FPGA fabric integrated within an MCU, results in using only 12% to 21% of the FPGA package area and is almost 2 times as energy efficient under 2 minutes daily usage as an FPGA. As FPGAs in IMDs is a novel field, research was done in legal regulation of IMDs, where it was found that existing regulations on software devices also applies to FPGAs. Therefore, all obstacles of the technical and legal kind have been removed that hold IMD manufacturers from using FPGAs in their devices.