The rapid development of technology has brought about a revolution in healthcare stimulating a wide range of smart and autonomous applications in homes, clinics, surgeries and hospitals. Smart healthcare opens the opportunity for a qualitative advance in the relations between healthcare providers and end-users for the provision of healthcare such as enabling doctors to diagnose remotely while optimizing the accuracy of the diagnosis and maximizing the benefits of treatment by enabling close patient monitoring. This paper presents a comprehensive review of non-invasive vital data acquisition and the Internet of Things in healthcare informatics and thus reports the challenges in healthcare informatics and suggests future work that would lead to solutions to address the open challenges in IoT and non-invasive vital data acquisition. In particular, the conducted review has revealed that there has been a daunting challenge in the development of multi-frequency vital IoT systems, and addressing this issue will help enable the vital IoT node to be reachable by the broker in multiple area ranges. Furthermore, the utilization of multi-camera systems has proven its high potential to increase the accuracy of vital data acquisition, but the implementation of such systems has not been fully developed with unfilled gaps to be bridged. Moreover, the application of deep learning to the real-time analysis of vital data on the node/edge side will enable optimal, instant offline decision making. Finally, the synergistic integration of reliable power management and energy harvesting systems into non-invasive data acquisition has been omitted so far, and the successful implementation of such systems will lead to a smart, robust, sustainable and self-powered healthcare system.

Timely, patient-specific cranial implants are critical for restoring skull integrity after trauma, accidents or surgery, yet off-the-shelf devices arrive in coarse, fixed size increments that often fail to match irregular defects. Surgeons are therefore forced to open and try multiple implants intra-operatively, adding infection risk, operative time, and likelihood of early implant failure. This paper presents an end-to-end workflow that predicts “print-ready” cranial implants directly from defective CT volumes and validates them through physical prototyping. The pipeline couples a five-stage volumetric preprocessing routine with a systematic exploration of neural architectures. A baseline and two deeper 3-D U-Nets are benchmarked alongside the proposed version 3 model, a channel-rebalanced U-Net that shifts capacity from shallow texture filters to boundary-aware decoding paths. Two public datasets, SciData and SkullFix, were merged, and split for training the model. On 40 unseen skulls, the proposed version 3 achieved a mean Dice of 0.901, Boundary Dice of 0.908, and HD95 of 1.52 mm, which is surpassing all alternatives and reducing the number of intra-operative trials predicted by simulation from 3-4 to zero. Bench-top tests confirmed that implants printed directly from the network’s STL output seated flush on three morphologically diverse phantoms without CAD edits or sanding, demonstrating true “push-button” manufacturability. These results show that a carefully optimized architecture, paired with robust pre-processing, can supply size-perfect implants using modest data and commodity hardware.