Intelligent Medical Research Articles

Intelligent Medical Chatbot System For Women’s Healthcare

Even today talking about women health is big taboo in India. The society is unbiased about women especially about their health issues hence they face disparity in the society. Women feel hesitant to talk about or speak about their health issues or problems openly. Thus, the goal of this chatbot is to help women to find information and remedial solutions about their health. Query is processed by the bot and response will be displayed on web application. This chatbot will provide helpful information instantly. It will also provide prediction about the disease that the women might be suffering.So this bot will help to make Right decision and give right advice to women on 24/7 basis. It will act as an helping hand for working women to keep check on their health in their busy routine. At the same time this will also help women in rural areas who are apprehensive to talk about their health issues publicly.

The Recovery Mechanism of Standardized Aphasia in Intelligent Medical Treatment.

A total of 35 patients with aphasia after cerebral infarct were included. Among them, 15 conjunctures were sensory (Wernicke's) aphasia and 20 cases were motor (Broca) aphasia. Perfusion Weighted Imaging (PWI) and Magnetic Resonance Spectroscopy (MRS) were performed on the attached hard area to measure the local cerebral blood flow (rCBF) and sectional cerebral blood compass (rCBV), mean conveyance tense (MTT), point delay (TTP), and N-acetylaspartate (NAA), choline (Cho), creatine (Cr)), and lactic acidic (lactate, Lac) and generally a relative analysis. Results. Among the patients with contaminative aphasia, rCBF was way diminished in the contralateral mirror extent. MTT and TTP were significantly longer than the contralateral mirror range, NAA and Cho were sullenness than the contralateral side, and the Lac peak appeared. The distinction was statistically taken (P < 0.05). Compared with the contralateral mirror circumference, motor aphasia was significantly reduced in rCBF and rCBV, and MTT and TTP were way prolonged. NAA and Cho were reduced compared with the contralateral side, and the Lac peak appeared. The dispute was statistically momentous (P < 0.05). Conclusion. After cerebral infarction, the language cosine extent of patients with aphasia bestows a rank of hypoperfusion and light metabolism, suggesting that it may be the pathogeny of aphasia.

Demand analysis of an intelligent medication administration system for older adults with chronic diseases based on the Kano model

ObjectivesGerontechnology has great potential in promoting older adults’ well-being. With the accelerated aging process, gerontechnology has a promising market prospect. However, most technological developers and healthcare professionals attached importance to products’ effectiveness, and ignored older adults’ demands and user experience, which reduced older adults' adoption intention of gerontechnology use. The inclusion of older adults in the design process of technologies is essential to maximize the effect. This study explored older adults’ demands for a self-developed intelligent medication administration system and proposed optimization schemes, thus providing reference to developing geriatric-friendly technologies and products. MethodsA cross-sectional survey was conducted to explore older adults’ technological demands for the self-developed intelligent medication administration system, and data were analyzed based on the Kano model. A self-made questionnaire was administered from July 2020 to October 2020 after participants used this system for two weeks. The study was registered with the Chinese Clinical Trial Registry (ChiCTR2000040644). ResultsA total of 354 older adults participated in the survey. Four items, namely larger font size, simpler operation process, scheduled medication reminders and reliable hardware, were classified as must-be attributes; three items, namely searching drug instructions through WeChat, more sensitive system and longer battery life, as attractive attributes; one item, viewing disease-related information through WeChat, as the one-dimensional attribute; and the rest were indifferent attributes, including simple and beautiful displays, blocking advertisements automatically, providing user privacy protection protocol, viewing personal medical information only by logged-in users, recording all the medications, ordering medications through WeChat. The satisfaction values were between 0.24 and 0.69, and dissatisfaction values were between 0.06 and 0.94. ConclusionThis study suggested that older adults had personalized technology demands. Including their technological demands and desire may assist in decreasing the digital divide and promoting the satisfaction of e-health and/or m-health. Based on older adults’ demands, our study proposed optimization schemes of the intelligent medication administration system, which may help developers design geriatric-friendly intelligent products and nurses to perform older adults-centered and efficient medication management.

Prediction of the Collapse of Necrotic Femoral Head by CT and X-Ray Examinations before Hip Replacement Based on Intelligent Medical Big Data.

