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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.

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.

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)