Intelligent Medicine Research Articles

The industry demand oriented construction of intelligence medicine specialty in traditional Chinese medicine university: teachers’ perspective

BackgroundThe aim of this study was to explore the relationships between teaching competence, career development, industrial orientation, and specialty construction in the case of intelligent medicine specialties in universities of traditional Chinese medicine, as well as the influencing factors of specialty construction.MethodsTwo-stage sampling with unequal probability was conducted to distribute self-administered questionnaires in the fieldwork. A structural equation model was built to investigate the influencing factors of specialty construction. Forty-two teachers were recruited and completed the questionnaire.ResultsThe study found that teaching competence, career development, and industrial orientation positively affect specialty construction at a significant level. Career development and industrial orientation play chain mediating roles in the effect of teaching competence on specialty construction.ConclusionsIn the construction of intelligent medicine specialties, a professional curriculum system should be built that caters to industrial demand and is combined with industrial development, the traditional mode of teaching should be transformed, and the transformation of theoretical knowledge into practical ability should be promoted. Innovations in teaching modes should be achieved by introducing information technology, and the teacher evaluation system should be optimized.

Polymer hydrogel electronics with adhesive, self-healing, and anti-ultraviolet capabilities for machine learning-promoted electrophysiological detection

While adjustable mechanical properties, diverse biochemical features, and good ionic conductivity enabled by molecular engineering, polymer hydrogels as ionic skins (i-skins) are still limited by multiple challenges in the practical human health monitoring and health diagnosis, such as undesirable ductility and contact adhesion, poor functionality, and insufficient sensing ability. In view of this, the strategy of multiple dynamic interactions was proposed, and a mussel characteristic double-network polymer composite hydrogel (PG-BT2) with tannic acid and borax as dynamic media was reasonably designed. Due to the dynamic reversible interactions from the crosslinked network combined with special functional groups, the obtained PG-BT2 hydrogel attains remarkable stretchability with >1200 % elongation and good toughness of 268.6 kJ/m−3, and also possesses favorable versatility (e.g., self-healing time <2 s, adhesion strength of 4.62 kPa to pigskin, and UV transmittance of 15.3 % at 365 nm). Additionally, the i-skin derived from the PG-BT2 hydrogel exhibits excellent sensing properties, including a low interface contact resistance of 8.3 kΩ at 1 kHz, fast response/recovery time of 0.657 s/0.348 s, and a wide sensing range of 0–1100 % strain. By combining the hydrogel-based i-skin patch with the wireless circuit connected to a Bluetooth module, the constructed sensor system is capable of accurately monitoring micro-expressions and multi-joint movements. The portable system is also demonstrated to capture high-fidelity signals of electrocardiograms available for training artificial intelligence, achieving differentiation and recognition of blood pressure status, which will greatly benefit health management and intelligent medicine.

The Role of Artificial Intelligence Technology in Promoting Socio-Economic Development

This paper comprehensively examines the pivotal role of artificial intelligence (AI) technology in promoting socio-economic development. It commences by introducing the rapid advancements and current status of AI technology, which has permeated diverse industries through breakthroughs in big data, cloud computing, and deep learning. The paper highlights AI’s contributions to economic growth by enhancing production efficiency (e.g., 45% increase in efficiency through automation), optimizing resource allocation (25% raw material utilization improvement), and fostering innovative business models such as personalized customization and intelligent supply chain management. AI also fosters the emergence of new industries (intelligent medicine, education, finance) and creates substantial job opportunities directly for data scientists and algorithm engineers, and indirectly through driving related industries. Furthermore, AI facilitates industrial upgrading, transforming manufacturing into smart manufacturing, enhancing service quality in industries like retail, and modernizing agriculture with smart equipment. A case study on Chongqing Soft River Turing AI Technology Co., Ltd. showcases AI’s practical applications and corporate competitiveness enhancement. Overall, the paper underscores AI’s significance in economic growth, job creation, and industry transformation, while acknowledging challenges like data privacy and job displacement, emphasizing the need for strategic policy guidance and regulation.