It was to explore the effect of the CT and X-ray examinations before the hip replacement to predict the collapse of the necrotic femoral head under the classification of medical big data based on the decision tree algorithm of the difference grey wolf optimization (GWO) and provide a more effective examination basis for the treatment of patients with the osteonecrosis of the femoral head (ONFH). From January 2019 to January 2021, a total of 152,000 patients with ONFH and hip replacement in the tertiary hospitals were enrolled in this study. They were randomly divided into two groups, the study sample-X group (X-ray examination results) and based-CT group (CT examination results)—76,000 cases in each group. The actual measurement results of the femoral head form the gold standard to evaluate the effect of the two groups of detection methods. The measurement results of X-ray and CT before hip replacement are highly consistent with the detection results of the physical femoral head specimens, which can effectively predict the collapse of ONFH and carry out accurate staging. It is worthy of clinical promotion.

Image Processing Technology in Remote Monitoring and Intelligent Medical System.

In order to study the application of image processing technology in remote monitoring and intelligent medical systems, the principle and implementation method of a remote intelligent image monitoring system based on virtual local area network is proposed; this method analyzes the key technologies to be considered in the remote realization of image monitoring, adopts advanced digital image compression coding and decoding technology and digital image transmission technology, and applies intelligent image processing and recognition technology to display, adjust, and track images; it overcomes the defects that the general monitoring system requires excessive intervention by monitoring personnel and low intelligence. After verification, the experimental results show that the proposed model can accurately and efficiently segment nonoverlapping cervical cell images, and compared with other existing models, this model has both higher segmentation accuracy and faster calculation speed. The application of multicast is still only in the laboratory or small local area network; with the further development of network technology, its application prospects will be very broad.

Physician satisfaction with electronic medical records (EMRs): Time for an intelligent health record?

318 Background: EMRs are devised to improve the quality and efficiency of healthcare delivery and to reduce medical errors. Despite the widespread use of EMRs, various factors can limit their effectiveness in improving healthcare quality. General EMR use has been cited as a factor contributing to increased workload and clinician burnout in oncology and other specialties. The objective of this qualitative research study was to identify barriers perceived by medical oncologists and hematologists (mO/H) in utilizing EMR software and factors associated with levels of satisfaction. Methods: Between January and April 2021, mO/H from across the U.S. were invited to complete a web-based survey about various trends and critical issues in oncology care. Demographics about the physicians and characteristics of their practices were captured as well in the survey. Responses were aggregated and analyzed using descriptive statistics. Results: A total of 369 mO/H completed the survey: 72% practice in a community setting; 47% identified as a hospital employee; they have an average of 19 years of clinical experience and spend on average 86% of their working time in direct patient care, seeing 17 patients per day on average on clinic days. Most (99%) of mO/H surveyed use an EMR software at their practice, with Epic (45%) and OncoEMR (16%) being the most common. Regarding satisfaction, 16% and 50% reported feeling highly satisfied and satisfied, respectively, with their current EMR, and 3% and 11% reported feeling very dissatisfied or dissatisfied, respectively. Some (19%) stated that they have considered changing their EMR, and 68% are unsure how EMR licensing fees for their practice are paid. EMR pain points most commonly experienced were: time-consuming, e.g., too many steps/click (70%); interoperability, e.g., difficulty sharing information across institutions or other EMR software (45%); data entry issues, e.g., difficulty entering clinical information, scheduling patient visits and reminders, or ordering multiple labs (38%); and poor workflow support (31%). The most useful aspects/features of their EMR software reported were availability of information, e.g., preloaded protocols, chemotherapy regimens and pathways (64%); data access (64%); and multiple access points, including remote access (37%). Conclusions: Satisfaction with EMR were generally positive among the mO/H surveyed. However, there are multiple deterrents to the efficient use of current EMR systems. This information is essential in the design of next-generation EMR (an Intelligent Medical Records system) to allow for incorporation of aspects most useful to the end-users, such as pathway access, preloaded information on cancer management as well as ease of access and portability, and a user experience that minimizes clicks and reduces physician time with EMR.

Radiology Alchemy: GAN We Do It?

Radiology Alchemy: GAN We Do It?

Intelligent Medical System with Low-Cost Wearable Monitoring Devices to Measure Basic Vital Signals of Admitted Patients.

