The exciting advancement related to the “modeling of digital human” in terms of a computational phantom for radiation dose calculations has to do with the latest hype related to deep learning. The advent of deep learning or artificial intelligence (AI) technology involving convolutional neural networks has brought an unprecedented level of innovation to the field of organ segmentation. In addition, graphics processing units (GPUs) are utilized as boosters for both real-time Monte Carlo simulations and AI-based image segmentation applications. These advancements provide the feasibility of creating three-dimensional (3D) geometric details of the human anatomy from tomographic imaging and performing Monte Carlo radiation transport simulations using increasingly fast and inexpensive computers. This review first introduces the history of three types of computational human phantoms: stylized medical internal radiation dosimetry (MIRD) phantoms, voxelized tomographic phantoms, and boundary representation (BREP) deformable phantoms. Then, the development of a person-specific phantom is demonstrated by introducing AI-based organ autosegmentation technology. Next, a new development in GPU-based Monte Carlo radiation dose calculations is introduced. Examples of applying computational phantoms and a new Monte Carlo code named ARCHER (Accelerated Radiation- transport Computations in Heterogeneous EnviRonments) to problems in radiation protection, imaging, and radiotherapy are presented from research projects performed by students at the Rensselaer Polytechnic Institute (RPI) and University of Science and Technology of China (USTC). Finally, this review discusses challenges and future research opportunities. We found that, owing to the latest computer hardware and AI technology, computational human body models are moving closer to real human anatomy structures for accurate radiation dose calculations.

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The active layer thickness of X-ray detectors needs to reach hundreds of micrometers to absorb X-ray photons, and therefore, high voltages over tens or hundreds of volts should be applied to extract the X-ray generated carriers1-3. The high voltage causes several issues including current drift, performance degradation, and operation insecurity4,5. Here, we propose a bulk Schottky junction (BSJ) for X-ray detector using interpenetrated microporous-carbon electrodes and metal-halide perovskite networks. In the BSJ, the X-ray-generated holes are extracted by the microporous-carbon electrodes under the built-in electric field, while the electrons in the perovskite result in a high gain effect. The BSJ-based detector achieves a high sensitivity of 1.42×105 μC Gyair-1 cm-2 and a low detection limit of 35 nGyair s-1 at a low voltage of -1 V. We fabricate a dry battery-powered portable X-ray alarm prototype which responds 24 times faster than the commercial Geiger-Müller counter tube. The BSJ-based detector maintains 92% of its initial sensitivity after storage for five months and exposure to 28.8 Gyair, equivalent to 144000 chest X-ray examinations. The BSJ-based detector arrays also show remarkable uniformity and decent spatial resolution up to 5.0 lp/mm, demonstrating its potential application in X-ray imaging.