Medical Articles Research Articles

The issues of development and functioning of common market of medical equipment and articles within the framework of the Eurasian Economic Union

The market of medical devices within the framework of the Eurasian Economic Union is regulated by special legislation that ensures formation of common market and compliance of products with all necessary requirements. This allows to ensure high level of quality of medical equipment and articles that contributes into improving safety and efficiency of medical procedures. The development of uniform requirements, considering world practices, is an important step, since it allows to guarantee high standards not only at level of single states, but also at level of the entire Union. This approach facilitates reinforcement of confidence to medical production of manufacturers from he Eurasian Economic Union countries. The article examines normative legal base regulating issues of formation and functioning of common market of medical equipment and articles within the framework of the Eurasian Economic Union. The analysis of normative legal base of the Russian Federation in section related to medical articles market was carried out. The analysis of the regulatory framework of the Russian Federation in terms of issues related to the market of medical devices was also carried out. The corresponding conclusions were made based on the study results.

Hunting the Royal Navy’s Medical ‘Snark’: Diagnosis, Prevention and Treatment of Tropical Neurosis in British Sailors, 1943–1945

Summary Between 1943 and 1945, Britain’s Royal Naval Medical Service dispatched urgent missions to investigate physiological and psychological effects suffered by British sailors who were deployed in tropical climates. This article draws on the resulting, previously neglected, medical articles and medical research reports to examine understandings of ‘tropical neurosis’ in the wartime Fleet. Exploring how tropical neurosis was encountered, framed and explained by senior naval medical professionals, this article investigates the condition’s portrayal as a serious health and military risk during the Second World War. This research analyses hitherto unexplored intersections of constructions of race, gender and environment in British naval medical conclusions and recommendations, delivering significant new understandings of the insidious operation of medical racism in Britain’s wartime armed forces. It also establishes, for the first time, how this ambiguous illness was construed as a threat to Britain’s naval war effort, and even the very future of Empire, by the Navy’s medical branch.

JURIDICAL REVIEW OF LAW ENFORCEMENT OF THE MEDICAL CODE OF ETHICSQAGAINST THE DOCTORWHO COMMITS MALPRATIC ACTS MEDICAL

Formulation in this research is: (1) Indonesian Medical Code of Ethics? (2)Judges Considerations in Deciding Case Tagainst Doctors Who Commit Medical Malpracticebased on Decision Number1441/Pid.Sus/2019 /PN.Mks?. The method used is qualitative descriptive with data sources obtained from laws, books, journals and related articles. The results of this research are: In relation to violations of the code of ethics, in health law there are several ethical theories that can be used as a basis for consideration in making a decision to violate the code of ethics for the medical profession. There are three types of ethical theories that are widely known in the health aspect: Classical Ethical Theory, value ethical theory, Contemporary Ethical Theory. Decision 1441/Pid.Sus/2019/PN. Max. regarding malpractice, the perpetrator was acquitted by the judge, the perpetrator was charged with Article 79 letter c in conjunction with Article 51 letter a of Law Number 29 of 2004 concerning Medical Practice and Article 360 ​​paragraph (1) of the Criminal Code, because it was based on the chronology of events, documentary evidence, witness statements and the defendants statement. in this case it has fulfilled the elements contained in the statutory regulations.

Knowledge mapping of job burnout and satisfaction of medical staff and a cross-sectional investigation of county-level hospitals in Southern China