This article presents the design of a low-cost Wireless Body Sensor Network (WBSN) for monitoring vital signs including a low-cost smart wristwatch that contains an ESP-32 microcontroller and three sensors: heart rate (HR), blood pressure (BP) and body temperature (BT), and an Internet of Things (IoT) platform. The vital signs data are processed and displayed on an OLED screen of the patient’s wristwatch and sent the data over a wireless connection (Wi-Fi) and a Cloud Thing Board system, to store and manage the data in a data center. The data can be analyzed and notified to medical staff when abnormal signals are received from the sensors based on a set parameters from specialists. The proposed low-cost system can be used in a wide range of applications including field hospitals for asymptotic or mild-condition COVID-19 patients as the system can be used to screen those patients out of symptomatic patients who require more costly facilities in a hospital with considerably low expense and installation time, also suitable for bedridden patients, palliative care patients, etc. Testing experiments of a 60-person sample size showed an acceptable accuracy level compared with standard devices when testing with 60 patient-samples with the mean errors heart rate of 1.22%, systolic blood pressure of 1.39%, diastolic blood pressure of 1.01%, and body temperature of 0.13%. According to testing results with 10 smart devices connected with the platform, the time delay caused by the distance between smart devices and the router is 10 s each round with the longest outdoor distance of 200 m. As there is a short-time delay, it does not affect the working ability of the smart system. It is still making the proposed system be able to show patient’s status and function in emergency cases.

The Current Situation and Influencing Factors of the Alarm Fatigue of Nurses' Medical Equipment in the Intensive Care Unit Based on Intelligent Medical Care.

After rapid development and reform, the health level and medical diagnosis and treatment capabilities of Chinese residents have been significantly improved, and high-quality medical resources have significantly improved the life safety and health of the masses. As the most concentrated area of medical equipment in the hospital, the intensive care unit produces the most alarms during the operation of the equipment. The intensive care unit nurses are responsible for heavier nursing work, and the problem of alarming in other departments is more prominent. Therefore, this paper presents an analysis and research on the current situation and influencing factors of the alarm fatigue of nurse medical equipment in the intensive care unit based on intelligent medicine. This article uses a variety of related methods such as literature data method and questionnaire survey method to deeply study the theoretical knowledge of intelligent medical treatment, medical equipment alarm fatigue device, and so on. The logistic regression analysis method is introduced to classify its influencing factors, and the analysis experiment on the influencing factors of the medical equipment alarm fatigue of nurses in the intensive care unit is designed. The nurses' cognition of clinical alarms and the analysis of clinical alarm fatigue questionnaire data are studied. The alarm fatigue of nurses in the intensive care unit is at a severe level, which needs to be taken seriously in the intensive care unit. Unmarried, high-level positions, long working years, high professional titles, and high education are negatively correlated with alarm fatigue (P < 0.05), and those without an alarm habit are positively correlated with alarm fatigue (P < 0.05), and the number of night shifts per month is related to alarm fatigue. There is no correlation between them (P > 0.05).

Liver Protection Mechanism and Absorption Promotion Technology of Silybin Based on Intelligent Medical Analysis.

With the continuous popularization of smart medicine, the protective effect of silibinin in the liver has attracted much attention. This study mainly explores the liver protection mechanism and absorption promotion technology of silybin based on intelligent medical analysis. Refining of silibinin: accurately weigh 1.0 g of silibinin in a three-necked flask; gradually add 50 mL of anhydrous methanol, reflux and filter the precipitated solid; and weigh it after drying. ICR male mice were taken as experimental subjects and randomly divided into groups of 10 each. The mice in the normal group and the model group were given intragastrically with 0.5% CMC-Na solution; the mice in the silibinin group were given intragastrically with SB/CMC-Na suspension; the mice in the remaining groups were given low, medium, and high-dose suspensions to their stomachs, and silibinin 23 acylate/CMC-Na suspension was administered at a dose of 10 mL/kg for 7 consecutive days. After that, the mice were fasted for 12 hours. After 6 hours of fasting (18 hours after modeling), the blood cells from their orbits were taken, placed in a 37°C water bath for 30 minutes, and centrifuged at 4000 rpm for 10 minutes, and then the serum was taken; the activity equivalent of AST and ALT in serum was measured; serum determination Medium AST and ALT vitality. The mice were killed by decapitation, fresh liver tissue was immediately collected, and part of it was frozen in liquid nitrogen for the RT-PCR test. The hepatocyte expansion and death were observed using a transmission electron microscope, and the oncosis index (OI) was calculated. Another part of the liver tissue was fixed in 4% paraformaldehyde solution, embedded in paraffin, dehydrated, and sliced at 4 μm. Some sections were stained with conventional HE, and the pathological changes of liver cells were observed under light microscope; some sections were subjected to immunohistochemistry. Only one mouse died when 240 mg/kg of silibinin was given 10 minutes after the model was modeled. However, when 240 mg/kg silibinin was given to the mice 20 minutes after modeling, the mortality rate of the mice rose to 50%, and the therapeutic effect was significantly weakened. This research is helpful to advance the research of silybin in liver protection.