Intelligent medicine in focus: the 5 stages of evolution in robot-assisted surgery for prostate cancer in the past 20 years and future implications

Robot-assisted surgery has evolved into a crucial treatment for prostate cancer (PCa). However, from its appearance to today, brain-computer interface, virtual reality, and metaverse have revolutionized the field of robot-assisted surgery for PCa, presenting both opportunities and challenges. Especially in the context of contemporary big data and precision medicine, facing the heterogeneity of PCa and the complexity of clinical problems, it still needs to be continuously upgraded and improved. Keeping this in mind, this article summarized the 5 stages of the historical development of robot-assisted surgery for PCa, encompassing the stages of emergence, promotion, development, maturity, and intelligence. Initially, safety concerns were paramount, but subsequent research and engineering advancements have focused on enhancing device efficacy, surgical technology, and achieving precise multi modal treatment. The dominance of da Vinci robot-assisted surgical system has seen this evolution intimately tied to its successive versions. In the future, robot-assisted surgery for PCa will move towards intelligence, promising improved patient outcomes and personalized therapy, alongside formidable challenges. To guide future development, we propose 10 significant prospects spanning clinical, research, engineering, materials, social, and economic domains, envisioning a future era of artificial intelligence in the surgical treatment of PCa.

Analysis and prediction of research hotspots and trends in heart failure research.

Comprehensive data analyses in heart failure research can provide academics with information and help policymakers formulate relevant policies. We collected data from reports published between 1945 and 2021 to identify research topics, trends, and cross-domains in the heart failure disease literature. Text fragments were extracted and clustered from the titles and abstracts in 270617 publications using artificial intelligence techniques. Two algorithms were used to corroborate the results and ensure that they were reliable. Experts named themes and document clusters based on the results of these semiautomated methods. Using consistent methods, we identified and flagged 107 heart failure topics and 16 large document clusters (divided into two groups by time). The annual vocabularies of research hotspots were calculated to draw attention to niche research fields. Clinical research is an expanding field, followed by basic research and population research. The most frequently raised issues were intensive care treatment for heart failure, applications of artificial intelligence technologies, cardiac assist devices, stem cells, genetics, and regional distribution and use of heart failure-related health care. Risk scoring and classification, care for patients, readmission, health economics of treatment and care, and cell regeneration and signaling pathways were among the fastest-growing themes. Drugs, signaling pathways, and biomarkers were all crucial issues for clinical and basic research in the entire population. Studies on intelligent medicine and telemedicine, interventional therapy for valvular disease, and novel coronavirus have emerged recently. Clinical and population research is increasingly focusing on the customization of intelligent treatments, improving the quality of patients' life, and developing novel treatments. Basic research is increasingly focusing on regenerative medicine, translational medicine, and signaling pathways. Additionally, each research field exhibits mutual fusion characteristics. Medical demands, new technologies, and social support are all potential drivers for these changes.

Processing of clinical notes for efficient diagnosis with feedback attention-based BiLSTM.

Predicting a patient's future health state through the analysis of their clinical records is an emerging area in the field of intelligent medicine. It has the potential to assist healthcare professionals in prescribing treatments safely, making more accurate diagnoses, and improving patient care. However, clinical notes have been underutilized due to their complexity, high dimensionality, and sparsity. Nevertheless, these clinical records hold significant promise for enhancing clinical decision. To tackle these problems, a novel feedback attention-based bidirectional long short-term memory (FABiLSTM) model has been proposed for more effective diagnosis using clinical records. This model incorporates PubMedBERT for filtering irrelevant information, enhances global vector word embeddings for numerical representations and K-means clustering, and performs to explore term frequency and inverse document frequency intricacies. The proposed approach excels in capturing information, aiding accurate disease prediction. The predictive capability is further enhanced with the help of a billiards-inspired optimization algorithm. The effectiveness of the FABiLSTM method has been assessed with the MIMIC-III dataset, yielding impressive results in accuracy, precision, F1 score, and recall score of 98.52%, 98%, 98.2%, and 98.2% individually. These results reveal ways in which the proposed technique excels in comparison with current practices.

Clinical diagnosis, prevention, and treatment of neurodyspepsia syndrome using intelligent medicine.

Against the backdrop of rapid social economy and scientific and technological development, intelligent medical technology expanded based on the Internet plays a crucial role in the innovation and development of the modern medical industry. Intelligent medical technology has completely changed the fixed medical methods of the past, and it can solve the isolated defects between various unit systems, greatly improving the overall informatization level of hospitals. This article analyzed the clinical diagnosis, prevention, and treatment of neurodyspepsia syndrome (NDS) in intelligent medicine. Dyspepsia can cause palpitations, vomiting, abdominal distension, dizziness, and other symptoms so that it can cause discomfort and pain in the middle or around the epigastric region. Therefore, it is necessary to make a correct diagnosis of neurodyspepsia in order to reduce the discomfort of patients. Intelligent medical technology is of great significance in improving patients' symptoms. This study sets up a control group and an experimental group for the experiment. The control group used conventional medication technology, while the experimental group used intelligent medical technology to analyze the patient samples taken. By comparing the factors that affect patients with NDS, it was found that the physical function score of the experimental group was 6.3% lower than that of the control group. Intelligent medical technology has high diagnostic efficiency and can achieve rapid diagnosis of NDS, meeting the clinical diagnosis and prevention requirements of NDS.

