Application and Development of Intelligent Medicine in Traditional Chinese Medicine

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.