Introduction to the Special Section on Security and Privacy of Smart Network Systems

The papers in this special section focus on security and privacy of smart network systems. The explosion in the availability of smart devices and the extensive development of network infrastructure bring a new paradigm, called Smart Network Systems. Rich information can be transferred among the connected devices in Smart Network Systems, which enable a large variety of real-world applications, such as smart cities, smart grid, internet of vehicles, and intelligent medication. Even though Smart Network Systems have enormous values in terms of tackling the computational and communicational overheads and resource management problems, there are still many challenging issues that hinder their further developments. Specifically, distributed computing protocols play a significant role in Smart Network Systems where a large number of smart devices are geographically distributed and logically scattered. However, such distributed techniques are vulnerable to malicious codes and false data injections, internal and/or external attacks, and private information disclosures. Furthermore, designing secure and privacy-preserving protocols in Smart Network Systems is also challenging, since extra communicational and computational costs can be created due to encryption/decryption and the network utility may be degraded under certain security and privacy protection strategies.

Enhancing Electronic Health Systems to End Transmission of Chronic Hepatitis B During COVID-19: A Collaborative Approach.

Enhancing Electronic Health Systems to End Transmission of Chronic Hepatitis B During COVID-19: A Collaborative Approach.

Clinical Effects of Form-Based Management of Forceps Delivery under Intelligent Medical Model.

Background Forceps delivery is one of the most important measures to facilitate vaginal delivery. It can reduce the rate of first cesarean delivery. Frustratingly, adverse maternal and neonatal outcomes associated with forceps delivery have been frequently reported in recent years. There are two major reasons: one is that the abilities of doctors and midwives in forceps delivery vary from hospital to hospital and the other one is lack of regulations in the management of forceps delivery. In order to improve the success rate of forceps delivery and reduce the incidence of maternal and neonatal complications, we applied form-based management to forceps delivery under an intelligent medical model. The aim of this work is to explore the clinical effects of form-based management of forceps delivery. Methods Patients with forceps delivery in Maternal and Child Health Hospital Affiliated to Nanchang University were divided into two groups: form-based patients from January 1, 2019, to December 31, 2020, were selected as the study group, while traditional protocol patients from January 1, 2017, to December 31, 2018, were chosen as the control group. Then, we compared the maternal and neonatal outcomes of these two groups. Results There were significant differences in the maternal and neonatal adverse outcomes such as rate of postpartum hemorrhage, degree of perineal laceration, and incidence of neonatal facial skin abrasions between the two groups, whereas differences in the incidence of asphyxia and intracranial hemorrhage were not significant. Conclusions Form-based management could help us assess the security of forceps delivery comprehensively, as it could not only improve the success rate of the one-time forceps traction scheme but also reduce the incidence of maternal and neonatal adverse outcomes effectively.

Research on Current Situation of Quality Management of Artificial Intelligence Medical Device Enterprises

To understand the current situation of artificial intelligence production enterprise quality management system, so as to provide reference basis for the research and standardization of Artificial Intelligence Medical Device (AIMD) product quality management. Based on YY/T 0287-2017 Medical Device Quality Management System for Regulatory Requirements, Medical Equipment Production and the Quality Control Standard for Independent Software Appendix and Xavier GMLP report, the relevant factors were screened and the questionnaire was designed by combining expert consultation and literature review. Then, a total of 32 representative AIMD enterprises were invited to fill in the questionnaire. Descriptive statistical analysis was performed on the data results using Excel 2016. Through in-depth analysis of the four themes in product planning and design, result output, product quality control and product change, it was found that it was necessary for enterprises participating in the survey to improve the quality management system of AIMD products to different degrees. This study is the first time to systematically investigate the status quo of quality management of AIMD enterprises. The result will be useful for the establishment and continuous improvement of product quality management system. It will also provide a reference for the research of AIMD product quality management and the establishment of the standard.

