Objectives: Smart hospitals involve the application of recent information and communications technology (ICT) innovations to medical services; however, the concept of a smart hospital has not been rigorously defined. In this study, we aimed to derive the definition and service types of smart hospitals and investigate cases of each type. Methods: A literature review was conducted regarding the background and technical characteristics of smart hospitals. On this basis, we conducted a focus group interview with experts in hospital information systems, and ultimately derived eight smart hospital service types.Results: Smart hospital services can be classified into the following types: services based on location recognition and tracking technology that measures and monitors the location information of an object based on short-range communication technology; high-speed communication network-based services based on new wireless communication technology; Internet of Things-based services that connect objects embedded with sensors and communication functions to the internet; mobile health services such as mobile phones, tablets, and wearables; artificial intelligence-based services for the diagnosis and prediction of diseases; robot services provided on behalf of humans in various medical fields; extended reality services that apply hyper-realistic immersive technology to medical practice; and telehealth using ICT. Conclusions: Smart hospitals can influence health and medical policies and create new medical value by defining and quantitatively measuring detailed indicators based on data collected from existing hospitals. Simultaneously, appropriate government incentives, consolidated interdisciplinary research, and active participation by industry are required to foster and facilitate smart hospitals.

Background: Federated learning is a decentralized approach to machine learning; it is a training strategy that overcomes medical data privacy regulations and generalizes deep learning algorithms. Federated learning mitigates many systemic privacy risks by sharing only the model and parameters for training, without the need to export existing medical data sets. In this study, we performed ultrasound image analysis using federated learning to predict whether thyroid nodules were benign or malignant. Objective: The goal of this study was to evaluate whether the performance of federated learning was comparable with that of conventional deep learning. Methods: A total of 8457 (5375 malignant, 3082 benign) ultrasound images were collected from 6 institutions and used for federated learning and conventional deep learning. Five deep learning networks (VGG19, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) were used. Using stratified random sampling, we selected 20% (1075 malignant, 616 benign) of the total images for internal validation. For external validation, we used 100 ultrasound images (50 malignant, 50 benign) from another institution. Results: For internal validation, the area under the receiver operating characteristic (AUROC) curve for federated learning was between 78.88% and 87.56%, and the AUROC for conventional deep learning was between 82.61% and 91.57%. For external validation, the AUROC for federated learning was between 75.20% and 86.72%, and the AUROC curve for conventional deep learning was between 73.04% and 91.04%. Conclusions: We demonstrated that the performance of federated learning using decentralized data was comparable to that of conventional deep learning using pooled data. Federated learning might be potentially useful for analyzing medical images while protecting patients’ personal information.

Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We propose a polyp detection system that reflects the morphological characteristics of SSLs to detect unrecognized or easily missed polyps. To develop a well-trained system with imbalanced polyp data, a generative adversarial network (GAN) was used to synthesize high-resolution whole endoscopic images, including SSL. Quantitative and qualitative evaluations on GAN-synthesized images ensure that synthetic images are realistic and include SSL endoscopic features. Moreover, traditional augmentation methods were used to compare the efficacy of the GAN augmentation method. The CADe system augmented with GAN synthesized images showed a 17.5% improvement in sensitivity on SSLs. Consequently, we verified the potential of the GAN to synthesize high-resolution images with endoscopic features and the proposed system was found to be effective in detecting easily missed polyps during a colonoscopy.

Significance: Why have primates evolved epidermal ridges on the volar regions of the hands and feet and with a much greater density of sweat glands than flat skin, which respond to anxiety rather than act as a thermoregulation mechanism? During contact with solid objects, the ridges are important for grip and precision manipulation by regulating moisture levels from either external sources or the sweat pores so that the friction is maximized and catastrophic slip is inhibited. An understanding of the underlying mechanisms involved has become particularly important with the almost ubiquitous contact of the finger pads with flat screens and recent developments in haptic feedback using ultrasonic vibrations for which the performance is critically related to the friction.

Gravity is omnipresent on Earth; however, humans in space, such as astronauts at the International Space Station, experience microgravity. Long-term exposure to microgravity is considered to elicit physiological changes, such as muscle atrophy, in the human body. In addition, certain types of cancer cells demonstrate inhibited proliferation under condition of time-averaged simulated microgravity (taSMG). However, the response of human Hodgkin’s lymphoma cancer cells to reduced gravity, and the associated physiological changes in these cells, have not been elucidated. In this study, the proliferation of human Hodgkin’s lymphoma cancer cells (L-540 and HDLM-2) under taSMG condition (<10−3 G, 1 G is defined as 9.8 m/s2) was studied using a 3D clinostat. Normal human dermal fibroblast (HDF) was proliferated in the same condition as a control group. For the development of 3D clinostat, two motors were used to actuate the frames. Electrical wires for power supply and communication were connected via slip ring. For symmetrical path of gravitational vector, optimal angular velocities of the motors were found using simulation results. Under the condition of taSMG implemented by the 3D clinostat, proliferation of the cells was observed for 3 days. The results indicated that proliferation of these cancer cells was significantly (p < 0.0005) inhibited under taSMG, whereas proliferation of normal HDF cells was not affected. Findings in this study could be significantly valuable in developing novel strategies for selective killing of cancer cells such as lymphoma.