[The application of artificial intellect in health care: prospects and challenges for science and clinical medicine].

The paper analyses publications data related to issues of application of AI and machine learning systems in medical science and practice. The particular attention is paid to key points of AI application in health care: diagnostics, telemedicine, development of new medications, medical rehabilitation and management decision-making process. Despite broad perspectives of applying the given systems in clinical practice and pharmaceutical industry, there are a number of such unsolved problems as ensuring information security, risk of making erroneous decisions and necessity to change existing normative legal base of health care.

Digital Technology in Clinical Medicine: Correspondence.

We found the article on "The Digital Technology in Clinical Medicine: From Calculators to ChatGPT" interesting.1 According to Kulkarni et al., humanity has witnesses four important social system changes, starting with the primitive huntersgatherers and progressing to horticultural, agricultural, industrial, and the current fifth, which is based on digital information technology and has altered the way we present, recognize, and utilize different factors of production. In clinical medicine, digital technology has advanced significantly since the days of computations. According to Kulkarni et al., we have to benefit from these advancements as we all improve the lives of our patients while being cautious not to overturn the doctor-patient relationship. If technology, clinical expertise, and humanistic values are properly balanced, Kulkarni et al. concluded that the future is quite glorious.1 Regulatory organizations are pushing for improvements through clinical trials as a result of recognition of the expanding influence of digital technology in healthcare delivery. The "World Health Organizations Guidelines for Digital Interventions" and the "Food and Drug Administration's Digital Health Center of Excellence" are only two of the projects that are currently being highlighted in the study as efforts to analyze and implement digital health services.

The 100 most influential papers in medical artificial intelligence; a bibliometric analysis.

Objective: To assess the current trends in the field of artificial intelligence in medicine by analysing 100 most cited original articles relevant to the field. METHODS: The bibliometric analysis was conducted in September 2022, and comprised literature search on Scopus database for original articles only. Google and Medical Subject Headings databases were used as resources to extract key words. In order to cover a broad range of articles, original studies comprising human as well as non-human subjects, studies without abstract and studies in languages other than English were part of the inclusion criteria. There was no specific time period applied to the search and no specific selection was done regarding the journals in the database. The screening was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to extract the top 100 most cited articles in the field of artificial intelligence usage in medicine. Data was analysed using SPSS 23. RESULTS: Of the 11,571 studies identified, 100(0.86%) were analysed in detail. The studies were published between 1986 and 2021, with a median of 43 citations (IQR 53) per article. The journal 'Artificial Intelligence in Medicine' accounted for the highest number 9(9%)) of articles, and the United States was the country of origin for most of the articles 36(36%). Conclusion: The trends, development and shortcomings in field of artificial intelligence usage in medicine need to be understood to conduct an effective research in areas that still need attention, and to guide the authorities to direct their funding accordingly.

[Significance and application of quality improvement in clinical medicine]. / 质量改进在临床医学中的意义和应用.

Quality improvement is a methodology which was initially developed and employed in the field of industrial manufacturing. This approach involves implementing a series of interventions aimed at elevating the existing quality standards to a higher level. In daily medical work, there are often spontaneous quality improvements. Medical quality improvements supported by scientific methodology can evaluate medical quality more scientifically and provide objective feedback on the quality of medical work for healthcare professionals. This article provides a concise introduction to quality improvement and shows its application and significance in the field of clinical medicine through examples.

Performance of ChatGPT on Chinese Master's Degree Entrance Examination in Clinical Medicine.