Intelligent Medicine Box for Senior Citizens Using Arduino Uno

Intelligent Medicine Box for Senior Citizens Using Arduino Uno

Intelligent Medicine Delivery Robot Trolley for Hospital Admitted Patients

The goal of the research is to enhance hospital patient care through the creation and application of an Intelligent Medicine Delivery Robot Trolley (IMDRT). In order to improve patient happiness, minimise mistakes, and expedite medicine delivery, the IMDRT makes use of cutting-edge technology including robots and artificial intelligence. The research combines human-computer interaction, robotics, and healthcare administration. Initial assessments indicate good feedback, shorter delivery times, and increased accuracy in the administration of medications. Key Words: healthcare, Medicine Delivery, robots and artificial intelligence

S3-Net: A Self-Supervised Dual-Stream Network for Radiology Report Generation.

Intelligent medicine is eager to automatically generate radiology reports to ease the tedious work of radiologists. Previous researches mainly focused on the text generation with encoder-decoder structure, while CNN networks for visual features ignored the long-range dependencies correlated with textual information. Besides, few studies exploit cross-modal mappings to promote radiology report generation. To alleviate the above problems, we propose a novel end-to-end radiology report generation model dubbed Self-Supervised dual-Stream Network (S3-Net). Specifically, a Dual-Stream Visual Feature Extractor (DSVFE) composed of ResNet and SwinTransformer is proposed to capture more abundant and effective visual features, where the former focuses on local response and the latter explores long-range dependencies. Then, we introduced the Fusion Alignment Module (FAM) to fuse the dual-stream visual features and facilitate alignment between visual features and text features. Furthermore, the Self-Supervised Learning with Mask(SSLM) is introduced to further enhance the visual feature representation ability. Experimental results on two mainstream radiology reporting datasets (IU X-ray and MIMIC-CXR) show that our proposed approach outperforms previous models in terms of language generation metrics.

Thermoelectric Hydrogel Electronic Skin for Passive Multimodal Physiological Perception.

Electronic skins (e-skins) are being extensively researched for their ability to recognize physiological data and deliver feedback via electrical signals. However, their wide range of applications is frequently restricted by the indispensableness of external power supplies and single sensory function. Here, we report a passive multimodal e-skin for real-time human health assessment based on a thermoelectric hydrogel. The hydrogel network consists of poly(vinyl alcohol)/low acyl gellan gum with [Fe(CN)6]4-/3- as the redox couple. The introduction of glycerol and Li+ furnishes the gel-based e-skin with antidrying and antifreezing properties, a thermopower of 2.04 mV K-1, fast self-healing in less than 10 min, and high conductivity of 2.56 S m-1. As a prospective application, the e-skin can actively perceive multimodal physiological signals without the need for decoupling, including body temperature, pulse rate, and sweat content, in real time by synergistically coupling sensing and transduction. This work offers a scientific basis and designs an approach to develop passive multimodal e-skins and promotes the application of wearable electronics in advanced intelligent medicine.

Computational modeling for medical data: From data collection to knowledge discovery

&lt;p&gt;Biomedical data encompasses images, texts, physiological signals, and molecular omics data. As the costs of various data acquisition methods, such as genomic sequencing, continue to decrease, the availability of biomedical data is increasing. However, this data often exhibits high dimensionality, heterogeneity, and multimodal characteristics, necessitating the use of advanced computational modeling. Transforming raw data into meaningful biological insights is a critical aspect of computational modeling, which plays an increasingly important role in biomedical research in the era of big data. This review outlines the collection of various types of biomedical data and the challenges faced in data modeling, including high dimensionality, standardization, and privacy protection. Additionally, it addresses the complexity and interpretability of models used to guide knowledge discoveries. The review also discusses computational architectures such as parallel computing, cloud computing, and edge computing, which are essential to meet the demands of large-scale computation. Furthermore, it highlights the driving force of computational modeling in advancing medical research. With the foundation of big data, big models, and big computation, biomedical research is transitioning from experimental observation to theoretical deduction and data-driven approaches, profoundly impacting scientific research methodologies and paradigms. The development of biomedical computational modeling is steering medical research toward intelligent medicine, redefining the scientific research paradigm in biomedicine.&lt;/p&gt;

Feature Separation in Diffuse Lung Disease Image Classification by Using Evolutionary Algorithm-Based NAS.

