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近年来，大语言模型在通用领域涌现出了惊人的智慧能力，并快速在各行业得到了广泛而有效的应用。然而，临床医学领域由于专业性强、场景复杂，大语言模型能否提供准确、可靠、高效的医疗服务，目前还没有形成一致性的结论。本研究从以下几个方面对大语言模型在临床医学中的研究进展进行综述：医学知识和基本医学能力评测情况；临床场景特定能力方面的研究进展；脑血管病等临床疾病及相关临床应用方面的研究进展。 Abstract: In recent years, large language models have demonstrated remarkable intelligence capabilities across various general domains and have been widely and effectively applied in multiple industries. However, due to the high level of specialization and complexity of scenarios in clinical medicine, there is no consensus on whether large language models can provide accurate, reliable, and efficient medical services. This study reviewed the progress of large language models in clinical medicine from the following aspects: evaluation of medical knowledge and basic medical capabilities; research progress on specific capability requirements in clinical scenarios; research progress on clinical diseases such as cerebrovascular diseases and related clinical applications.

Copyright of Chinese Journal of Stroke is the property of Chinese Journal of Stroke Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unbalanced medical and educational resources, low intelligence of the medical system, and reliance on individual experience in surgical operations are common in medical trade.The metaverse with immersive and interactive features is an effective tool to solve the problem.However, most of the existing solutions are based on a specific technology of virtual reality or artificial intelligence or a specific operation, and there is little systematic research on the multifunctional and multi-scenario medical metaverse.Therefore, a multi-scenario artificial intelligent medical model based on metaverse (MetaMed) was proposed, which elaborated the bottom-up implementations from four layers, including the access layer, data layer, technology layer and application layer.MetaMed was mathematically applied in five medical scenarios, i.e., intelligent surgery, online consultation, medical education, robotic surgery and outpatient registration scenarios, which provides references for the construction of medical metaverse in the future.

To address the problem of business process prediction in smart healthcare involving the fusion of multimodal data from interactions among multiple systems, this paper proposes an object-centered explainable prediction method (OCPPP). The approach comprises three modules: 1) Utilizing control flow and data flow constraints between activities for process modelling, Algorithm 1 constructs a Petri net centered on the object to be predicted; 2) Recognizing that business activities share resources during system interactions, individual activities are analysed using AI models, with distinct models applied to different modalities of data in activity logs (e.g., multi-object detection for image data, time-series forecasting for text data); 3) Employing coloured Petri nets and Algorithm 2 for predicting activity durations, integrating outputs from various intelligent models to formulate predictions, including those for low-frequency events. The experimental outcomes indicate that during both individual component analysis and sequential forecasting phases, a versatile selection of AI models enables effective operation. Integrating Petri Nets for system-level predictions enhances explainability through six distinct service compositions. Furthermore, the introduction of precursor transitions and a waiting threshold facilitates the anticipation of infrequent behaviours, thereby augmenting the system’s predictive capabilities for a broader spectrum of occurrences.

Brain-Computer Interface (BCI) is a revolutionary technique that employs wearable electroencephalography (EEG) sensors and artificial intelligence (AI) to monitor and decode brain activity. EEG-based motor imagery (MI) brain signal is widely utilized in various BCI fields including intelligent healthcare, robot control, and smart homes. Yet, the limited capability of decoding brain signals remains a significant obstacle to BCI techniques expansion. In this study, we describe an architecture known as the dual-branch attention temporal convolutional network (DB-ATCNet) for EEG-based MI classification. DB-ATCNet improves MI classification performance with relatively fewer parameters by utilizing a dual-branch convolutional network and channel attention. The DB-ATCNet model consists of two primary modules: attention dual-branch convolution (ADBC) and attention temporal fusion convolution (ATFC). The ADBC module utilizes a dual-branch convolutional network to extract low-level MI-EEG features and incorporates channel attention to improve spatial feature extraction. ATFC employs sliding windows with self-attention to obtain the high-level temporal features, and utilizes feature fusion strategies to minimize information loss. The DB-ATCNet achieved subject-independent accuracies of 87.33% and 69.58% in two-class and four-class classification tasks, respectively, on the PhysioNet dataset. On the BCI Competition IV-2a dataset, it achieved an accuracy of 71.34% and 87.54% for subject-independent and subject-dependent evaluations, respectively, surpassing existing methods. The code is available at https://github.com/zk-xju/DB-ATCNet.