BACKGROUND: ChatGPT is a large language model designed to generate responses based on a contextual understanding of user queries and requests. This study utilised the entrance examination for the Master of Clinical Medicine in Traditional Chinese Medicine to assesses the reliability and practicality of ChatGPT within the domain of medical education. METHODS: We selected 330 single and multiple-choice questions from the 2021 and 2022 Chinese Master of Clinical Medicine comprehensive examinations, which did not include any images or tables. To ensure the test's accuracy and authenticity, we preserved the original format of the query and alternative test texts, without any modifications or explanations. RESULTS: Both ChatGPT3.5 and GPT-4 attained average scores surpassing the admission threshold. Noteworthy is that ChatGPT achieved the highest score in the Medical Humanities section, boasting a correct rate of 93.75%. However, it is worth noting that ChatGPT3.5 exhibited the lowest accuracy percentage of 37.5% in the Pathology division, while GPT-4 also displayed a relatively lower correctness percentage of 60.23% in the Biochemistry section. An analysis of sub-questions revealed that ChatGPT demonstrates superior performance in handling single-choice questions but performs poorly in multiple-choice questions. CONCLUSION: ChatGPT exhibits a degree of medical knowledge and the capacity to aid in diagnosing and treating diseases. Nevertheless, enhancements are warranted to address its accuracy and reliability limitations. Imperatively, rigorous evaluation and oversight must accompany its utilization, accompanied by proactive measures to surmount prevailing constraints.

Investigation of clinical medicine undergraduates' recognition of narrative medicine.

BACKGROUND: Narrative Medicine (NM), a contemporary medical concept proposed in the 21st century, emphasizes the use of narrative as a literary form in medicine. This study aims to explore the understanding about NM and willingness to learn NM among medical students in our hospital. METHODS: A questionnaire survey was conducted among 130 students at Xiangya Medical College of Central South University. RESULTS: The findings revealed that a small percentage of students (3.1%) were familiar with narrative medicine and its training methods. Knowledge about the treatment skills (77.7%) and core content (55.4%) of narrative medicine was limited among the students. Despite this, a majority (63.1%) expressed a lack of interest in further understanding and learning about narrative medicine. Surprisingly, the survey indicated that students possessed a high level of narrative literacy, even without formal training in narrative medicine. Additionally, over half of the surveyed students (61.5%) believed that narrative medicine could benefit their clinical practice. CONCLUSIONS: This study serves as a preliminary basis for the future development of narrative medicine education in China. It highlights the need to prioritize medical humanities education and provide medical students with more opportunities to access information on narrative medicine. By doing so, we can strive to enhance the visibility and promote the integration of narrative medicine into medical humanities education in China.

Performance of ChatGPT on the Chinese Postgraduate Examination for Clinical Medicine: Survey Study.

BACKGROUND: ChatGPT, an artificial intelligence (AI) based on large-scale language models, has sparked interest in the field of health care. Nonetheless, the capabilities of AI in text comprehension and generation are constrained by the quality and volume of available training data for a specific language, and the performance of AI across different languages requires further investigation. While AI harbors substantial potential in medicine, it is imperative to tackle challenges such as the formulation of clinical care standards; facilitating cultural transitions in medical education and practice; and managing ethical issues including data privacy, consent, and bias. OBJECTIVE: The study aimed to evaluate ChatGPT's performance in processing Chinese Postgraduate Examination for Clinical Medicine questions, assess its clinical reasoning ability, investigate potential limitations with the Chinese language, and explore its potential as a valuable tool for medical professionals in the Chinese context. METHODS: A data set of Chinese Postgraduate Examination for Clinical Medicine questions was used to assess the effectiveness of ChatGPT's (version 3.5) medical knowledge in the Chinese language, which has a data set of 165 medical questions that were divided into three categories: (1) common questions (n=90) assessing basic medical knowledge, (2) case analysis questions (n=45) focusing on clinical decision-making through patient case evaluations, and (3) multichoice questions (n=30) requiring the selection of multiple correct answers. First of all, we assessed whether ChatGPT could meet the stringent cutoff score defined by the government agency, which requires a performance within the top 20% of candidates. Additionally, in our evaluation of ChatGPT's performance on both original and encoded medical questions, 3 primary indicators were used: accuracy, concordance (which validates the answer), and the frequency of insights. RESULTS: Our evaluation revealed that ChatGPT scored 153.5 out of 300 for original questions in Chinese, which signifies the minimum score set to ensure that at least 20% more candidates pass than the enrollment quota. However, ChatGPT had low accuracy in answering open-ended medical questions, with only 31.5% total accuracy. The accuracy for common questions, multichoice questions, and case analysis questions was 42%, 37%, and 17%, respectively. ChatGPT achieved a 90% concordance across all questions. Among correct responses, the concordance was 100%, significantly exceeding that of incorrect responses (n=57, 50%; P<.001). ChatGPT provided innovative insights for 80% (n=132) of all questions, with an average of 2.95 insights per accurate response. CONCLUSIONS: Although ChatGPT surpassed the passing threshold for the Chinese Postgraduate Examination for Clinical Medicine, its performance in answering open-ended medical questions was suboptimal. Nonetheless, ChatGPT exhibited high internal concordance and the ability to generate multiple insights in the Chinese language. Future research should investigate the language-based discrepancies in ChatGPT's performance within the health care context.