In the field of diagnosing lung diseases, the application of neural networks (NNs) in image classification exhibits significant potential. However, NNs are considered "black boxes," making it difficult to discern their decision-making processes, thereby leading to skepticism and concern regarding NNs. This compromises model reliability and hampers intelligent medicine's development. To tackle this issue, we introduce the Evolutionary Neural Architecture Search (EvoNAS). In image classification tasks, EvoNAS initially utilizes an Evolutionary Algorithm to explore various Convolutional Neural Networks, ultimately yielding an optimized network that excels at separating between redundant texture features and the most discriminative ones. Retaining the most discriminative features improves classification accuracy, particularly in distinguishing similar features. This approach illuminates the intrinsic mechanics of classification, thereby enhancing the accuracy of the results. Subsequently, we incorporate a Differential Evolution algorithm based on distribution estimation, significantly enhancing search efficiency. Employing visualization techniques, we demonstrate the effectiveness of EvoNAS, endowing the model with interpretability. Finally, we conduct experiments on the diffuse lung disease texture dataset using EvoNAS. Compared to the original network, the classification accuracy increases by 0.56%. Moreover, our EvoNAS approach demonstrates significant advantages over existing methods in the same dataset.

Research on Problem-based Learning Teaching Mode in the Teaching of Introduction to Intelligent Medicine

Research on Problem-based Learning Teaching Mode in the Teaching of Introduction to Intelligent Medicine

LMKG: A large-scale and multi-source medical knowledge graph for intelligent medicine applications

Medical Knowledge Graph (KG) has shown great potential in various healthcare scenarios, such as drug recommendation and clinical decision support system. The factors that determine the role of a medical KG in practical applications are the scale, coverage, and quality of the medical knowledge it can provide. Most existing medical KGs are extracted from a single or a few information sources. However, medical knowledge extracted from insufficient information sources is usually highly incomplete or even biased, which results in a lack of data completeness and may lessen their effectiveness in real-world scenarios. Besides, the coverage of entity and relation types is inadequate in most previous works, which also might restrict their potential usage in future applications. In this paper, we build a unified system that can extract and manage medical knowledge from heterogeneous information sources. We first employ named entity recognition and relation extraction methods to extract knowledge triplets from medical texts. Then we propose a hierarchical entity alignment framework for further knowledge refinement. Based on our system, we construct a large-scale, high-quality, multi-source, and multi-lingual medical KG named LMKG, which includes 13 entity types and 17 relation types, and contains 403,784 entity and 1,225,097 relation instances. We conduct extensive experiments to evaluate the quality of LMKG. Experimental results show that LMKG can effectively enhance the performance of both upstream and downstream intelligent medicine applications. We have publicly released the KG resources and corresponding management service interface to facilitate research and applications in the medical field.

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023).

With the upsurge of artificial intelligence (AI) technology in the medical field, its application in ophthalmology has become a cutting-edge research field. Notably, machine learning techniques have shown remarkable achievements in diagnosing, intervening, and predicting ophthalmic diseases. To meet the requirements of clinical research and fit the actual progress of clinical diagnosis and treatment of ophthalmic AI, the Ophthalmic Imaging and Intelligent Medicine Branch and the Intelligent Medicine Committee of Chinese Medicine Education Association organized experts to integrate recent evaluation reports of clinical AI research at home and abroad and formed a guideline on clinical research evaluation of AI in ophthalmology after several rounds of discussion and modification. The main content includes the background and method of developing this guideline, an introduction to international guidelines on the clinical research evaluation of AI, and the evaluation methods of clinical ophthalmic AI models. This guideline introduces general evaluation methods of clinical ophthalmic AI research, evaluation methods of clinical ophthalmic AI models, and commonly-used indices and formulae for clinical ophthalmic AI model evaluation in detail, and amply elaborates the evaluation methods of clinical ophthalmic AI trials. This guideline aims to provide guidance and norms for clinical researchers of ophthalmic AI, promote the development of regularization and standardization, and further improve the overall level of clinical ophthalmic AI research evaluations.

Guidelines for the application of artificial intelligence in the diagnosis of anterior segment diseases (2023).