BackgroundJob burnout is a worldwide public health problem that has rarely been addressed among rural medical staff, particularly in county-level hospitals. Hence, we conducted a bibliometric study to gain global insights and research trends and a cross-sectional study to assess the current situation among medical staff of county-level hospitals in Southern China. By conducting these studies, we aim to identify factors associated with burnout among medical staff of county-level hospitals in China and provide recommendations for improvement. MethodsRelevant literature on job burnout among medical personnel was searched using the Web of Science Core Collection (WoSCC). CiteSpace was employed for an in-depth cluster analysis to determine research trends and identify the study population. Subsequently, a cross-sectional survey was randomly conducted in three county-level hospitals in Hunan Province of Southern China. Job burnout and satisfaction were assessed using the Maslach Burnout Inventory MBI-Human Services Survey (MBI-HSS) and Job Satisfaction Questionnaire in a total of 362 valid questionnaires collected. The influence factors of the prevalence of job burnout were investigated using logistic regression. ResultsIn this bibliometric study, 1626 articles were retrieved from 1999 to 2022. China lags behind the United States (US) in both the number and quality of publications in the field of medical staff burnout compared with the US. However, there is a lack of comparative research on job burnout across different job types. County-level medical staff articles are more in line with research hotspots in the field. In total, 362 valid questionnaires were obtained. The total incidence of job burnout among rural medical staff was 27.3 %. Nurses (p < 0.01, OR = 5.95), doctors (p < 0.01, OR = 6.43), and those with administrative jobs (p < 0.01, OR = 7.79) were more likely to experience burnout than those with technical jobs. Medical staff aged 40–49 years (p < 0.01, OR = 0.22) and 50–59 years (p < 0.05, OR = 0.14) were less likely to experience burnout than those aged 20–29 years. Job rewards satisfaction showed a positive correlation with job burnout (p < 0.01, OR = 1.32), but negative correlations with personal development satisfaction (p < 0.05, OR = 0.81) and work internal environment satisfaction (p < 0.05, OR = 0.81). ConclusionBetter working environments, more accessible resources, and higher job rewards contribute to job satisfaction and reduce job burnout among the medical staff of county-level hospitals in China.

Transabdominal ultrasound for the diagnostic workup of parenchymal pancreatic diseases.

This continuing medical education article highlights the central role of transcutaneous sonography in diagnosing parenchymal pancreatic diseases. It emphasizes the importance of in-depth knowledge of sonographic imaging of organ anatomy and a structured examination methodology, particularly for identifying acute and chronic pancreatitis and related complications. The article provides detailed guidance for optimized examination techniques and equipment settings, even under challenging conditions, and discusses the application of ultrasound in various scenarios of pancreatic diseases. Moreover, the relevance of advanced sonographic techniques such as high-frequency sonography, elastography, and contrast-enhanced sonography is illuminated in the context of expanded diagnostic workup.

Building a challenging medical dataset for comparative evaluation of classifier capabilities

Since the 2000s, digitalization has been a crucial transformation in our lives. Nevertheless, digitalization brings a bulk of unstructured textual data to be processed, including articles, clinical records, web pages, and shared social media posts. As a critical analysis, the classification task classifies the given textual entities into correct categories. Categorizing documents from different domains is straightforward since the instances are unlikely to contain similar contexts. However, document classification in a single domain is more complicated due to sharing the same context. Thus, we aim to classify medical articles about four common cancer types (Leukemia, Non-Hodgkin Lymphoma, Bladder Cancer, and Thyroid Cancer) by constructing machine learning and deep learning models. We used 383,914 medical articles about four common cancer types collected by the PubMed API. To build classification models, we split the dataset into 70% as training, 20% as testing, and 10% as validation. We built widely used machine-learning (Logistic Regression, XGBoost, CatBoost, and Random Forest Classifiers) and modern deep-learning (convolutional neural networks - CNN, long short-term memory - LSTM, and gated recurrent unit - GRU) models. We computed the average classification performances (precision, recall, F-score) to evaluate the models over ten distinct dataset splits. The best-performing deep learning model(s) yielded a superior F1 score of 98%. However, traditional machine learning models also achieved reasonably high F1 scores, 95% for the worst-performing case. Ultimately, we constructed multiple models to classify articles, which compose a hard-to-classify dataset in the medical domain.