Unbalanced medical and educational resources, low intelligence of the medical system, and reliance on individual experience in surgical operations are common in medical trade. The metaverse with immersive and interactive features is an effective tool to solve the problem. However, most of the existing solutions are based on a specific technology of virtual reality or artificial intelligence or a specific operation, and there is little systematic research on the multi-functional and multi-scenario medical metaverse. Therefore, a multi-scenario artificial intelligent medical model based on metaverse (MetaMed) was proposed, which elaborated the bottom-up implementations from four layers, including the access layer, data layer, technology layer and application layer. MetaMed was mathematically applied in five medical scenarios, i.e., intelligent surgery, online consultation, medical education, robotic surgery and outpatient registration scenarios, which provides references for the construction of medical metaverse in the future. [ABSTRACT FROM AUTHOR]

Based on the parallel medical theory, this paper proposes the theory and method of parallel anesthesia, which aims to build an organic and unified intelligent full-cycle anesthesia platform composed of biological humans (medical personnel), robots (mechanical automation equipment) and digital humans (digital medical care) through artificial system modeling, computational experimental analysis and parallel execution of human-computer interaction.Starting from the current status quo and bottleneck of the development of anesthesiology, this paper first conceives multiple intelligent scene modes jointly constructed by biological humans, digital humans and robots in parallel anesthesia systems from the aspects of clinical anesthesia management, crisis management, scientific research, education and teaching, and operation management, and analyzes the role of biological humans in each scene and the complementary relationship between the three.At the same time, the improvement and improvement of parallel anesthesia on patients’ medical safety and medical work efficiency through computational experiments and the implementation correction mode of virtual and real combination are introduced.Finally, by analyzing the ethical issues in the construction of parallel anesthesia system, analyzing the boundary and regulations of automation and intelligent equipment participation in anesthesia, and taking “respect for life, fairness and transparency, efficient operation and labor saving” as the system criteria, an intelligent full-cycle anesthesia platform is established to reduce the repetitive work of medical mechanization, enhance the ability of intelligent decisionmaking assistance, and improve the level of refined treatment and management.

Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications. Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field. To this end, we offer an in-depth review of MKG in this work. Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development. Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed. A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG. In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types. Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

The Internet of Things (IoT) uses wireless networks without infrastructure to install a huge number of wireless sensors that track system, physical, and environmental factors. There are a variety of WSN uses, and some well-known application factors include energy consumption and lifespan duration for routing purposes. The sensors have detecting, processing, and communication capabilities. In this paper, an intelligent healthcare system is proposed which consists of nano sensors that collect real-time health status and transfer it to the doctor's server. Time consumption and various attacks are major concerns, and some existing techniques contain stumbling blocks. Therefore, in this research, a genetic-based encryption method is advocated to protect data transmitted over a wireless channel using sensors to avoid an uncomfortable data transmission environment. An authentication procedure is also proposed for legitimate users to access the data channel. Results show that the proposed algorithm is lightweight and energy efficient, and time consumption is 90% lower with a higher security ratio. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Intelligent Science & Technology is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Breast cancer ranks among the leading causes of death for women globally, making it imperative to swiftly and precisely detect the condition to ensure timely treatment and enhanced chances of recovery. This study focuses on transfer learning with 3D U-Net models to classify ductal carcinoma, the most frequent subtype of breast cancer, in histopathology imaging. In this research work, a dataset of 162 microscopic images of breast cancer specimens is utilized for breast histopathology analysis. Preprocessing the original image data includes shrinking the images, standardizing the intensities, and extracting patches of size 50 × 50 pixels. The retrieved patches were employed to construct a basic 3D U-Net model and a refined 3D U-Net model that had been previously trained on an extensive medical image segmentation dataset. The findings revealed that the fine-tuned 3D U-Net model (97%) outperformed the simple 3D U-Net model (87%) in identifying ductal cancer in breast histopathology imaging. The fine-tuned model exhibited a smaller loss (0.003) on the testing data (0.041) in comparison to the simple model. The disparity in the training and testing accuracy reveals that the fine-tuned model may have overfitted to the training data indicating that there is room for improvement. To progress in computer-aided diagnosis, the research study also adopted various data augmentation methodologies. The experimental approach that was put forward achieved state-of-the-art performance, surpassing the benchmark techniques used in previous studies in the same field, and exhibiting greater accuracy. The presented scheme has promising potential for better cancer detection and diagnosis in practical applications of mammography. [ABSTRACT FROM AUTHOR]

