Hasil untuk "History of medicine. Medical expeditions"

Nuray Yaşar Soydan, Ahmet Acıduman

Amaç: Bu çalışmada, Homeros’un (MÖ VIII.yüzyıl) Ἰλιάς (Ilias)/İlyada ve Ὀδύσσεια/Odysseia adlı destanlarında geçen Δίκη (Dike)/hak kavramı üzerine odaklanılmıştır. Bu bağlamda, Homeros toplumu içinde yaşanmış hak kavramıyla ilgili söylem ve uygulamalar, yaşanan anlaşmazlıklar, hak arayışları, sözel çözüm yöntemleri ve uygulanan sözel cezalar irdelenerek hangi etik ve adalet kuramların ilk örneklerini oluşturduğu gösterilmeye çalışılmıştır. Hak kavramı, hukuki bir terim olsa da uygulanan kararların doğru-yanlış, iyi-kötü olması, kavramın erdem ve değerler yönüyle de ele alınmasının gerekliliğini ortaya koymaktadır. Destanlarda geçen ὕβρις (hubris)/küstahlık, aιδώs (aidos)/utanç, (τime)/onur, μέτρoν (metron)/ölçülü olma gibi kavramlar da etik açıdan ele alınmıştır.Yöntem: Çalışmada Homeros’un Ἰλιάς destanı için Azra Erhat ve A. Kadir, Ὀδύσσεια destanı için ise Azra Erhat/A. Kadir ve Ahmet Cevat Emre’nin Türkçeye kazandırdığı çalışmalar kaynak olarak kullanılmıştır. Çalışmada metin çözümlemesi yöntemine başvurularak, destanlarda geçen Δίκη kavramının anlamları incelenmiş ve bulgular kısmında sunulmuştur. Bulgular: Bu inceleme sonucunda; Δίκη kavramının hukuki anlamda haksız davranış, hüküm vermek, pay, hukuk, karar, iddia, dava; sıfat anlamı olan doğru-dürüst anlamının da etik anlamda kullanıldığı saptanmıştır. Temel etik ilkelerden olan erdem ve ödev etiği ile adaletin temel ilkelerinden olan dağıtıcı ve düzeltici adalet ilkesi göze çarpan bulgular arasındadır. Dağıtıcı adalet ilkelerinden yararcılık, liyakatçilik ve eşitlik temelli yaklaşımların da ilk örneklerine rastlanılmıştır.Sonuç: Homeros destanlarında Δίκη kavramına farklı anlamların ve değerlerin yüklendiği görülmüştür. Tek bir kavramın etik ve adalet olmak üzere iki farklı kurama kaynaklık ettiği fark edilmiştir. Homeros destanlarının, temel etik ve adalet kuramlarının ilk örneklerini içermesi bakımından özgün eserler olarak önemli bir yere sahip olduğu düşünülmektedir.

Salil Patel

Medical data range from genomic sequences and retinal photographs to structured laboratory results and unstructured clinical narratives. Although these modalities appear disparate, many encode convergent information about a single underlying physiological state. The Latent Space Hypothesis frames each observation as a projection of a unified, hierarchically organized manifold -- much like shadows cast by the same three-dimensional object. Within this learned geometric representation, an individual's health status occupies a point, disease progression traces a trajectory, and therapeutic intervention corresponds to a directed vector. Interpreting heterogeneous evidence in a shared space provides a principled way to re-examine eponymous conditions -- such as Parkinson's or Crohn's -- that often mask multiple pathophysiological entities and involve broader anatomical domains than once believed. By revealing sub-trajectories and patient-specific directions of change, the framework supplies a quantitative rationale for personalised diagnosis, longitudinal monitoring, and tailored treatment, moving clinical practice away from grouping by potentially misleading labels toward navigation of each person's unique trajectory. Challenges remain -- bias amplification, data scarcity for rare disorders, privacy, and the correlation-causation divide -- but scale-aware encoders, continual learning on longitudinal data streams, and perturbation-based validation offer plausible paths forward.

