Medical Knowledge Base Research Articles

Knowledge-injected Prompt Learning for Chinese Biomedical Entity Normalization

The Biomedical Entity Normalization (BEN) task aims to align raw, unstructured medical entities to standard entities, thus promoting data coherence and facilitating better downstream medical applications. Recently, prompt learning methods have shown promising results in the natural language processing field. However, existing research falls short in tackling the more complex Chinese BEN task, especially in the few-shot scenario with limited medical data, and the vast potential of the external medical knowledge base has not yet been fully exploited. To address these challenges, this article proposes a novel Knowledge-injected Prompt Learning (PL-Knowledge) method. Specifically, the approach consists of five stages: candidate entity matching, knowledge extraction, knowledge encoding, knowledge injection, and prediction output. By effectively encoding the knowledge items contained in medical entities and incorporating them into tailor-made knowledge-injected templates, the additional knowledge enhances the model’s ability to capture latent relationships between medical entities, thus achieving a better match with the standard entities. Comprehensive experiments are conducted on a benchmark dataset in both few-shot and full-scale settings. This method outperforms existing baselines, with an average accuracy improvement of 12.96 percentage points in few-shot and 0.94 percentage points in full-data cases, showcasing its excellence in the BEN task.

Critical Thinking Disposition and Influencing Factors Among Sophomore Pediatric Medical Students.

The second year of undergraduate medical education is a critical phase transitioning from basic medical knowledge to specialized learning, requiring strong critical thinking abilities. Pediatric diseases, with their unique characteristics, demand active critical thinking from pediatricians. This study aims to investigate and analyze the critical thinking dispositions of second-year pediatric medical students, identify influencing factors, and propose recommendations for improving teaching methods. This cross-sectional study employed the Chinese version of the California Critical Thinking Disposition Inventory (CTDI-CV) and conducted an online survey among 240 second-year pediatric medical students at Chongqing Medical University, Chongqing, China. The study described the overall CTDI-CV scores and sub-dimension scores (mean ± standard deviation) and analyzed the distribution of critical thinking dispositions using t-tests and trend analysis. A total of 229 students (95.4%) completed the survey, with 58.95% being female. The overall mean critical thinking score was 287.96 ± 39.09, and 139 students (60.70%) exhibited positive or highly positive critical thinking dispositions. Rural students scored lower than non-rural students (t = -2.773, P = 0.0069), while only children scored higher than non-only children (t = 2.659, P = 0.0086). Higher high school academic ranking was associated with higher scores (H = 23.85, P < 0.001). Students whose parents had a bachelor's degree or higher scored significantly better (t = 2.373, P = 0.0188). Interest in pediatrics was linked to higher scores (H = 15.36, P = 0.0015). Positive correlations were found between analyticity, inquisitiveness, and self-confidence (r ≥ 0.75). Second-year pediatric medical students in China generally display strong critical thinking abilities. Factors such as family background, academic performance, parental education level, and interest in pediatrics significantly influence these abilities. Pediatric educators should account for these individual differences to better enhance critical thinking development in students and improve teaching strategies accordingly.

Research on Construction of Knowledge Base for MRI Equipment Maintenance Based on Knowledge Graph

To improve the level of modern hospital management and establish a scientific and effective medical equipment maintenance guarantee system, this paper proposes the construction of a dynamic interactive fault repair knowledge base system for magnetic resonance imaging (MRI) equipment based on knowledge graph technology. This innovative application of intelligent management for large medical equipment involves collecting and organizing actual maintenance data. The paper constructs the medical equipment maintenance knowledge base, with MRI equipment as a case study, and initially implements functions such as fault information retrieval, technical guidance, and training and learning for MRI equipment. These functions aim to provide an objective decision-making basis for the maintenance and management of large medical equipment, enhance the efficiency of fault repair and maintenance, and elevate the construction level of intelligent hospitals.

Development and Prospect of ChatGpt in the Medical Field

In the current era, there exists an uneven distribution of global medical resources, making it a crucial focal point for medical digital transformation. This paper aims to explore the application status, potential challenges, and future development prospects of ChatGPT, a large language model based on OpenAI, in the field of medicine. Given the current scarcity of global medical resources, this study analyzes existing medical use cases and examines the ethical and regulatory environment surrounding ChatGPT under the Transformer model within specialties like orthopedics and ophthalmology. The findings propose that its present advantages lie in its ability to support multiple languages while enhancing efficiency and quality of medical services. Furthermore, measures should be taken to reinforce privacy and security protocols for ChatGPT data usage while ensuring ethical considerations are met. Additionally, it is recommended that future advancements in ChatGPT focus on deepening its medical knowledge base and developing more specialized modules.