Influence of automated indexing in Medical Subject Headings (MeSH) selection for pharmacy practice journals

BackgroundThe Medical Subject Headings (MeSH) thesaurus is the controlled vocabulary used to index articles in MEDLINE. MeSH were mainly manually selected until June 2022 when an automated algorithm, the Medical Text Indexer (MTI) automated was fully implemented. A selection of automated indexed articles is then reviewed (curated) by human indexers to ensure the quality of the process. ObjectiveTo describe the association of MEDLINE indexing methods (i.e., manual, automated, and automated + curated) on the MeSH assignment in pharmacy practice journals compared with medical journals. MethodsOriginal research articles published between 2016 and 2023 in two groups of journals (i.e., the Big-five general medicine and three pharmacy practice journals) were selected from PubMed using journal-specific search strategies. Metadata of the articles, including MeSH terms and indexing method, was extracted. A list of pharmacy-specific MeSH terms had been compiled from previously published studies, and their presence in pharmacy practice journal records was investigated. Using bivariate and multivariate analyses, as well as effect size measures, the number of MeSH per article was compared between journal groups, geographic origin of the journal, and indexing method. ResultsA total of 8479 original research articles was retrieved: 6254 from the medical journals and 2225 from pharmacy practice journals. The number of articles indexed by the various methods was disproportionate; 77.8 % of medical and 50.5 % of pharmacy manually indexed. Among those indexed using the automated system, 51.1 % medical and 10.9 % pharmacy practice articles were then curated to ensure the indexing quality. Number of MeSH per article varied among the three indexing methods for medical and pharmacy journals, with 15.5 vs. 13.0 in manually indexed, 9.4 vs. 7.4 in automated indexed, and 12.1 vs. 7.8 in automated and then curated, respectively. Multivariate analysis showed significant effect of indexing method and journal group in the number of MeSH attributed, but not the geographical origin of the journal. ConclusionsArticles indexed using automated MTI have less MeSH than manually indexed articles. Articles published in pharmacy practice journals were indexed with fewer number of MeSH compared with general medical journal articles regardless of the indexing method used.

Artificial intelligence meets medical expertise: evaluating GPT-4's proficiency in generating medical article abstracts

Purpose: The advent of large language models like GPT-4 has opened new possibilities in natural language processing, with potential applications in medical literature. This study assesses GPT-4's ability to generate medical abstracts. It compares their quality to original abstracts written by human authors, aiming to understand the effectiveness of artificial intelligence in replicating complex, professional writing tasks. Materials and Methods: A total of 250 original research articles from five prominent radiology journals published between 2021 and 2023 were selected. The body of these articles, excluding the abstracts, was fed into GPT-4, which then generated new abstracts. Three experienced radiologists blindly and independently evaluated all 500 abstracts using a five-point Likert scale for quality and understandability. Statistical analysis included mean score comparison inter-rater reliability using Fleiss' Kappa and Bland-Altman plots to assess agreement levels between raters. Results: Analysis revealed no significant difference in the mean scores between original and GPT-4 generated abstracts. The inter-rater reliability yielded kappa values indicating moderate to substantial agreement: 0.497 between Observers 1 and 2, 0.753 between Observers 1 and 3, and 0.645 between Observers 2 and 3. Bland-Altman analysis showed a slight systematic bias but was within acceptable limits of agreement. Conclusion: The study demonstrates that GPT-4 can generate medical abstracts with a quality comparable to those written by human experts. This suggests a promising role for artificial intelligence in facilitating the abstract writing process and improving its quality.

Age-related differences in information-seeking behaviors of patients with multiple myeloma.