People focus on the intelligent healthcare system expecting sufficient medical facilities even from a remote location. However, to make the healthcare system more trustworthy, secure access control plays a vital role in resisting several cyber-attacks. Security threat on medical data is highly sensitive since it is associated with life risks. When a user accesses a healthcare system through the Internet, the main concern is preventing unauthorized access to vital healthcare-related data. Traditional authentication (based on a password, smartcard, and/or biometric) provides a solution to allow such kind of application only to the authorized users. But once a user is authenticated, and he/she is idle for a while, the chances of security threat are reasonably high. This article has designed an intelligent user recognization mechanism for continuous monitoring of the user’s activity throughout the session. We have made the system user-friendly through single sign-on and eliminated trusted third-party dependence to avoid data breaches. Once the authentication is performed, then in a later stage, the continuous monitoring mechanism based on machine learning techniques helps to decide the user’s unique behavior allowing the application server to make better decisions regarding the user’s authenticity in the current session. The security analysis shows that the proposed system is more secure than the traditional authentication mechanisms. [ABSTRACT FROM AUTHOR]

This paper proposes a posture recognition system that can be applied for medical surveillance. The proposed method estimates human posture using mobilenetV2 and long short-term memory (LSTM) to extract the important features of an image. The output of the system was a fully estimated skeleton. We used seven human indoor postures, including lying, sitting, crouching, standing, walking, fighting, and falling, and classified them. The output results are the extraction of the human skeleton and the corresponding labels for the poses. We first experiment with classification using machine learning. The system only achieves approximately 88% accuracy because it is not able to classify similar postures, such as standing and walking. This difference can be caused by the extraction of features for static images, and the machine learning classification algorithm has not reached accuracy with training data. Therefore, we proposed the integration of the LSTM model into the proposed system. LSTM learns the features of the skeleton and provides classification results for postures. As a result, our system improved the accuracy by up to 99%. Similar postures, such as standing and walking, have improved accuracy by up to 7%. In addition, we performed the system on the Jetson Nano hardware. The results show that it can run on a low-profile (44% CPU and 2.1 frames per second) that is capable of applications for remote patient monitoring devices.

Rehabilitation engineering is playing a more vital role in the field of healthcare for humanity. It is providing many assistive devices to diplegia patients (The patients whose conditions are weak in terms of muscle mobility on both sides of the body and their paralyzing effects are high either in the arms or in the legs). Therefore, in order to rehabilitate such types of patients, an intelligent healthcare system is proposed in this research. The electric sticks and chairs are also a type of this system which was used previously to facilitate the diplegia patients. It is worth noting that a voice recognition system along with wireless control feature has been integrated intelligently in the proposed healthcare system in order to replace the common and conventional assistive tools for diplegia patients. These features will make the proposed system more user friendly, convenient and comfortable. The voice recognition system has been used for movements of system in any desired direction along with the ultrasonic sensor and light detecting technology. These sensors detect the obstacles and low light environment intelligently during the movement of the wheelchair and then take the necessary actions accordingly.

6G technology is now attracting many scientific researchers due to its prominent features including high mobility, high data rate, high operating frequency, and ultra-low end-to-end delay. Compared to the 5G, 6G has certain advantages that can transform smart healthcare to a new advanced intelligent healthcare system, where multiple issues and concerns of the services (e.g., slow ambulance response) are effectively resolved. In such a communication system, patients and service providers communicate with each other via an open internet channel. Since the healthcare data is important and very sensitive, security and privacy in the healthcare network become prominent. In this paper, we introduce a 6G-aided intelligent healthcare environment. Our work also proposes a solution called Centerless User-Controlled Single Sign-On (CL-UCSSO) for achieving a convenient and cost-saving communication in a multi-server system constructed. A three-factor mechanism (combining smart card, password and biometrics) and time bound property are integrated to design the protocol with fast authentication that allows patients and providers to efficiently establish secure communications. Security proof of the proposed protocol is provided using well-known verification tools including RoR model, AVISPA simulation and BAN logic. Results of performance comparisons on various aspects show that our work provides more functionalities and incurs less cost compared with the related works.