XAI-Based Clinical Decision Support Systems: A Systematic Review

With increasing electronic medical data and the development of artificial intelligence, clinical decision support systems (CDSSs) assist clinicians in diagnosis and prescription. Traditional knowledge-based CDSSs follow an accumulated medical knowledgebase and a predefined rule system, which clarifies the decision-making process; however, maintenance cost issues exist in the medical data quality control and standardization processes. Non-knowledge-based CDSSs utilize vast amounts of data and algorithms to effectively make decisions; however, the deep learning black-box problem causes unreliable results. EXplainable Artificial Intelligence (XAI)-based CDSSs provide valid rationales and explainable results. These systems ensure trustworthiness and transparency by showing the recommendation and prediction result process using explainable techniques. However, existing systems have limitations, such as the scope of data utilization and the lack of explanatory power of AI models. This study proposes a new XAI-based CDSS framework to address these issues; introduces resources, datasets, and models that can be utilized; and provides a foundation model to support decision-making in various disease domains. Finally, we propose future directions for CDSS technology and highlight societal issues that need to be addressed to emphasize the potential of CDSSs in the future.

Creation of a Medical Knowledge Base for Unify the Development of Clinical Decision Support Systems Based on the National Metathesaurus

Background: The rapid growth in the volume of medical data, the extensive possibilities of information technology, the transfer of medical document flow to electronic format generates a high demand for the introduction of information and reference assistance tools and clinical decision support systems (CDSS). Work on the creation of CDSS currently combines the expert activities of doctors with the work of information technology specialists, mathematical statisticians, data scientists, knowledge engineers. Most of the developments involving the formation of knowledge bases are created in isolation, without the use of universal approaches that allow combining various solutions. At the heart of any medical knowledge base (MKB) there is a thesaurus, which is a systematized dictionary of terms that helps to standardize terminology, which makes it possible to speed up the search and exchange of information. It includes concept terms and relationships between them, as well as synonyms and various attributes. Aims — creation of a national medical metathesaurus, built on the ontological principle and the development of MKB based on it. Methods. International systematized dictionary of medical terms UMLS (Unified Medical Language System); clinical recommendations for 22 groups of nosologies; reference books of the federal portal of normative reference information of the Ministry of Health of the Russian Federation; electronic medical records – 330 thousand (dataset MIMIC-IV); abstracts of PubMed publications-28 million. Semantic analyzers SemRep (Semantic Repository) and MetaMap were used; methods for evaluating lexical similarity, connectivity, contextual combinability of entities in a subgraph, and mathematical statistics. Results. The first version of the Unified National Medical Nomenclature (UNMN) has been created. It is proved that ontological models are an effective way of presenting structured information. Components of information search engines have been created. Analytical tools for working with metathesaurus have been developed. Conclusions. On the basis of the UNMN and the created tools, it is possible to automate the formation of a clinical picture of the disease (knowledge base) and single-platform development of the CDSS.

Large language models leverage external knowledge to extend clinical insight beyond language boundaries.

Large Language Models (LLMs) such as ChatGPT and Med-PaLM have excelled in various medical question-answering tasks. However, these English-centric models encounter challenges in non-English clinical settings, primarily due to limited clinical knowledge in respective languages, a consequence of imbalanced training corpora. We systematically evaluate LLMs in the Chinese medical context and develop a novel in-context learning framework to enhance their performance. The latest China National Medical Licensing Examination (CNMLE-2022) served as the benchmark. We collected 53 medical books and 381149 medical questions to construct the medical knowledge base and question bank. The proposed Knowledge and Few-shot Enhancement In-context Learning (KFE) framework leverages the in-context learning ability of LLMs to integrate diverse external clinical knowledge sources. We evaluated KFE with ChatGPT (GPT-3.5), GPT-4, Baichuan2-7B, Baichuan2-13B, and QWEN-72B in CNMLE-2022 and further investigated the effectiveness of different pathways for incorporating LLMs with medical knowledge from 7 distinct perspectives. Directly applying ChatGPT failed to qualify for the CNMLE-2022 at a score of 51. Cooperated with the KFE framework, the LLMs with varying sizes yielded consistent and significant improvements. The ChatGPT's performance surged to 70.04 and GPT-4 achieved the highest score of 82.59. This surpasses the qualification threshold (60) and exceeds the average human score of 68.70, affirming the effectiveness and robustness of the framework. It also enabled a smaller Baichuan2-13B to pass the examination, showcasing the great potential in low-resource settings. This study shed light on the optimal practices to enhance the capabilities of LLMs in non-English medical scenarios. By synergizing medical knowledge through in-context learning, LLMs can extend clinical insight beyond language barriers in healthcare, significantly reducing language-related disparities of LLM applications and ensuring global benefit in this field.

