Application and Development of Intelligent Medicine in Traditional Chinese Medicine

Digital intelligence technology: new quality productivity for precision traditional Chinese medicine

Traditional Chinese medicine is a complex medical system characterized by multiple metabolites, targets, and pathways, known for its low drug resistance and significant efficacy. However, challenges persist within Traditional Chinese medicine, including difficulties in assessing the quality of Botanical drugs, reliance on experiential knowledge for disease diagnosis and treatment, and a lack of clarity regarding the pharmacological mechanisms of Traditional Chinese medicine. The advancement of digital intelligence technology is driving a shift towards precision medicine within the Traditional Chinese medicine model. This transition propels Traditional Chinese medicine into an era of precision, intelligence, and digitalization. This paper introduces standard digital intelligence technologies and explores the application of digital intelligence technologies in quality control and evaluation of Traditional Chinese medicine, studies the research status of digital intelligence technologies in assisting diagnosis, treatment and prevention of diseases, and further promotes the application and development of digital intelligence technologies in the field of Traditional Chinese medicine.

Evaluation index system of core competence of Traditional Chinese Medicine nurse specialists: A qualitative evidence synthesis

BACKGROUND

Traditional Chinese Medicine nurse specialists, as the nursing team's core, crucially enhance service quality and spread Traditional Chinese Medicine culture, so strengthening their training based on core competencies is vital for advancing Traditional Chinese Medicine nursing internationally.

AIM

Integration of the core competency evaluation indexes of Traditional Chinese Medicine nurse specialists in different geographical regions.

DESIGN

A qualitative systematic review and evidence synthesis.

METHODS

6 English and 4 Chinese databases were systematically searched for studies related to the core competencies of Traditional Chinese Medicine nurse specialists. The JBI critical appraisal tool was used to evaluate the quality of the included literature. The extracted data were thematically integrated using Thomas and Harden's three-stage framework.

RESULTS

The review included 16 studies. The results can be summarized into 3 themes, 7 categories and 21 items. The themes included personality structure (personal qualities, professional qualities), competency structure (professional development competence, general personal competence, organizational and managerial competence) and knowledge structure (professional knowledge and skills, healthcare ethics and regulations).

CONCLUSIONS

The findings of this study have practical implications for the development of training and assessment programs for Traditional Chinese Medicine nurse specialists, providing a more comprehensive and systematic framework to guide professional development. It will be further applied to the training of Traditional Chinese Medicine nurse specialists in the future, serving to enhance the core competitiveness of Traditional Chinese Medicine nurse specialists and promote the development of Traditional Chinese Medicine nursing.

Application and Prospects of Deep Learning Technology in Fracture Diagnosis

Artificial intelligence (AI) is an interdisciplinary field that combines computer technology, mathematics, and several other fields. Recently, with the rapid development of machine learning (ML) and deep learning (DL), significant progress has been made in the field of AI. As one of the fastest-growing branches, DL can effectively extract features from big data and optimize the performance of various tasks. Moreover, with advancements in digital imaging technology, DL has become a key tool for processing high-dimensional medical image data and conducting medical image analysis in clinical applications. With the development of this technology, the diagnosis of orthopedic diseases has undergone significant changes. In this review, we describe recent research progress on DL in fracture diagnosis and discuss the value of DL in this field, providing a reference for better integration and development of DL technology in orthopedics.

Orthopedic disease classification based on breadth-first search algorithm

Orthopedic diseases are widespread worldwide, impacting the body's musculoskeletal system, particularly those involving bones or hips. They have the potential to cause discomfort and impair functionality. This paper aims to address the lack of supplementary diagnostics in orthopedics and improve the method of diagnosing orthopedic diseases. The study uses binary breadth-first search (BBFS), binary particle swarm optimization (BPSO), binary grey wolf optimizer (BGWO), and binary whale optimization algorithm (BWAO) for feature selections, and the BBFS makes an average error of 47.29% less than others. Then we apply six machine learning models, i.e., RF, SGD, NBC, DC, QDA, and ET. The dataset used contains 310 instances and six distinct features. Through experimentation, the RF model led to optimal outcomes during comparison to the remaining models, with an accuracy of 91.4%. The parameters of the RF model were optimized using four optimization algorithms: BFS, PSO, WAO, and GWO. To check how well the optimized RF works on the dataset, this paper uses prediction evaluation metrics such as accuracy, sensitivity, specificity, F-score, and the AUC curve. The results showed that the BFS-RF can improve the performance of the original classifier compared with others with 99.41% accuracy.