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The flexible photodetector plays an important role in improving human medical health status. However, the narrow spectral detection range, poor stress stability, and non‐degradation of traditional flexible photodetectors greatly hinder the further development of wearable medical devices. In this paper, a novel flexible infrared photodetector is proposed for intelligent healthcare monitoring using high purity lead sulfide (PbS) nanoparticles on paper‐based flexible substrate synthesized by hydrothermal method and physical friction. The excellent performance of the detector is attributed to the 1.01 eV band gap and six‐arm stellate dendritic structure of PdS, a good combination between PbS and paper substrate via physical friction. As a result, our photodetector demonstrates wide‐spectrum detection capabilities from 365 to 1550 nm. The photodetector at 980 nm (50.4 μW cm−2) shows responsivity of 6.45 mA W−1, detectivity of 6.4 × 1010 Jones, response recovery time of 0.36 s/0.41 s, with good mechanical stability. By comparison, our detector has a wider detection range, better weak signal detection performance, and shorter response time than the performance of the former paper‐based detector. Furthermore, the paper‐based PbS photodetector as a dual‐wavelength photoplethysmography sensor is applied to analyze the oxygen saturation and develop an intelligent bandage to monitor wound healing. This paper‐based PbS photodetector has tremendous potential in the field of wearable medical devices and intelligent medical applications are expected. [ABSTRACT FROM AUTHOR]

In this article, we develop a novel approach for detecting patients’ position using the radial basis function of the neural network. This new approach aims to continuously monitor the patients’ health statistics and real-time prediction, even when they are outside of cellular coverage. Our research is driven by an initiative to innovate a novel healthcare system of significant importance for intelligent and efficient medical services. For example, doctors need to remotely monitor any patient’s health with the provided health statistics derived from data collected from battery-powered Internet of Things sensors. To this end, our proposed method has been quantified with a holistic mathematical analysis and extensive simulations considering realistic network situations. Our results have accredited the efficiency in the prediction of localization for the patients’ position for the anticipated intelligent healthcare system. [ABSTRACT FROM AUTHOR]

With the rapid economic development and the increasing pressure of work and life, people are increasingly eager to obtain real-time intelligent healthcare to monitor their sub-healthy bodies. Healthcare wireless body area network (HWBAN) is one of the key infrastructures to realize intelligent healthcare. Health data is extremely private information, which makes adversaries prefer to carry out data-related interception, modification, and destruction attacks against the HWBAN system. To provide secure protection for HWBAN, many researchers have proposed various HWBAN schemes, but there are still deficiencies in security, performance, and availability. Our paper proposes a lightweight and amplified secure scheme for HWBAN by using fewer Elliptic Curve Cryptography(ECC) operations and Physically Unclonable Function(PUF) to improve security and efficiency at the same time. Furthermore, we allow users to know their health status in real-time through their mobile phones without initiating additional requests to the medical server in our system to achieve better availability. In addition, we provide strict formal security proof to demonstrate the proposed scheme meets the security and reliability requirements in this paper. The detailed comparative analysis illustrates that the proposed scheme has certain advantages in computing, communication, and security.

Breast cancer ranks among the leading causes of death for women globally, making it imperative to swiftly and precisely detect the condition to ensure timely treatment and enhanced chances of recovery. This study focuses on transfer learning with 3D U-Net models to classify ductal carcinoma, the most frequent subtype of breast cancer, in histopathology imaging. In this research work, a dataset of 162 microscopic images of breast cancer specimens is utilized for breast histopathology analysis. Preprocessing the original image data includes shrinking the images, standardizing the intensities, and extracting patches of size 50 × 50 pixels. The retrieved patches were employed to construct a basic 3D U-Net model and a refined 3D U-Net model that had been previously trained on an extensive medical image segmentation dataset. The findings revealed that the fine-tuned 3D U-Net model (97%) outperformed the simple 3D U-Net model (87%) in identifying ductal cancer in breast histopathology imaging. The fine-tuned model exhibited a smaller loss (0.003) on the testing data (0.041) in comparison to the simple model. The disparity in the training and testing accuracy reveals that the fine-tuned model may have overfitted to the training data indicating that there is room for improvement. To progress in computer-aided diagnosis, the research study also adopted various data augmentation methodologies. The experimental approach that was put forward achieved state-of-the-art performance, surpassing the benchmark techniques used in previous studies in the same field, and exhibiting greater accuracy. The presented scheme has promising potential for better cancer detection and diagnosis in practical applications of mammography.