e19523 Background: For patients with relapsed refractory multiple myeloma (RRMM), obtaining essential disease information for effective self-advocacy and decision-making is challenging, exacerbated by age-related educational barriers and issues related to cultural resources. This study evaluated age-related preferences of patient education resource utilization in RRMM patients. Methods: This survey was conducted on HealthTree Cure Hub, a patient-driven platform designed to efficiently share clinical insights from myeloma specialists, help patients navigate their disease, and contribute to research. The analysis focuses on evaluating the use of 16 different resources and age-related differences among patients with RRMM. Scales included: 1-to-5 for usage (1 - never, 2 - almost never, 3 - occasionally/sometimes, 4 - almost every time, 5 - every time) and 1-to-6 for education (some high school to a graduate degree). Results: Of the 688 participants, 289 had available birth dates and were included in the analysis. The &lt;65 cohort (Y) was 57±6 years old (yo), and the ≥ 65 cohort was 71±5 yo at the time of the survey. The ≥65 cohort (O) was more educated (O: 5.2±1.0; Y: 4.8±1.3, p&lt;0.05), increased their utilization of webinars with time (1st change: 3.1±1.2; &gt; 1 changes: 3.4±1.3, p&lt;0.05) and review of NCCN, ASCO, mSMART, or other blood cancer society guidelines over time (1st change: 2.1±1.2 ; &gt;1 changes: 2.4±1.4, p&lt;0.05). Additionally, the older patients used pamphlets (printed or PDF) created by pharmaceutical companies more than younger patients (O: 2.4±1.1; Y: 2.1±1.2, p&lt;0.05), however, this difference decreased for &gt;1 changes in therapy (Y: 2.1±1.2; O: 2.2±1.2, p&gt;0.05). The older patients utilized social media (non-myeloma expert posts) less at &gt;1 changes in therapy (O: 1.5±0.8; Y: 2.0±1.0, p&lt;0.05), with 69% (n = 69) of the older cohort reported never using this resource, while only 44% (n = 18) of the younger patients reported never using it. The &lt;65 cohort consulted family members, friends or loved ones more than the ≥65 cohort for &gt;1 changes in therapy (Y: 2.5±1.5; O: 1.9±1.1, p&lt;0.05). In addition, the younger patients increased the utilization of medical journal articles with subsequent changes in therapy (2.8±1.2 to 3.0±1.2, p&lt;0.05). Conclusions: The survey revealed that older patients increasingly engaged in webinars and consulted cancer society guidelines with subsequent changes in therapy. While younger patients increased their use of medical journals and more often consulted with their family, friends and loved ones. Both groups demonstrated a wide range of resources (16) used to build their knowledge of myeloma. These findings indicate a desire among patients to comprehend their disease, utilizing diverse resources for information and gaining more knowledge over time.

Improvement of a Machine Learning Model Using a Sentiment Analysis Algorithm to Detect Fake News

These days, the problem of fake news has grown to be a major social and personal concern. With the amount of information generated through social media, it is very crucial to be able to detect and properly take care of that fake information. Previous studies proposed a machine learning model to detect fake news in online Thai health and medical articles. Still, the problem of detecting fake news with similar content but different objectives exists, and the accuracy of the model needs improvement. Therefore, this study aims to solve these problems by adding 33 new features, including textual features, sentiment-based features, and lexicon features, i.e., herbs, fruits, and vegetables, to identify the objective of an article. We trained and tested the model's prediction accuracy on a new dataset containing 582 reliable and 435 unreliable (fake news) articles from eight Thai websites. Our improved classification model using XGBoost with Lasso, the best feature selection method, achieved an accuracy of 97.76% without over-fitting, reflecting a 7.16% improvement over our earlier model.

What makes an article a must read in medical education?

BackgroundThe dissemination of published scholarship is intended to bring new evidence and ideas to a wide audience. However, the increasing number of articles makes it challenging to determine where to focus one’s attention. This study describes factors that may influence decisions to read and recommend a medical education article.MethodsAuthors analyzed data collected from March 2021 through September 2022 during a monthly process to identify “Must Read” articles in medical education. An international team of health sciences educators, learners, and researchers voted on titles and abstracts to advance articles to full text review. Full texts were rated using five criteria: relevance, methodology, readability, originality, and whether it addressed a critical issue in medical education. At an end-of-month meeting, 3–4 articles were chosen by consensus as “Must Read” articles. Analyses were used to explore the associations of article characteristics and ratings with Must Read selection.ResultsOver a period of 19 months, 7487 articles from 856 journals were screened, 207 (2.8%) full texts were evaluated, and 62 (0.8%) were chosen as Must Reads. During screening, 3976 articles (53.1%) received no votes. BMC Medical Education had the largest number of articles at screening (n = 1181, 15.8%). Academic Medicine had the largest number as Must Reads (n = 22, 35.5%). In logistic regressions adjusting for the effect of individual reviewers, all rating criteria were independently associated with selection as a Must Read (p < 0.05), with methodology (OR 1.44 (95%CI = 1.23–1.69) and relevance (OR 1.43 (95%CI = 1.20–1.70)) having the highest odds ratios.ConclusionsOver half of the published medical education articles did not appeal to a diverse group of potential readers; this represents a missed opportunity to make an impact and potentially wasted effort. Our findings suggest opportunities to enhance value in the production and dissemination of medical education scholarship.

