Inspection, the foremost of the four diagnostic methods in traditional Chinese medicine (TCM), refers to a clinical approach involving observation of the body′s overall condition and specific regions to assess pathological conditions. This method holds an important position in the clinical diagnostic system of TCM. However, traditional inspection is limited by the subjectivity of physician experience and technological means, often resulting in inconsistent and subjective outcomes. With the advancement of science and technology, the generalized inspection diagnosis technology of smart TCM has emerged. This innovative approach integrates classical TCM diagnostic theories with digital-intelligent technologies, enabling accurate collection and intelligent analysis of physiological indicators related to spirit, complexion, physique, and postures. The development of a generalized inspection diagnosis system of smart TCM based on this technology is crucial for promoting the objectivity, precision, and intelligence of TCM diagnosis and treatment. This article proposes that the core feature of the generalized inspection diagnosis system of smart TCM is the analytical logic based on the classic theory of TCM. The framework design of the system can be divided into five modules: data acquisition, data processing, knowledge graph and reasoning, report generation, and report analysis. The limitations and future development trends in data annotation standardization, integration of TCM theory and algorithms are also discussed. The aim is to provide a new paradigm for precise diagnosis in TCM, and advance research and practical implementation of smart TCM generalized inspection.

Cardiovascular diseases (CVDs) impose a significant burden worldwide. Despite the elucidation of the etiology and underlying molecular mechanisms of CVDs by numerous studies and recent discovery of effective drugs, their morbidity, disability, and mortality are still high. Therefore, precise risk stratification and effective targeted therapies for CVDs are warranted. Recent improvements in single-cell RNA sequencing and spatial transcriptomics have improved our understanding of the mechanisms and cells involved in cardiovascular phylogeny and CVDs. Single-cell RNA sequencing can facilitate the study of the human heart at remarkably high resolution and cellular and molecular heterogeneity. However, this technique does not provide spatial information, which is essential for understanding homeostasis and disease. Spatial transcriptomics can elucidate intracellular interactions, transcription factor distribution, cell spatial localization, and molecular profiles of mRNA and identify cell populations causing the disease and their underlying mechanisms, including cell crosstalk. Herein, we introduce the main methods of RNA-seq and spatial transcriptomics analysis and highlight the latest advances in cardiovascular research. We conclude that single-cell RNA sequencing interprets disease progression in multiple dimensions, levels, perspectives, and dynamics by combining spatial and temporal characterization of the clinical phenome with multidisciplinary techniques such as spatial transcriptomics. This aligns with the dynamic evolution of CVDs (e.g., “angina–myocardial infarction–heart failure” in coronary artery disease). The study of pathways for disease onset and mechanisms (e.g., age, sex, comorbidities) in different patient subgroups should improve disease diagnosis and risk stratification. This can facilitate precise individualized treatment of CVDs.

Clinical practice guidelines are an important basis for clinical decision-making, but there are still many factors that prevent physicians from following the guidelines. One of the problems, the limited external validity of the guidelines, ultimately prevents the guidelines from providing an adequate basis for clinical decision-making, thus forming the gap between the guidelines and clinical decisions. Real-world research can enhance the external validity of the guidelines, provide a basis for clinical decision-making, and offer localized evidence for the guidelines, evidence of cost-effectiveness and other evidence for the guidelines. It can also monitor the implementation of the guidelines and help to make up for the lack of evidence in the formulation of traditional guidelines. Therefore, high-quality real-world studies and real-world evidence may be considered in the guideline development process to facilitate guideline optimization and serve as an aid to guideline quality improvement.