Copyright of Architectural Journal / Jian Zhu Xue Bao is the property of Architectural Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This letter proposes an radio frequency identification (RFID)‐based bidirectional wireless rollover sensor for intelligent wheelchair. This sensor consists of two symmetric meandered dipole‐based RFID tags and a tilt switch. The tilt of the wheelchair can be recognized directly by the chip code received by the RFID reader and no data postprocessing is needed, so it could be used to detect the rollover status and activate rollover protective measures of the intelligent wheelchair. Meanwhile, emergency signal containing the location of the fallen wheelchair can also be sent to the hospitals or aid stations nearby through the RFID reader. A typical application scenario of the sensor has been proposed, modeled, and analyzed. Simulations and measurements of the sensor in this scenario have been carried out to validate its performance. [ABSTRACT FROM AUTHOR]

Background: Huge amounts of health-related data are generated every moment with the rapid development of Internet of Things (IoT) and wearable technologies. These big health data contain great value and can bring benefit to all stakeholders in the health care ecosystem. Currently, most of these data are siloed and fragmented in different health care systems or public and private databases. It prevents the fulfillment of intelligent health care inspired by these big data. Security and privacy concerns and the lack of ensured authenticity trails of data bring even more obstacles to health data sharing. With a decentralized and consensus-driven nature, distributed ledger technologies (DLTs) provide reliable solutions such as blockchain, Ethereum, and IOTA Tangle to facilitate the health care data sharing.Objective: This study aimed to develop a health-related data sharing system by integrating IoT and DLT to enable secure, fee-less, tamper-resistant, highly-scalable, and granularly-controllable health data exchange, as well as build a prototype and conduct experiments to verify the feasibility of the proposed solution.Methods: The health-related data are generated by 2 types of IoT devices: wearable devices and stationary air quality sensors. The data sharing mechanism is enabled by IOTA's distributed ledger, the Tangle, which is a directed acyclic graph. Masked Authenticated Messaging (MAM) is adopted to facilitate data communications among different parties. Merkle Hash Tree is used for data encryption and verification.Results: A prototype system was built according to the proposed solution. It uses a smartwatch and multiple air sensors as the sensing layer; a smartphone and a single-board computer (Raspberry Pi) as the gateway; and a local server for data publishing. The prototype was applied to the remote diagnosis of tremor disease. The results proved that the solution could enable costless data integrity and flexible access management during data sharing.Conclusions: DLT integrated with IoT technologies could greatly improve the health-related data sharing. The proposed solution based on IOTA Tangle and MAM could overcome many challenges faced by other traditional blockchain-based solutions in terms of cost, efficiency, scalability, and flexibility in data access management. This study also showed the possibility of fully decentralized health data sharing by replacing the local server with edge computing devices. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence has constantly reshaped the field of intelligent healthcare and medicine. As a vital technology, multimodal learning has increasingly garnered interest because of data complementarity, comprehensive information fusion, and great application potential. Currently, numerous researchers are dedicating their attention to this field, conducting extensive studies and constructing abundant intelligent systems. Naturally, an open question arises that has multimodal learning delivered universal intelligence in healthcare? To answer this question, we adopt three unique viewpoints for a holistic analysis. Firstly, we conduct a comprehensive survey of the current progress of medical multimodal learning from the perspectives of datasets, task-oriented methods, and universal foundation models. Based on them, we further discuss the proposed question from five issues to explore the real impacts of advanced techniques in healthcare, from data and technologies to performance and ethics. The answer is that current technologies have NOT achieved universal intelligence and there remains a significant journey to undertake. Finally, in light of the above reviews and discussions, we point out ten potential directions for exploration to promote multimodal fusion technologies in the domain, towards the goal of universal intelligence in healthcare. • A comprehensive survey of the current progress of medical multimodal learning. • Explorations of the real impacts of advanced techniques in healthcare. • Potential directions towards the goal of universal intelligence in healthcare. [ABSTRACT FROM AUTHOR]