Information and vaccine hesitancy: The role of broadband Internet.

We examine the effect of Internet diffusion on the uptake of an important public health intervention: the measles, mumps and rubella (MMR) vaccine. We study England between 2000 and 2011 when Internet diffusion spread rapidly and there was a high profile medical article (falsely) linking the MMR vaccine to autism. OLS estimates suggest Internet diffusion led to an increase in vaccination rates. This result is reversed after allowing for endogeneity of Internet access. The effect of Internet diffusion is sizable. A one standard deviation increase in Internet penetration led to around a 20% decrease in vaccination rates. Localities characterized by higher proportions of high skilled individuals and lower deprivation levels had a larger response to Internet diffusion. These findings are consistent with higher skilled and less-deprived parents responding faster to false information that the vaccine could lead to autism.

New Frontiers in Wide-Awake Surgery.

After studying this article, the participant should be able to: 1. Explain the most important benefits of wide-awake surgery to patients. 2. Tumesce large parts of the body with minimal pain local anesthesia injection technique to eliminate the need for sedation for many operations. 3. Apply tourniquet-free surgery to upper and lower limb operations to avoid the sedation required to tolerate tourniquet pain. 4. Move many procedures out of the main operating room to minor procedure rooms with no increase in infection rates to decrease unnecessary cost and solid waste in surgery. Three disruptive innovations are changing the landscape of surgery: (1) minimally painful injection of large-volume, low-concentration tumescent local anesthesia eliminates the need for sedation for many procedures over the entire body; (2) epinephrine vasoconstriction in tumescent local anesthesia is a good alternative to the tourniquet and proximal nerve blocks in extremity surgery (sedation for tourniquet pain is no longer required for many procedures); and (3) evidence-based sterility and the elimination of sedation enable many larger procedures to move out of the main operating room into minor procedure rooms with no increase in infection rates. This continuing medical education article explores some of the new frontiers in which these changes affect surgery all over the body.

Public and Research Interest in Telemedicine From 2017 to 2022: Infodemiology Study of Google Trends Data and Bibliometric Analysis of Scientific Literature.

Telemedicine offers a multitude of potential advantages, such as enhanced health care accessibility, cost reduction, and improved patient outcomes. The significance of telemedicine has been underscored by the COVID-19 pandemic, as it plays a crucial role in maintaining uninterrupted care while minimizing the risk of viral exposure. However, the adoption and implementation of telemedicine have been relatively sluggish in certain areas. Assessing the level of interest in telemedicine can provide valuable insights into areas that require enhancement. The aim of this study is to provide a comprehensive analysis of the level of public and research interest in telemedicine from 2017 to 2022 and also consider any potential impact of the COVID-19 pandemic. Google Trends data were retrieved using the search topics "telemedicine" or "e-health" to assess public interest, geographic distribution, and trends through a joinpoint regression analysis. Bibliographic data from Scopus were used to chart publications referencing the terms "telemedicine" or "eHealth" (in the title, abstract, and keywords) in terms of scientific production, key countries, and prominent keywords, as well as collaboration and co-occurrence networks. Worldwide, telemedicine generated higher mean public interest (relative search volume=26.3%) compared to eHealth (relative search volume=17.6%). Interest in telemedicine remained stable until January 2020, experienced a sudden surge (monthly percent change=95.7%) peaking in April 2020, followed by a decline (monthly percent change=-22.7%) until August 2020, and then returned to stability. A similar trend was noted in the public interest regarding eHealth. Chile, Australia, Canada, and the United States had the greatest public interest in telemedicine. In these countries, moderate to strong correlations were evident between Google Trends and COVID-19 data (ie, new cases, new deaths, and hospitalized patients). Examining 19,539 original medical articles in the Scopus database unveiled a substantial rise in telemedicine-related publications, showing a total increase of 201.5% from 2017 to 2022 and an average annual growth rate of 24.7%. The most significant surge occurred between 2019 and 2020. Notably, the majority of the publications originated from a single country, with 20.8% involving international coauthorships. As the most productive country, the United States led a cluster that included Canada and Australia as well. European, Asian, and Latin American countries made up the remaining 3 clusters. The co-occurrence network categorized prevalent keywords into 2 clusters, the first cluster primarily focused on applying eHealth, mobile health (mHealth), or digital health to noncommunicable or chronic diseases; the second cluster was centered around the application of telemedicine and telehealth within the context of the COVID-19 pandemic. Our analysis of search and bibliographic data over time and across regions allows us to gauge the interest in this topic, offer evidence regarding potential applications, and pinpoint areas for additional research and awareness-raising initiatives.