The landscape of ophthalmology has observed monumental shifts with the advent of artificial intelligence (AI) technologies. This article is devoted to elaborating on the nuanced application of AI in the diagnostic realm of anterior segment eye diseases, an area ripe with potential yet complex in its imaging characteristics. Historically, AI's entrenchment in ophthalmology was predominantly rooted in the posterior segment. However, the evolution of machine learning paradigms, particularly with the advent of deep learning methodologies, has reframed the focus. When combined with the exponential surge in available electronic image data pertaining to the anterior segment, AI's role in diagnosing corneal, conjunctival, lens, and eyelid pathologies has been solidified and has emerged from the realm of theoretical to practical. In light of this transformative potential, collaborations between the Ophthalmic Imaging and Intelligent Medicine Subcommittee of the China Medical Education Association and the Ophthalmology Committee of the International Translational Medicine Association have been instrumental. These eminent bodies mobilized a consortium of experts to dissect and assimilate advancements from both national and international quarters. Their mandate was not limited to AI's application in anterior segment pathologies like the cornea, conjunctiva, lens, and eyelids, but also ventured into deciphering the existing impediments and envisioning future trajectories. After iterative deliberations, the consensus synthesized herein serves as a touchstone, assisting ophthalmologists in optimally integrating AI into their diagnostic decisions and bolstering clinical research. Through this guideline, we aspire to offer a comprehensive framework, ensuring that clinical decisions are not merely informed but transformed by AI. By building upon existing literature yet maintaining the highest standards of originality, this document stands as a testament to both innovation and academic integrity, in line with the ethos of renowned journals such as Ophthalmology.

Healthcare professionals' and patients' assessments of listed mobile health apps in China: a qualitative study.

In recent years, mobile health (mHealth) has gradually developed in China, and intelligent medicine has become an important research topic. However, there are still significant problems in mHealth applications (apps). Although healthcare professionals and patients are the main users, few studies have focused on their perceptions of the quality of mHealth apps. This study aimed to (1) understand the respective perceptions of healthcare professionals and patients regarding mHealth apps, (2) assess what barriers exist that influence the user experience, and (3) explore how to improve the quality of mHealth apps and the development of the mHealth market in China. The study aims to promote the standardization of mHealth apps and provide effective information for the improvement and development of mHealth apps in the future. Semistructured interviews with 9 patients and 14 healthcare professionals were conducted from January 2022 to April 2022 in the Affiliated Hospital of Xuzhou Medical University. The participants used mHealth apps for more than 3 months, including the "Good Mood" and "Peace and Safe Doctors" apps and apps developed by the hospital that were popular in China. Interview transcripts were analysed using thematic analysis. The following five themes were extracted: different concerns, hidden medical dangers, distance and insecurity, barriers for older people, and having positive perceptions of mHealth apps. Healthcare professionals prioritized simplicity in regard to mHealth apps, whereas patients rated effectiveness as the most crucial factor. The study also revealed several problems with mHealth apps, including insufficient information about physician qualifications, inaccurate medical content, nonstandard treatment processes, and unclear accountability, which led to a sense of distance and insecurity among participants. Older individuals faced additional obstacles when using mHealth apps. Despite these issues, the participants remained optimistic about the future of mHealth app development. The utilization, advantages, and obstacles of mHealth applications for healthcare professionals and patients were explored through semistructured interviews. Despite the promising prospects for mHealth apps in China, numerous issues still need to be addressed. Enhancing the safety monitoring system and developing user-friendly mHealth apps for older adult patients are essential steps to bridge the gap between healthcare providers and patients.

Multiple stimuli-response strategy for on-demand degradable implants: Tannic acid-intercalated polycaprolactone

The versatile potential of polycaprolactone (PCL) as degradable implant materials has been wildly studied. Nevertheless, it is still elusive to tailor the degradation rates as well as the evolution of the mechanical performance in vivo. Taking into account the implant materials require different performance as the stage goes on, this work conceived a novel degradation strategy possessing multiple-stimuli response degradation capability, through incorporating tannic acid (TA) as the cross-linking agent into PCL via facile condensation polymerization. The water contents and degradation rates of the PCL-co-TA composite increased with the increase of TA content. Besides the degradation stimulated by reactive oxygen species (ROS), the PCL-co-TA composite shows accelerated degradation in neutral and basic solution with obvious surface erosion and continuous mass loss within 90 days, however, its mechanical properties maintain stable during this period. Moreover, the PCL-co-TA composite shows controllable drug release capacity with linear release for over 40 days and stable antimicrobial properties for both E.coli and S.aureus. This work provides a novel principle to design degradable implant materials with on-demand degradation behavior. The PCL-co-TA composite shows advanced specificity, sensitivity and diversity of controlling the degradation, demonstrates the great potential applications for tissue engineering and intelligent medicine device.