GPT-4/4V's performance on the Japanese National Medical Licensing Examination

Background Recent advances in Artificial Intelligence (AI) are changing the medical world, and AI will likely replace many of the actions performed by medical professionals. The overall clinical ability of the AI has been evaluated by its ability to answer a text-based national medical examination. This study uniquely assesses the performance of Open AI's ChatGPT against all Japanese National Medical Licensing Examination (NMLE), including images, illustrations, and pictures. Methods We obtained the questions of the past six years of the NMLE (112th to 117th) from the Japanese Ministry of Health, Labour and Welfare website. We converted them to JavaScript Object Notation (JSON) format. We created an application programming interface (API) to output correct answers using GPT-4 for questions without images and GPT4-V(ision) or GPT4 console for questions with images. Results The percentage of image questions was 723/2400 (30.1%) over the past six years. In all years, GPT-4/4V exceeded the minimum score the examinee should score. In total, over the six years, the percentage of correct answers for basic medical knowledge questions was 665/905 (73.5%); for clinical knowledge questions, 1143/1531 (74.7%); and for image questions 497/723 (68.7%), respectively. Conclusions Regarding medical knowledge, GPT-4/4V met the minimum criteria regardless of whether the questions included images, illustrations, and pictures. Our study sheds light on the potential utility of AI in medical education.

IJCM_395A: Exploring medical student’s perspective on Competency based medical education (CBME) curriculum at Government Medical College in Maharashtra.

Background: Competency based medical education (CBME) is implemented recently in India. The study was conducted to evaluate the perspectives of undergraduate medical students towards CBME curriculum at a Government Medical College in Maharashtra. CBME is helpful in expanding the dimensions besides basic medical knowledge to the core competencies of a high-grade skilled clinical graduate. Moreover, value addition with other competencies such as empathy, professionalism, altruism, communication skills, ethics, and humanities that focuses on health systems as a National goal ‘Health for All’. Objective: To explore medical student’s perspective on Competency based medical education (CBME) curriculum at Government Medical College in Maharashtra. Methodology: This cross-sectional descriptive study was conducted among undergraduate medical students at Dr. SCGMC, Nanded. A validated questionnaire was administered through Google link among medical students. Results: A total of 225 students has participated. Around 130 (58%) students are from first year and 96(43.2%) students opined that learning AETCOM skills are necessary and around 125(55.8%) students opined that there should be sometime for sports activities during week days also and 107(48.2%) students stated that early clinical exposure in the first year is beneficial. Conclusion: The CBME curriculum focuses on learning the competencies needed in clinical practice and provides standards and framework for measuring performance. The thrust of the new curriculum attempts to make medical education in India more outcomes oriented.

Mdpg: a novel multi-disease diagnosis prediction method based on patient knowledge graphs.

Diagnosis prediction, a key factor in enhancing healthcare efficiency, remains a focal point in clinical decision support research. However, the time-series, sparse and multi-noise characteristics of electronic health record (EHR) data make it a great challenge. Existing methods commonly address these issues using RNNs and incorporating medical prior knowledge from medical knowledge bases, but they neglect the local spatial characteristics and spatial-temporal correlation of the data. Consequently, we propose MDPG, a diagnosis prediction model based on patient knowledge graphs. Initially, we represent the electronic visit records of patients as a patient-centered temporal knowledge graph, capturing the local spatial structure and temporal characteristics of the visit information. Subsequently, we design the spatial graph convolution block, temporal self-attention block, and spatial-temporal synchronous graph convolution block to capture the spatial, temporal, and spatial-temporal correlations embedded in them, respectively. Ultimately, we accomplish the prediction of patients' future states through multi-label classification. We conduct comprehensive experiments on two real-world datasets independently and evaluate the results using visit-level precision@k and code-level accuracy@k metrics. The experimental results demonstrate that MDPG outperforms all baseline models, yielding the best performance.