Reply to the Letter to the Editor by Stuck et al. on the continued medical education article “Secondary obstructive sleep apnoea” (Somnologie 2023;27:290–297)

Reply to the Letter to the Editor by Stuck et al. on the continued medical education article “Secondary obstructive sleep apnoea” (Somnologie 2023;27:290–297)

New Horizons in Emergency Department Management of Pediatric Sickle Cell Disease.

Sickle cell disease (SCD) is an important topic for emergency medicine audiences because complications of the disease account for a large proportion of hematologic emergencies that are seen in the emergency department each year. Early recognition and aggressive management of emergency complications of SCD can help to reduce the morbidity and mortality associated with this disease. Although the treatment recommendations for some complications of SCD are based on expert opinion, there has been advancement in the understanding of the pathogenesis of the disease and evidence regarding the treatment options available for managing acute complications. This continuing medical education article will provide a summary of the clinical manifestation and management of the most common acute complications of SCD: infection, vaso-occlusive episode, acute chest syndrome, splenic sequestration, stroke, and priapism.

OSSEOINTEGRATION IN LIMB REPLACEMENT SURGERY: AN ANALYSIS OF THE QUALITY AND READABILITY OF AVAILABLE INFORMATION ON THE INTERNET

IntroductionThe use of osseointegrated limb replacement prostheses for amputees are rapidly growing in popularity due to the many benefits over conventional sockets. Many institutions worldwide are beginning to provide this surgical option to their patients and are advertising the service online. The aim of this study was to assess the quality and reading level of related information available online.Materials &amp; MethodsA search was performed using the 3 most popular English language search engines reviewing the top 40 websites from each, using the term osseointegration on. Duplicates, videos, medical journal articles and dentistry websites were excluded. All included websites were assessed for reading using the FKGL, G-Fog and FRE. Quality was assessed using the JAMA benchmark criteria, HONC erificate and a novel 16-point scoring system developed specifically for limb replacement osseointegration.ResultsThe search resulted in 9,985,000 websites, 120 were assessed. 23 websites were included after removal of duplicates, medical journals and dentistry websites. None with reading grades of 6 or lower. Mean FKGL grade was 11.3/18. Mean FRE was 38.83/100. Mean G-FOG was 12.57/20. No websites had HONC. Mean JAMA score was 1.76/4. Mean HSSOIS was 5.70/16. Most provided information on indications(16/23) and explanation of osseointegration 17/23) but few discussed costs(3/23) or restrictions(9/23).ConclusionsThere is already a large amount of information available online regarding osseointegrated prostheses. However, the most accessible information may exceed most patients’ reading levels, limiting is usefulness. Further, the information available is generally of low quality, The quality of information and the readability of online content should be improved to ensure more effective patient education.

Assessment of confidence in medical writing: Development and validation of the first trustworthy measurement tool.