[Selected novelties for hospital based internal medicine]. / Nouveautés en médecine interne hospitalière.

In this selective overview of articles, we describe new concepts, therapeutic measures and pharmacological agents that may modify current practice in clinical internal medicine. Novelties for the management of cardiovascular disease, such as heart failure, hypoxemic respiratory failure, nosocomial pneumonia and certain allergies are discussed.

À travers quelques articles et études choisis, cet article décrit de nouveaux concepts, mesures thérapeutiques et agents pharmacologiques pouvant modifier les pratiques courantes en médecine interne. Des notions concernant la gestion de maladies cardiovasculaires telles que l'insuffisance cardiaque, les décompensations respiratoires hypoxémiques, les pneumonies nosocomiales et la gestion d'allergies y figurent au premier plan.

[On clinical elites and their role in institutionalization of new national clinical disciplines (the second half of XX century)].

The development of national clinical medicine in Russia in XX century is related not only to evolution of scientific schools, but also to such historical phenomenon as clinical elites. In the article definition of therapeutic elites is proposed. The destiny of therapeutic elites and their role in institutionalization of national clinical disciplines is discussed.

The human vaginal microbiota: from clinical medicine to models to mechanisms.

The composition of the vaginal microbiota is linked to numerous reproductive health problems, including increased susceptibility to infection, pregnancy complications, and impaired vaginal tissue repair; however, the mechanisms contributing to these adverse outcomes are not yet fully defined. In this review, we highlight recent clinical advancements associating vaginal microbiome composition and function with health outcomes. Subsequently, we provide a summary of emerging models employed to identify microbe-microbe interactions contributing to vaginal health, including metagenomic sequencing, multi-omics approaches, and advances in vaginal microbiota cultivation. Last, we review new in vitro, ex vivo, and in vivo models, such as organoids and humanized microbiota murine models, used to define and mechanistically explore host-microbe interactions at the vaginal mucosa.

Multimodal brain tumor image segmentation based on DenseNet.

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient's condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value.

Spotlight on El-Zahrawi, Father of Modern Surgery: Reflections on His Impact on Contemporary Medicine and the Need for Greater Medical Education on Pivotal Figures in Medicine.

Issue: For students in the preclinical years of medical school, it is easy to overlook the narrative component of medicine and become occupied with learning the vast sea of information about the human body. There are limited, if any, options to learn about historical figures in medicine and how they can inform our future in clinical medicine. Evidence: There is an apparent lack of education offered on pivotal figures in medicine across many institutions. The few instances that medical history has been incorporated into the curriculum are further discussed. Implications: In order to incorporate cultural competency in our delivery of care, it is important to consider the diversity of the population we will be serving and how we can prepare to help patients feel heard in their unique issues. In this paper, we propose learning about the true history of certain medical practices, rather than the "colonial" version often utilized in textbooks and lectures, as a means of diversifying students' perspectives of the origins of these practices as well as giving credit where it is due. The time period during which many of these medical practices were cultivated is referred to as the Islamic Golden Age, but scholars who made contributions belonged to many different faiths and cultural backgrounds. El-Zahrawi was a Muslim physician whose principal work, Kitab-at-Tasrif, contains topics on medicine, surgery, midwifery, pharmacology, therapeutics, diet, psychotherapy, and medical chemistry. He pioneered numerous techniques in surgery and invented surgical devices that are still used to this day.

Applied Research of Case-based Learning Teaching in Nephrology Medicine for Professional Postgraduate Students in Clinical Medicine.

