Implantable sensors have revolutionized the way we monitor biophysical and biochemical parameters by enabling real-time closed-loop intervention or therapy. These technologies align with the new era of healthcare known as healthcare 5.0, which encompasses smart disease control and detection, virtual care, intelligent health management, smart monitoring, and decision-making. This review explores the diverse biomedical applications of implantable temperature, mechanical, electrophysiological, optical, and electrochemical sensors. We delve into the engineering principles that serve as the foundation for their development. We also address the challenges faced by researchers and designers in bridging the gap between implantable sensor research and their clinical adoption by emphasizing the importance of careful consideration of clinical requirements and engineering challenges. We highlight the need for future research to explore issues such as long-term performance, biocompatibility, and power sources, as well as the potential for implantable sensors to transform healthcare across multiple disciplines. It is evident that implantable sensors have immense potential in the field of medical technology. However, the gap between research and clinical adoption remains wide, and there are still major obstacles to overcome before they can become a widely adopted part of medical practice.

Objective: Mastoidectomy surgical training is challenging due to the complex nature of the anatomical structures involved. Traditional training methods based on direct patient care and cadaveric temporal bone training have practical shortcomings. 3D‐printed temporal bone models and augmented reality (AR) have emerged as promising solutions, particularly for mastoidectomy surgery, which demands an understanding of intricate anatomical structures. Evidence is needed to explore the potential of AR technology in addressing these training challenges.Methods: 21 medical students in their clinical clerkship were recruited for this prospective, randomized controlled trial assessing mastoidectomy skills. The participants were randomly assigned to the AR group, which received real‐time guidance during drilling on 3D‐printed temporal bone models, or to the control group, which received traditional training methods. Skills were assessed on a modified Welling scale and evaluated independently by two senior otologists.Results: The AR group outperformed the control group, with a mean overall drilling score of 19.5 out of 25, compared with the control group's score of 12 (p < 0.01). The AR group was significantly better at defining mastoidectomy margins (p < 0.01), exposing the antrum, preserving the lateral semicircular canal (p < 0.05), sharpening the sinodural angle (p < 0.01), exposing the tegmen and attic, preserving the ossicles (p < 0.01), and thinning and preserving the external auditory canal (p < 0.05).Conclusion: AR simulation in mastoidectomy, even in a single session, improved the proficiency of novice surgeons compared with traditional methods.Level of Evidence: NA Laryngoscope, 135:894–900, 2025

One-lung intubation (OLI) is among the most common complications of endotracheal intubation. None of the monitoring tools now available has proved effective for its early detection. In this study we investigated the efficacy of acoustic analysis for the detection of OLI. We collected lung sounds from 11 patients undergoing thoracic surgery requiring the placement of a double-lumen tube. Recordings of separate lung ventilation were performed after induction and confirmation of adequate tube positioning, before surgery. Samples of lung sounds were collected by three piezoelectric microphones, one on each side of the chest and one on the right forearm, for background noise sampling. The samples were filtered, the signals' energy envelopes were calculated, and segmentation to breath and rest periods was performed. Each respiration was classified into one of the three categories: bilateral ventilation, selective right-lung ventilation, or selective left-lung ventilation, on the basis of the ratio between the energy signals of each lung. OLI was accurately identified in 10 of the 11 patients during right OLI and in all 11 patients during left OLI. This study suggests that acoustic monitoring is effective for the detection of selective lung ventilation and may be useful for early diagnosis of OLI.