Text duplication of papers in four medical related fields

Purpose:To reveal the typical features of text duplication in papers from four medical fields:basic medicine,health management,pharmacology and pharmacy,and public health and preventive medicine.To analyze the reasons for duplication and provide suggestions for the management of medical academic misconduct.Design/methodology/approach:In total,2,469 representative Chinese journal papers were included in our research,which were submitted by researchers in 2020 and 2021.A plagiarism check was carried out using the Academic Misconduct Literature Check System(AMLC).We generated a corrected similarity index based on the AMLC general similarity index for further analysis.We compared the similarity indices of papers in four medical fields and revealed their trends over time;differences in similarity index between review and research articles were also analyzed according to the different fields.Further analysis of 143 papers suspected of plagiarism was also performed from the perspective of sections containing duplication and according to the field of research.Findings:Papers in the field of pharmacology and pharmacy had the highest similarity index(8.67±5.92%),which was significantly higher than that in other fields,except health management.The similarity index of review articles(9.77±10.28%)was significantly higher than that of research articles(7.41±6.26%).In total,143 papers were suspected of plagiarism(5.80%)with similarity indices≥15%;most were papers on health management(78,54.55%),followed by public health and preventive medicine(38,26.58%);90.21%of the 143 papers had duplication in multiple sections,while only 9.79%had duplication in a single section.The distribution of sections with duplication varied among different fields;papers in pharmacology and pharmacy were more likely to have duplication in the data/methods and introduction/background sections,however,papers in health management were more likely to contain duplication in the introduction/background or results/discussion sections.Different structures for papers in different fields may have caused these differences.Research limitations:There were three limitations to our research.Firstly,we observed that a small number of papers have been checked early.It is unknown who conducted the plagiarism check as this can be included in other evaluations,such as applications for Science and technology projects or awards.If the authors carried out the check,text with high similarity indices may have been excluded before submission,meaning the similarity index in our research may have been lower than the original value.Secondly,there were only four medical fields included in our research.Additional analysis on a wider scale is required in the future.Thirdly,only a general similarity index was calculated in our study;other similarity indices were not tested.Practical implications:A comprehensive analysis of similarity indices in four medical fields was performed.We made several recommendations for the supervision of medical academic misconduct and the formation of criteria for defining suspected plagiarism for medical papers,as well as for the improved accuracy of text duplication checks.Originality/value:We quantified the differences between the AMLC general similarity index and the corrected index,described the situation around text duplication and plagiarism in papers from four medical fields,and revealed differences in similarity indices between different article types.We also revealed differences in the sections containing duplication for papers with suspected plagiarism among different fields.

The Role of Artificial Intelligence in the Medical Field

The Artificial Intelligence in the medical field has revolutionized the industry. Recently, A. I. has interested medical practitioners in applying innovation to healthcare systems. A. I. has еmеrgеd as a transformative forcе, revolutionizing the industry by leveraging advanced algorithms and computing powеr to еnhancе various aspects of hеalthcarе dеlivеry. The background highlights that artificial intelligence as innovation promises to transform how medical staffs manage patients and treat and diagnose patients. This comprehensive literature review to identify the relevant sources of information on A. I implementation in healthcare, focusing on the advantages and disadvantages. The obtained results from the materials provided valuable insights into the various means A. I. is used in the medical industry and its effects on patient care and recovery. The findings indicated that;A. I. streamlines Tedious Tasks since it is accurate and gives speedy services enabling early detection of illnesses and leading to positive patient outcomes. A. I. provides Real-Time Data which is essential in addressing patients’ conditions with clear objectives;the use of A. I. improves helps to reduce Burnout in medical practitioners. The use of A. I. helps provide Precision Medicine since it can obtain and analyze large amounts of information. The future directions encompass the implementation of the legal framework, enhancing transparency and accountability, and addressing challenges related to data standardization.

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

Artificial intelligence plays an essential role in the medical and health industries.Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis.However,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected region.Therefore,an optimized deep convolution network is utilized for analyzing disease-affected regions in this work.Different disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning network.This method identifies affected pixels by the gray wolf hunting process.The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis.The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule.The pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation process.This process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.

Consideration of Postgraduate Training under the Cross Field of Medical Information and Engineering

The slogan of“Combination of medicine and engineering”proposed in“Made in China 2025”has aroused great attention to higher engineering education.However,it is a difficult problem and challenge for schools and educators to effectively adapt to the economic and social development,train new medical and engineering talents,and explore and cultivate new subject growth points.In order to deal with engineering change and a new round of scientific and technological revolution,facing the challenge of new engineering construction.This paper will combine the reality and characteristics of colleges and universities,analyze the problems existing in the current medical engineering cross-graduate training,and put forward countermeasures and suggestions to promote the progress and development of science and technology in colleges and universities.

Application and Development of 3D Printing in Medical Field

Different from reduction manufacturing and equal manufacturing, 3D printing is an additive manufacturing method, which transforms 3D model into 2D cross-section data to form entity layer by layer. This makes its processing not limited by complexity of the design model and number of the manufacturing products. It is very suitable for the medical field with high customization requirements. In fact, application of 3D printing technology in the medical field is particularly noticeable. In this paper, application and development of 3D printing technology are reviewed in medical model, rehabilitation equipment, tissue engineering, medical hygiene materials, lab-on-chip. Its applications include medical education, surgical planning, prosthesis customization, tissue culture and biosensor manufacturing and so on. Its wide application is due to its digital model, which makes the whole manufacturing process easier to digitize, so it is more conductive to updating and customization of products via 3D printing.

Simulation of the acoustic field emitted from medical linear transducer in a heterogeneous tissue

A numerical model is developed to simulate the acoustic field in heterogeneous tissue from a medical linear transducer.The coupled full-wave equation for nonlinear ultrasound is solved using a staggered-grid finite difference time domain method.The distribution of acoustic pressure and power in human abdominal wall with heterogeneities in sound speed,density,and nonlinear parameter are obtained.Compared with homogeneous medium,when sound speed in tissue is uniform and density unchanged,the acoustic energy decreases only1.8 dB in the focal region;when density in tissue is uniform and sound speed unchanged,the energy decreases 3.8 dB in the focal region,which is almost the same as heterogeneous tissue.Thus,the primary factor of the aberration of focused beam is the heterogeneous distribution of the tissue sound speed.