Attacking and defence pathways for Intelligent Medical Diagnosis System (IMDS)

BackgroundThe Intelligent Medical Diagnosis System (IMDS) has been targeted by the cyber attackers, who aim to damage the Healthcare Critical National Infrastructure (CNI). This research is motivated by the recent cyber attacks happened worldwide that have resulted in the compromise of medical diagnosis records. This study was conducted to demonstrate how the IMDS could be attacked and diagnosis records compromised (i.e. heart disease) and suggest a list of security defence strategies to prevent against such attacks. MethodsThis research developed an IMDS simulation platform by implementing the OpenEMR system. A Cardiac Diagnosis Component is then added to the IMDS. The IMDS is fed with the ECG data (retrieved from the PhysioNet/Computing in Cardiology Challenge 2017). This research then launched systematic ethical hacking, which was tailored to target IMDS diagnosis records. The systematic hacking was based on the NIST ethical hacking method and followed an attack pathway, starting from identifying the entry points of the medical websites, then propagating to gain access to the server, with the ultimate aim of modifying the heart disease diagnosis records. ResultsThe hacking was successful. Four major vulnerabilities (i.e. broken authentication, broken access control, security misconfiguration and using components with known vulnerabilities) were identified in the simulated IMDS and the cardiac diagnosis records were compromised. This research then proposed a list of security defence strategies to prevent such attacks at each possible attacking points along the attacking pathway. ConclusionsThis research demonstrated a systematic ethical hacking to the IMDS, identified four major vulnerabilities and proposed the security defence pathways. It provided novel insights into the protection of IMDS and will benefit researchers in the community to conduct further research in security defence of IMDS.

Lower Limb Joint Nursing and Rehabilitation System Based on Intelligent Medical Treatment.

With the aggravation of the problem of aging population, all kinds of lower limb paralysis caused by various diseases occur frequently. People's demand for lower limb nursing and rehabilitation treatment is growing. In this paper, combined with intelligent medical technology and lower limb kinematics model, this paper proposes to build a lower limb joint nursing and rehabilitation system based on intelligent medical treatment. It is expected that, through the following limb joint rehabilitation robot as the main rehabilitation means, a smart nursing rehabilitation system which can quickly respond to users and realize remote rehabilitation nursing can be designed. First of all, it is clear that the main body of the lower limb joint rehabilitation system consists of the robot body and the state display system. Then, the sensor, amplifier, and data acquisition card are set in the data acquisition system, and the plantar balance force is detected using a FlexiForce film pressure sensor. The final control system mainly includes the main control module program and the lower limb action recognition program. The motor control software adopts PID regulation method, and the lower limb action recognition adopts SVM one-to-one classification method. After the construction of lower limb joint nursing and rehabilitation system, the accuracy rate of action recognition and classification was tested. In the third experiment, the accuracy of all the movements was 100%. Then, the joint displacement and angle changes of the experimenter assisted by the system were analyzed. The experimenter's knee joint and hip joint show a normal walking state, and the joint angle changes tend to be normal. Ten out of 55 rehabilitation system users were randomly selected for interview survey. The total scores of operation convenience, wearing comfort, intensity suitability, and movement science of the system were 90, 83, 84, and 91, respectively. This shows that the rehabilitation action designed by the system is scientific and easy to operate and can be put into use in rehabilitation training after improving the wearing comfort.

A Staging Auxiliary Diagnosis Model for Nonsmall Cell Lung Cancer Based on the Intelligent Medical System.

At present, human health is threatened by many diseases, and lung cancer is one of the most dangerous tumors that threaten human life. In most developing countries, due to the large population and lack of medical resources, it is difficult for doctors to meet patients' needs for medical treatment only by relying on the manual diagnosis. Based on massive medical information, the intelligent decision-making system has played a great role in assisting doctors in analyzing patients' conditions, improving the accuracy of clinical diagnosis, and reducing the workload of medical staff. This article is based on the data of 8,920 nonsmall cell lung cancer patients collected by different medical systems in three hospitals in China. Based on the intelligent medical system, on the basis of the intelligent medical system, this paper constructs a nonsmall cell lung cancer staging auxiliary diagnosis model based on convolutional neural network (CNNSAD). CNNSAD converts patient medical records into word sequences, uses convolutional neural networks to extract semantic features from patient medical records, and combines dynamic sampling and transfer learning technology to construct a balanced data set. The experimental results show that the model is superior to other methods in terms of accuracy, recall, and precision. When the number of samples reaches 3000, the accuracy of the system will reach over 80%, which can effectively realize the auxiliary diagnosis of nonsmall cell lung cancer and combine dynamic sampling and migration learning techniques to train nonsmall cell lung cancer staging auxiliary diagnosis models, which can effectively achieve the auxiliary diagnosis of nonsmall cell lung cancer. The simulation results show that the model is better than the other methods in the experiment in terms of accuracy, recall, and precision.