Professional Quality and Skill Requirements of Maternal and Infant Care Teachers

Maternal and infant care teachers are professionals specializing in maternal and infant care, with certain professional quality and skill requirements. This paper aims to summarize the professional quality and skill requirements of maternal and infant care nurses. In terms of professional quality, maternal and child care teachers need to have good communication skills and interpersonal skills to cooperate effectively with family members and other health care staff. They should also have keen observation and patience, able to take meticulous care of the needs of the mother and baby. In addition, they need to have a sense of confidentiality and professional ethics, keep patient information confidential and respect patient privacy. In short, the professional quality of maternal and infant care teachers requires them to have good communication skills, interpersonal skills and observation skills, while maintaining a professional sense of confidentiality and professional ethics. In terms of skill requirements, they need to master basic medical knowledge and nursing skills, and be familiar with maternal rehabilitation and early childhood knowledge. These requirements enable maternal caregivers to provide high quality maternal and infant care services.

A new word embedding model integrated with medical knowledge for deep learning-based sentiment classification

The development of intelligent systems that use social media data for decision-making processes in numerous domains such as politics, business, marketing, and finance, has been made possible by the popularity of social media platforms. However, the utilization of textual data from social media in the healthcare management industry is still somewhat limited when it is compared to other industries. Investigating how current machine learning and natural language processing technologies can be used in the healthcare industry to gauge public sentiment is an important study. Earlier works on healthcare sentiment analysis have utilized traditional word embedding models trained on the general and medical corpus. However, integration of medical knowledge to pre-trained word embedding models has not been considered yet. Word embedding models trained on the general corpus led to the problem of lacking medical knowledge and the models trained on the small size of the medical corpus have limitations in capturing semantic and syntactic properties. This research proposes a new word embedding model named Word Embedding Integrated with Medical Knowledge Vector (WE-iMKVec). The proposed model integrates sentiment lexicons and medical knowledgebases into the pre-trained word embedding to enrich the properties of word embedding. A new medical-aware sentiment polarity score is proposed for the utilization in learning neural-network sentiment and these vectors incorporate with the original pre-trained word vectors. The resulting vectors are enriched with lexicon vectors and the medical knowledge vectors: Adverse Drug Reaction (ADR) vector and Unified Medical Language System (UMLS) vector are used to build the proposed WE-iMKVec model. WE-iMKVec is validated on the five different social media healthcare review datasets and the empirical results showed its superiority over traditional word embedding models in medical sentiment analysis. The highest improvement can be found in the patients.info medical condition dataset where the proposed model outperforms three conventional word2vec models (Google-News, PubMed-PMC, and Drug Reviews) by 12.7 %, 31.4 %, and 25.4 % respectively in terms of F1 score.

Valuable Knowledge Mining: Deep Analysis of Heart Disease and Psychological Causes Based on Large-Scale Medical Data

The task of accurately identifying medical entities and extracting entity relationships from large-scale medical text data has become a hot topic in recent years, aiming to mine potential rules and knowledge. How to conduct in-depth context analysis from biomedical texts, such as medical procedures, diseases, therapeutic drugs, and disease characteristics, and identify valuable knowledge in the medical field is our main research content. Through the process of knowledge mining, a deeper understanding of the complex relationships between various factors in diseases can be gained, which holds significant guiding implications for clinical research. An approach based on context semantic analysis is proposed to realize medical entity recognition and entity relationship extraction. In addition, we build a medical knowledge base related to coronary heart disease and combine the NCBI disease dataset and the medical lexicon dataset extracted from the text as the test data of the experiment. Experimental results show that this model can effectively identify entities in medical text data; the WBC model achieved an F1 score of 89.2% in the experiment, while the CSR model achieved an F1 score of 83.4%, and the result is better than other methods.

Student engagement and learning outcomes: an empirical study applying a four-dimensional framework

ABSTRACT Introduction This study applies Reeve’s four-dimensional student engagement framework to a medical education context to elucidate the relationship between behavioral, emotional, cognitive, and agentic engagement and learning outcomes. Meanwhile, we categorize learning outcomes in knowledge and skills, and added taxonomies to the cognitive education objectives for the knowledge part, including memorization, comprehension, and application. Methods We used the China Medical Student Survey to investigate student engagement, and combined it with the Clinical Medicine Proficiency Test for Medical Schools results as a standardized measurement of learning outcomes. We performed multivariate regression analyses to delve into the effectiveness of different types of student engagement. Moreover, we evaluated the moderating roles of gender and the National College Entrance Examination (NCEE) within the relationships between student engagement and learning outcomes. Results We observed that emotional engagement is most effective in promoting learning outcomes in basic medical knowledge and basic clinical skills. Emotional engagement and cognitive engagement could effectively contribute to learning outcomes in all three aspects of basic medical knowledge. In contrast, behavioral and agentic engagement showed negative effects on learning outcomes. Besides, we found that the results of the NCEE played a positive moderating role. Conclusion This study provides robust evidence for the effectiveness of emotional engagement and cognitive engagement in promoting learning outcomes. Whereas behavioral and agentic engagement may not be good predictors of learning outcomes in macro-level general competence tests. We suggest a combined effort by students and institutions to promote student engagement and bridge the distance between general competency tests and daily learning activities.