Objective: To explore the influence of case-based learning (CBL) teaching methods in comparison to the traditional lecture-based learning (LBL) model in clinical teaching of nephrology for master's degree students in clinical medicine. Methods: Clinical medicine master's degree students who were trained in the Department of Nephrology, The First Affiliated Hospital of Nanjing Medical University from December 2015 to December 2021 were selected as the study objects. The selected students were divided into two groups: the LBL group comprised 16 graduate students who received the traditional LBL model from December 2015 to December 2018, and the CBL group comprised 18 graduate students who received the CBL teaching methods from January 2019 to December 2021. Both groups participated in the professional theoretical knowledge assessment, including objective and subjective questions and calculating the total score), and the examination of clinical skills communication ability, preparation of handling materials, anesthesia techniques, operational skills, aseptic techniques, and postoperative management), at the time of discharge from the department. The independent learning ability (self-management ability, information ability, and learning ability) of students of the two groups after teaching was then assessed, and the satisfaction of the two groups with their respective teaching mode (including satisfaction with the teaching format, teaching effectiveness, interest stimulation, independent learning and the improvement of teamwork ability) was assessed by the questionnaire on the degree of satisfaction of the two groups. Results: The assessment scores of professional theoretical knowledge in the CBL group were significantly higher than those in the LBL group in objective questions, subjective questions, and total scores (P1 = .028; P2 = .036; P3 = .041). The CBL group scored higher than the LBL group in the assessment of communication skills, preparation of operative items, anesthesia technique, operative skills, aseptic technique, and postoperative handling skills, but the differences were not statistically significant (P1 = .071; P2 = .260; P3 = .184; P4 = .127; P5 = .352; P6 = .584). The self-management ability, information ability, and learning ability scores of students in the CBL group were significantly higher than those in the LBL group (P1 = .006; P2 = .013; P3 = .003). Students in the CBL group were significantly higher than those in the LBL group in terms of satisfaction with teaching form, teaching effect, interest stimulation, improvement of independent learning ability, and satisfaction with teamwork ability (P1 = .015; P2 = .008; P3 = .010; P4 = .024; P5 = .022). Conclusions: The CBL teaching model can improve and enhance the clinical thinking ability of clinical medicine master's degree students in nephrology, and stimulate their interest in learning. Professional master's degree students have a high degree of satisfaction with the CBL model.

Learning from the early careers of master clinicians.

BACKGROUND: Master clinicians are recognized as multidimensional experts in clinical medicine. Studying their formative clinical activities could generate insights to guide medical trainees and early career clinicians. OBJECTIVES: To investigate which early career activities were adopted more commonly by master clinicians than their matched peers and to characterize master clinicians' early career activities across institutions and specialties. SUBJECTS AND METHODS: We surveyed master clinicians at seven medical centres about their early career activities. For master clinicians in the Department of Medicine (DOM), we also surveyed matched internist peers. RESULTS: Of 150 master clinician respondents, 65% were internists (DOM); 35% practiced in other specialties. Compared to their internist peers, there was a trend toward internist master clinicians reading more about their patients' conditions (6.0 vs. 4.8 h per week), reading more case reports (4.0 vs. 2.1 per month), engaging in more frequent teaching duties and devoting less time to research. CONCLUSIONS: The early career activities identified in this study can be adopted by clinicians pursuing clinical excellence and promoted by training programs that seek to foster life-long learning.

The vagus nerve in cardiovascular physiology and pathophysiology: From evolutionary insights to clinical medicine.

The parasympathetic nervous system via the vagus nerve exerts profound influence over the heart. Together with the sympathetic nervous system, the parasympathetic nervous system is responsible for fine-tuned regulation of all aspects of cardiovascular function, including heart rate, rhythm, contractility, and blood pressure. In this review, we highlight vagal efferent and afferent innervation of the heart, with a focus on insights from comparative biology and advances in understanding the molecular and genetic diversity of vagal neurons, as well as interoception, parasympathetic dysfunction in heart disease, and the therapeutic potential of targeting the parasympathetic nervous system in cardiovascular disease.