A deep learning-based medication behavior monitoring system.

The internet of things (IoT) and deep learning are emerging technologies in diverse research fields, including the provision of IT services in medical domains. In the COVID-19 era, intelligent medication behavior monitoring systems for stable patient monitoring are further required, because many patients cannot easily visit hospitals. Several previous studies made use of wearable devices to detect medication behaviors of patients. However, the wearable devices cause inconvenience while equipping the devices. In addition, they suffer from inconsistency problems due to errors of measured values. We devise a medication behavior monitoring system that uses the IoT and deep learning to avoid sensing errors and improve user experiences by effectively detecting various activities of patients. Based on the real-time operation of our proposed IoT device, the proposed solution processes captured images of patents via OpenPose to check medication situations. The proposed system identifies medication status on time by using a human activity recognition scheme and provides various notifications to patients' mobile devices. To support reliable communication between our system and doctors, we employ MQTT protocol with periodic data transmissions. Thus, the measured information of patient's medication status is transmitted to the doctors so that they can periodically perform remote treatments. Experimental results show that all medication behaviors are accurately detected and notified to the doctor efficiently, improving the accuracy of monitoring the patient's medication behavior.

Construction and Evaluation of Intelligent Medical Diagnosis Model Based on Integrated Deep Neural Network.

In recent years, as human life expectancy increases, birth rate decreases and health management concerns; the traditional Healthcare imaging system, with its uneven Healthcare imaging resources, high Healthcare imaging costs, and diagnoses often relying on doctors' clinical experience and equipment level limitations, has affected people's demand for health, so there is a need for a more accurate, convenient, and affordable Healthcare imaging system that allows all people to enjoy fair and quality Healthcare imaging services. This paper discusses the construction and evaluation of an intelligent medical diagnostic model based on integrated deep neural networks, which not only provides a systematic diagnostic analysis of the various symptoms input by the inquirer but also has higher accuracy and efficiency compared with traditional medical diagnostic models. The construction of this model provides a theoretical basis for integrating deep neural networks applied to medical neighborhoods with big data algorithms.

User Acceptance Of Wearable Intelligent Medical Devices: Through a Modified Unified Theory of Acceptance and Use of Technology

Background: The acceptance of wearable intelligent medical devices (WIMDs) and the factors influencing behavioral intention to use them have been scarcely studied. Methods: Integrating the unified theory of acceptance and use of technology, the theory of perceived risk, and basing on the features of wearable intelligent medical devices, we propose a research model to identify factors influencing the acceptance of WIMDs. Using data collected from 2,192 respondents in China from an online survey, we used structural equation modeling to test the measurement and structural models. Findings: The findings suggest that: (1) facilitating conditions (path coefficient = 0.942, P < 0.001) are critical to the use WIMDs; (2) behavioral intention is a significant mediator of the effects of perceived risk, perceived cost, health expectation, perceived ease of use, and social influence on use behavior (path coefficient = 0.210, P < 0.001); (3) health expectation (path coefficient = 0.860, P < 0.001), perceived ease of use (path coefficient = 0.289, P < 0.001), and social influence (path coefficient = 0.153, P < 0.001) are found to play essential roles in predicting behavioral intention; (4) perceived cost (path coefficient = 0.034, P < 0.05) and perceived risk (path coefficient = -0.031, P < 0.05) have no significant effect on behavioral intention; and (5) people with underlying diseases have lower health expectations and perceived costs. Interpretation: The modified unified theory of acceptance and use of technology model in our research is a reliable model to evaluate the user acceptance of WIMDs. Funding Information: This study was supported by a project grant from National Key Research and Development Program of China: Artificial Intelligence Management and Service Model for Public Health and Medicine (2018YFC0116800). Declaration of Interests: All other authors declare no competing interest. Ethics Approval Statement: This study was approved by the Ethics Committee of Beijing TongRen Hospital, Capital Medical University (TRECKY2019-049) and all respondents provided informed consent online. Personal data were anonymized prior to analysis.