The trend toward miniaturization and multi-functionalization of implantable medical devices is shifting the paradigm of medicine from treatment to prevention and precision management; however, it faces a bottleneck due to the energy density limits of batteries. Existing power transfer technologies based on electromagnetic induction or radio frequency cause rapid efficiency degradation and electromagnetic interference issues in micro-scale devices. This paper reviews laser-based optical wireless power transfer technology as an innovative alternative to these issues, with particular emphasis on its potential medical applications. We provide an in-depth analysis of strategies to maximize penetration depth using near-infrared windows and light propagation characteristics within biological tissue, optimization of conversion efficiency through bandgap engineering of silicon (Si) and gallium arsenide (GaAs) based photovoltaic cells, and the latest wavefront shaping and optical phased array technologies to overcome dynamic scattering. Furthermore, by chronologically organizing research trends over the past 20 years, we discuss the paradigm shift from passive devices to active intelligent systems and present the technical and regulatory challenges for clinical adoption based on international safety standards (IEC 60825-1, ISO 14708-1), ultimately providing a technical roadmap for the energy autonomy of next-generation bio-electronic medicine.

Background: Femtosecond laser-induced breakdown spectroscopy (fs-LIBS) is a powerful tool for biomedical diagnostics because it provides real-time, in situ elemental analysis with high spatial resolution. This study used fs-LIBS to distinguish melanoma from normal dermis tissue. Methods: A crater size of approximately 20 μm and an enhanced signal-to-background ratio of critical emission lines were achieved by optimizing the key experimental parameters, such as laser energy, gate delay, and spectral normalization. Results: Principal component analysis and support vector machine algorithms were applied to the normalized spectra, resulting in 96.4% classification accuracy. Conclusion fs-LIBS, under the appropriate experimental conditions and data processing, provides precise tissue analysis and has significant potential for advancing bio-imaging and early cancer detection.