Large-view x-ray imaging for medical applications using the world’s only vertically polarized synchrotron radiation beam and a single asymmetric Si crystal

Objective. X-ray microangiography provides detailed information on the internal structure and function of a biological subject. Its ability to evaluate the microvasculature of small animals is useful for acquiring basic and clinical medical knowledge. The following three conditions are necessary to attain detailed knowledge of biological functions: (1) high temporal resolution with sufficient x-ray intensity, (2) high spatial resolution, and (3) a wide field of view. Because synchrotron radiation microangiography systems provide high sapatial resolution and high temporal resolution as a result of their high x-ray intensity, such systems have been developed at various synchrotron radiation facilities, starting with the photon factory, leading to numerous medical discoveries. However, the three aforementioned functions are incompatible with the use of synchrotron radiation because the x-ray intensity decreases when a wide field of view is obtained. To overcome these problems, we developed a new x-ray optical system for microangiography in rats using synchrotron radiation x-rays. Approach. Instead of using monochromatic synchrotron radiation x-rays with a conventional double-crystal monochromator, we used white synchrotron radiation x-rays and an asymmetric Si crystal to simultaneously monochromatize the beam and widen the field of view. Main results. The intensity profile and spatial resolution of the x-ray images were then evaluated. The proposed x-ray optics increased the x-ray intensity and beam width by factors of 1.3 and 2.7, respectively, compared with those of conventional monochromatic x-rays. In addition, in vivo studies on microangiography in rats were performed to confirm that the images had sufficient intensity, spatial resolution, and field of view. One of a series of images taken at 50 ms frame−1 was shown as an example. Significance. This x-ray optics provides sufficient x-ray intensity, high spatial resolution, and a wide field of view. This technique is expected providing new insights into the evaluation of the vascular system.

A framework for modeling, executing, and monitoring hybrid multi-process specifications with bounded global–local memory

So far, approaches for business process modeling, enactment and monitoring have mainly been based on process specifications consisting of a single process model. This setting aptly captures monolithic scenarios from domains in which all possible behaviors can be folded into a single model. However, the same strategy cannot be applied to domains where multiple interacting (procedural) processes simultaneously work over the same objects, in the presence of additional (declarative) constraints relating activities from the same or different processes. A relevant example for this setting is that of healthcare, where co-morbid patients may be subject to multiple clinical pathways at once, in the presence of additional, general constraints capturing basic medical knowledge. To fill this gap, we have previously presented the M3 Framework and an accompanying monitoring technique, which allows for a hybrid representation of a process using both procedural and declarative models, and supports the modular creation of multi-process specifications where domain experts can focus on specific procedures and domain constraints without being forced to merge them into one single specification. In this paper, we make significant extensions to this framework, allowing us to go from simple toy examples towards addressing practical real-life scenarios. We achieve this by introducing a richer form of integration between the interacting process components, in particular supporting asynchronous and synchronous activities that may operate over local and global (shared) data variables. This is framed by a discussion of the business meaning of these concepts, the introduction of the corresponding modeling patterns, and the application of our approach to real-life business processes, the latter being the driving-force behind this paper.

Instructional Design for Optimizing Biochemical Chemistry Education Using Basic Medical Knowledge

Biochemical chemistry, as one of the core courses in medical education, involves the structure, function, metabolism, and other key contents of human biomolecules, which is of great significance in cultivating students' scientific literacy and clinical practice ability. However, the traditional teaching of biochemical chemistry is often based on independent chemical concepts and lacks organic integration with basic medical knowledge, leading to a decline in students' interest in learning and poor learning results. Therefore, there is an urgent need to optimize the teaching design of biochemical chemistry using basic medical knowledge to provide students with a more in-depth learning experience and lay a solid foundation for future medical practice. The article discusses the application of basic medical knowledge in biochemical chemistry education from the aspects of anatomy, physiology, and pathology, and proposes three teaching design strategies, namely, case study, modularized learning, and experimental learning.