Neuro-evolutive development from birth until the age of six years is a decisive factor in a child’s quality of life. Early detection of development disorders in early childhood can facilitate necessary diagnosis and/or treatment. Primary-care pediatricians play a key role in its detection as they can undertake the preventive and therapeutic actions requested to promote a child’s optimal development. However, the lack of time and little specific knowledge at primary-care avoid to applying continuous early-detection anomalies procedures. This research paper focuses on the deployment and evaluation of a smart system that enhances the screening of language disorders in primary care. Pediatricians get support to proceed with early referral of language disorders. The proposed model provides them with a decision-support tool for referral actions to trigger essential diagnostic and/or therapeutic actions for a comprehensive individual development. The research was conducted by starting from a sample of 60 cases of children with language disorders. Validation was carried out through two complementary steps: first, by including a team of seven experts from the fields of neonatology, pediatrics, neurology and language therapy, and, second, through the evaluation of 21 more previously diagnosed cases. The results obtained show that therapist positively accepted the system proposal in 18 cases (86%) and suggested system redesign for single referral to a speech therapist in three remaining cases.

The most important part of the early prevention of atherosclerosis and cardiovascular diseases is the estimation of the cardiometabolic risk (CMR). The CMR estimation can be divided into two phases. The first phase is called primary estimation of CMR (PE-CMR) and includes solely diagnostic methods that are non-invasive, easily-obtained, and low-cost. Since cardiovascular diseases are among the main causes of death in the world, it would be significant for regional health strategies to develop an intelligent software system for PE-CMR that would save time and money by extracting the persons with potentially higher CMR and conducting complete tests only on them. The development of such a software system has few limitations - dataset can be very large, data can not be collected at the same time and the same place (eg. data can be collected at different health institutions) and data of some other region are not applicable since every population has own features. This paper presents a MATLAB solution for PE-CMR based on the ensemble of well-learned artificial neural networks guided by evolutionary algorithm or shortly EANN-EA system. Our solution is suitable for research of CMR in population of some region and its accuracy is above 90 %. [ABSTRACT FROM AUTHOR]

Sociometers are wearable sensors that continuously measure body movements, interactions, and speech. The purpose of this study is to test sociometers in a smart environment in a live clinical setting, to assess their reliability in capturing and quantifying data. The long-term goal of this work is to create an intelligent emergency department that captures real-time human interactions using sociometers to sense current system dynamics, predict future state, and continuously learn to enable the highest levels of emergency care delivery. Ten actors wore the devices during five simulated scenarios in the emergency care wards at a large non-profit medical institution. For each scenario, actors recited prewritten or structured dialogue while independent variables, e.g., distance, angle, obstructions, speech behavior, were independently controlled. Data streams from the sociometers were compared to gold standard video and audio data captured by two ward and hallway cameras. Sociometers distinguished body movement differences in mean angular velocity between individuals sitting, standing, walking intermittently, and walking continuously. Face-to-face (F2F) interactions were not detected when individuals were offset by 30°, 60°, and 180° angles. Under ideal F2F conditions, interactions were detected 50 % of the time (4/8 actor pairs). Proximity between individuals was detected for 13/15 actor pairs. Devices underestimated the mean duration of speech by 30-44 s, but were effective at distinguishing the dominant speaker. The results inform engineers to refine sociometers and provide health system researchers a tool for quantifying the dynamics and behaviors in complex and unpredictable healthcare environments such as emergency care. [ABSTRACT FROM AUTHOR]

Background: Intelligent healthcare (IH) is proposed with the fast application of Internet of Things technology in the healthcare area in recent years. It is considered as an expansion of e-health and telemedicine. As the largest developing country, China is investing large amounts of resources to push forward the development of IH. It is one of the centerpieces of the country's New Healthcare Reform, and great expectation is placed on it to help solve the conflict between limited healthcare resources and the large patient population. Materials and Methods: Essential policies, milestones, standards, and specifications from the Chinese government since the 1990s were reviewed to show the brief development history of IH in China. Some typical cases and products have been analyzed to present the current situation. The main problems and future development directions have been summarized. Conclusions: The IH industry in China has great potential and is growing very fast, but a lot of challenges also exist. In the future both government support and the active participation of nongovernmental capital are needed to push forward the whole industry. [ABSTRACT FROM AUTHOR]