The popularity of medical writing workshops highlights the need for a standard measurement tool to assess the impact of such workshops on participants' confidence in: 1- writing a standard article and 2- using optimal English language. Because such an instrument is not yet available, we undertook this study to devise and evaluate the first measurement tool to assess such confidence. We created an item pool of 50 items by searching Medline, Embase, and Clarivate Analytics to find related articles, using our prior experience, and approaching the key informants. We revised and edited the item pool, and redundant ones were excluded. Finally, the 36-item tool comprised two domains. We tested it in a group of workshop applicants for internal consistency and temporal reliability using Cronbach's α and Pearson correlations and for content and convergent validity using the content validity index and Pearson correlations. The participants had a mean age of 40.3 years, a female predominance (74.3%), and a majority of faculty members (51.4%). The internal consistency showed high reliability (> 0.95). Test-retest reliability showed very high correlations (r = 0.93). The CVI for domain 1 was 0.78, for domain 2 was 0.73, and for the entire instrument was 0.75. This unique, reliable, and valid measurement tool could accurately measure the level of confidence in writing a standard medical article and in using the appropriate English language for this purpose.

Validating the knowledge represented by a self-organizing map with an expert-derived knowledge structure.

Professionals are reluctant to make use of machine learning results for tasks like curriculum development if they do not understand how the results were generated and what they mean. Visualizations of peer reviewed medical literature can summarize enormous amounts of information but are difficult to interpret. This article reports the validation of the meaning of a self-organizing map derived from the Medline/PubMed index of peer reviewed medical literature by its capacity to coherently summarize the references of a core psychiatric textbook. Reference lists from ten editions of Kaplan and Sadock's Comprehensive Textbook of Psychiatry were projected onto a self-organizing map trained on Medical Subject Headings annotating the complete set of peer reviewed medical research articles indexed in the Medline/PubMed database (MedSOM). K-means clustering was applied to references from every edition to examine the ability of the self-organizing map to coherently summarize the knowledge contained within the textbook. MedSOM coherently clustered references into six psychiatric knowledge domains across teneditions (1967-2017). Clustering occurred at the abstract level of broad psychiatric practice including General/adult psychiatry, Child psychiatry, and Administrative psychiatry. The uptake of visualizations of published medical literature by medical experts for purposes like curriculum development depends upon validation of the meaning of the visualizations. The current research demonstrates that a self-organizing map (MedSOM) can validate the stability and coherence of the references used to support the knowledge claims of a standard psychiatric textbook, linking the products of machine learning to a widely accepted standard of knowledge.

Deep Learning in Medical Image Analysis Article Review

Transfer learning, in evaluation to common deep studying strategies which include convolutional neural networks (CNNs), stands proud due to its simplicity, efficiency, and coffee education value, efficaciously addressing the venture of restricted datasets. The importance of scientific picture analysis in both scientific research and medical prognosis can't be overstated, with image techniques like Computer Tomography (CT), Magnetic Resonance Image (MRI), Ultrasound (US), and X-Ray playing a crucial function. Despite their utility in non-invasive analysis, the scarcity of categorized medical images poses a completely unique challenge in comparison to datasets in other pc imaginative and prescient domains, like facial reputation.&#x0D; Given this shortage, switch getting to know has won reputation amongst researchers for medical photo processing. This complete evaluation draws on one hundred amazing papers from IEEE, Elsevier, Google Scholar, Web of Science, and diverse sources spanning 2000 to 2023 It covers vital components, which includes the (i) shape of CNNs, (ii) foundational know-how of switch learning, (iii) numerous techniques for enforcing transfer mastering, (iv) the utility of switch gaining knowledge of throughout numerous sub-fields of medical photo analysis, and (v) a dialogue at the future potentialities of transfer studying within the realm of medical image analysis. This evaluate no longer handiest equips beginners with a scientific understanding of transfer mastering applications in medical image analysis but additionally serves policymakers by means of summarizing the evolving trends in transfer learning within the scientific image domain. This insight might also encourage policymakers to formulate advantageous rules that support the continued development of Transfer learning knowledge of in medical image analysis.