Impact of reference panel composition on scores of script concordance test assessing basic nephrology knowledge in undergraduate medical education

Purpose In the assessment of basic medical knowledge, the composition of the reference panel between specialists and primary care (PC) physicians is a contentious issue. We assessed the effect of panel composition on the scores of undergraduate medical students in a script concordance test (SCT). Methods The scale of an SCT on basic nephrology knowledge was set by a panel of nephrologists or a mixed panel of nephrologists and PC physicians. The results of the SCTs were compared with ANOVA for repeated measurements. Concordance was assessed with Bland and Altman plots. Results Forty-five students completed the SCT. Their scores differed according to panel composition: 65.6 ± 9.73/100 points for nephrologists, and 70.27 ± 8.82 for the mixed panel, p < 0.001. Concordance between the scores was low with a bias of −4.27 ± 2.19 and a 95% limit of agreement of −8.96 to −0.38. Panel composition led to a change in the ranking of 71% of students (mean 3.6 ± 2.6 places). Conclusion The composition of the reference panel, either specialist or mixed, for SCT assessment of basic knowledge has an impact on test results and student rankings.

Assessing the influencing factors of out-of-pocket costs on tuberculosis in Sichuan Province: a cross-sectional study

BackgroundAlthough diagnosis and treatment services for tuberculosis (TB) are provided free of charge in most countries, direct non-medical and indirect costs due to absenteeism, also place a significant burden on patients and their families. Sichuan Province has the second highest incidence of TB in China, with an incidence of approximately 100 cases per 100 000 people. However, there are limited research on out-of-pocket expenditure (OOPE) and its influencing factors in TB patients in Sichuan Province.MethodsA retrospective cross-sectional study was conducted on TB patients in designated medical institutions for TB in Sichuan Province from 2017-2021. A face-to-face questionnaire was conducted to obtain the information related to hospitalization of patients, and the multi-level regression model was used to analyse the factors that influence OOPE and total out-of-pocket expenditure (TOOPE) of TB patients.ResultsA total of 2644 patients were investigated, and 74.24% of TB patients and their families experienced catastrophic total costs due to TB. The median total cost was 9223.37 CNY (1429.98 USD), in which the median direct and indirect costs of TB patients were 10185.00 CNY (1579.07 USD) and 2400.00 CNY (372.09 USD), respectively, and indirect costs contributed to 43% of total costs. The median OOPE and TOOPE costs were 6024.00 CNY (933.95 USD) and 11890.50 CNY (1843.49 USD), respectively. OOPE and TOOPE had common influencing factors including whether the patient's family had four or more members, a history of hospitalization, combination with other types of TB, the number of visits before diagnosis, and co-occurrence with chronic disease.ConclusionsThe OOPE and TOOPE for TB patients and their families in Sichuan Province are still heavy. In the long run, it is necessary to strengthen education and awareness campaigns on TB related knowledge, disseminate basic medical knowledge to the public, improve healthcare-seeking behavior, and enhance the healthcare infrastructure to improve the accuracy of TB diagnosis and reduce the significant OOPE and TOOPE faced by TB patients and their families in Sichuan Province.

Practical training of the 5th-year students in clinical departments

The educational process is a significant part of specialists' formation in medicine, including dentistry. Seekers of medical education in universities need the support and guidance of highly specialized teachers for their future realization as professionals. However, it is equally important for the students to be interested in successfully mastering the disciplines in different ways, from classroom work to independent projects. On the difficult student path of becoming a dentist, a teacher who can direct their ward in the right direction in the field of dental practice play a considerable role. Additionally, it is important to provide students with a high-quality theoretical base in university departments, modern clinical equipment for polishing professional practical skills, and constant support in the form of consultations, lectures, and master classes from leading specialists of the higher education institution. General medical disciplines are also a significant component of the educational program of dental students, as these subjects provide basic medical knowledge. Without mastering the materials of these disciplines, it will be difficult for the future dentist to integrate into the general field of medical practice. To fully understand and assimilate the material and topics during the lectures and classes, students need to be interested in learning more than what is prescribed in the curriculum of the educational program of the dental specialty; since this area of medicine is undergoing rapid development today. Therefore, such a type of activity as independent work is introduced in the student's curriculum. This type of work for students majorly contributes to improving discipline on the one hand and increases the ability of clinical thinking of future dentists on the other. The article is written in the form of a short scientific report. Keywords: clinical practice, independent work, practical skills, education seeker, practical classes.