Scientific epistemology beliefs and acceptance of Traditional Chinese Medicine: A multigroup analysis based on the UTAUT model in Southern China

Purpose

This study for the first time delves into the intricate relationship between scientific literacy and the acceptance of traditional Chinese medicine (TCM) by employing a multigroup path analysis based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. We adopted Scientific Epistemology Belief (SEB) as an indicator for measuring scientific literacy due to its comprehensive reflection of individuals' understanding of scientific knowledge and knowing. In assessing TCM acceptance, we focused on Chinese parents' receptivity towards pediatric TCM, as it offers a more genuine representation of actual inclinations.

Methods

A convenience sample of 1016 Chinese parents in Southern China was assessed using online Likert-scale questionnaires on SEB and UTAUT determinants (including performance expectancy, social influence, risk awareness, and facilitating conditions). A K-means cluster analysis was employed to discern distinct SEB profiles, followed by a multigroup path analysis to ascertain UTAUT model variations across these profiles.

Results

Five SEB profiles were identified, namely, intermediate, absolutistic, multiplistic, sophisticated, and evidence-based. Evidence-based believers manifested the highest pediatric TCM acceptance, albeit with elements of blind faith, while multiplistic skeptics, prone to questioning everything, displayed the least acceptance. The absolutistic, intermediate, and sophisticated demonstrated moderate TCM acceptance levels, with the intermediate profile outscoring both absolutistic and sophisticated. These findings highlight that individuals with high scientific literacy do not blindly endorse TCM, nor do those with limited scientific understanding fully appreciate TCM's merits.

Conclusion

SEB significantly moderates TCM acceptance factors in the UTAUT model, indicating that extremes in scientific knowledge spectrum result in less balanced TCM perspectives. Our findings pave the way for novel insights into harmonizing modern and traditional medical practices.

Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning

Background

With the development of traditional Chinese medicine (TCM) syndrome knowledge accumulation and artificial intelligence (AI), this study proposes a holistic TCM syndrome differentiation model for the classification prediction of multiple TCM syndromes based on deep learning and accelerates the construction of modern foundational TCM equipment.

Methods

We searched publicly available TCM guidelines and textbooks for expert knowledge and validated these sources using ten-fold cross-validation. Based on the BERT and CNN models, with the classification constraints from TCM holistic syndrome differentiation, the TCM-BERT-CNN model was constructed, which completes the end-to-end TCM holistic syndrome text classification task through symptom input and syndrome output. We assessed the performance of the model using precision, recall, and F1 scores as evaluation metrics.

Results

The TCM-BERT-CNN model had a higher precision (0.926), recall (0.9238), and F1 score (0.9247) than the BERT, TextCNN, LSTM RNN, and LSTM ATTENTION models and achieved superior results in model performance and predictive classification of most TCM syndromes. Symptom feature visualization demonstrated that the TCM-BERT-CNN model can effectively identify the correlation and characteristics of symptoms in different syndromes with a strong correlation, which conforms to the diagnostic characteristics of TCM syndromes.

Conclusions

The TCM-BERT-CNN model proposed in this study is in accordance with the TCM diagnostic characteristics of holistic syndrome differentiation and can effectively complete diagnostic prediction tasks for various TCM syndromes. The results of this study provide new insights into the development of deep learning models for holistic syndrome differentiation in TCM.

Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare

The convergence of traditional, complementary, and integrative medicine (TCIM) with artificial intelligence (AI) is a promising frontier in healthcare. TCIM is a patient-centric approach that combines conventional medicine with complementary therapies, emphasizing holistic well-being. AI can revolutionize healthcare through data-driven decision-making and personalized treatment plans. This article explores how AI technologies can complement and enhance TCIM, aligning with the shared objectives of researchers from both fields in improving patient outcomes, enhancing care quality, and promoting holistic wellness. This integration of TCIM and AI introduces exciting opportunities but also noteworthy challenges. AI may augment TCIM by assisting in early disease detection, providing personalized treatment plans, predicting health trends, and enhancing patient engagement. Challenges at the intersection of AI and TCIM include data privacy and security, regulatory complexities, maintaining the human touch in patient-provider relationships, and mitigating bias in AI algorithms. Patients' trust, informed consent, and legal accountability are all essential considerations. Future directions in AI-enhanced TCIM include advanced personalized medicine, understanding the efficacy of herbal remedies, and studying patient-provider interactions. Research on bias mitigation, patient acceptance, and trust in AI-driven TCIM healthcare is crucial. In this article, we outlined that the merging of TCIM and AI holds great promise in enhancing healthcare delivery, personalizing treatment plans, preventive care, and patient engagement. Addressing challenges and fostering collaboration between AI experts, TCIM practitioners, and policymakers, however, is vital to harnessing the full potential of this integration.