Keshav Jha, Joseph Mayer

Three-dimensional (3D) printed preoperative planning models serve a critical role in the success of many medical procedures. However, many of these models do not portray the patient's complete anatomy due to their monolithic and static nature. The use of dynamic 3D-printed models can better equip physicians by providing a more anatomically accurate model due to its movement capabilities and the ability to remove and replace printed anatomies based on planning stages. A dynamic 3D-printed preoperative planning model has the capability to move in similar ways to the anatomy that is being represented by the model, or reveal additional issues that may arise during the use of a movement mechanism. The 3D-printed models are constructed in a similar manner to their static counterparts; however, in the digital post-processing phase, additional care is needed to ensure the dynamic functionality of the model. Here, we discuss the process of creating a dynamic 3D-printed model and its benefits and uses in modern medicine.

Liu Li, Hanchun Wang, Matthew Baugh et al.

Although existing medical image segmentation methods provide impressive pixel-wise accuracy, they often neglect topological correctness, making their segmentations unusable for many downstream tasks. One option is to retrain such models whilst including a topology-driven loss component. However, this is computationally expensive and often impractical. A better solution would be to have a versatile plug-and-play topology refinement method that is compatible with any domain-specific segmentation pipeline. Directly training a post-processing model to mitigate topological errors often fails as such models tend to be biased towards the topological errors of a target segmentation network. The diversity of these errors is confined to the information provided by a labelled training set, which is especially problematic for small datasets. Our method solves this problem by training a model-agnostic topology refinement network with synthetic segmentations that cover a wide variety of topological errors. Inspired by the Stone-Weierstrass theorem, we synthesize topology-perturbation masks with randomly sampled coefficients of orthogonal polynomial bases, which ensures a complete and unbiased representation. Practically, we verified the efficiency and effectiveness of our methods as being compatible with multiple families of polynomial bases, and show evidence that our universal plug-and-play topology refinement network outperforms both existing topology-driven learning-based and post-processing methods. We also show that combining our method with learning-based models provides an effortless add-on, which can further improve the performance of existing approaches.

Wasif Khan, Seowung Leem, Kyle B. See et al.

Foundation models (FMs) are large-scale deep learning models trained on massive datasets, often using self-supervised learning techniques. These models serve as a versatile base for a wide range of downstream tasks, including those in medicine and healthcare. FMs have demonstrated remarkable success across multiple healthcare domains. However, existing surveys in this field do not comprehensively cover all areas where FMs have made significant strides. In this survey, we present a comprehensive review of FMs in medicine, focusing on their evolution, learning strategies, flagship models, applications, and associated challenges. We examine how prominent FMs, such as the BERT and GPT families, are transforming various aspects of healthcare, including clinical large language models, medical image analysis, and omics research. Additionally, we provide a detailed taxonomy of FM-enabled healthcare applications, spanning clinical natural language processing, medical computer vision, graph learning, and other biology- and omics- related tasks. Despite the transformative potentials of FMs, they also pose unique challenges. This survey delves into these challenges and highlights open research questions and lessons learned to guide researchers and practitioners. Our goal is to provide valuable insights into the capabilities of FMs in health, facilitating responsible deployment and mitigating associated risks.

Lie Cai, Andre Pfob

Background: Using artificial intelligence (AI) techniques for computational medical image analysis has shown promising results. However, colored images are often not readily available for AI analysis because of different coloring thresholds used across centers and physicians as well as the removal of clinical annotations. We aimed to develop an open-source tool that can adapt different color thresholds of HSV-colored medical images and remove annotations with a simple click. Materials and Methods: We built a function using MATLAB and used multi-center international shear wave elastography data (NCT 02638935) to test the function. We provide step-by-step instructions with accompanying code lines. Results: We demonstrate that the newly developed pre-processing function successfully removed letters and adapted different color thresholds of HSV-colored medical images. Conclusion: We developed an open-source tool for removing letters and adapting different color thresholds in HSV-colored medical images. We hope this contributes to advancing medical image processing for developing robust computational imaging algorithms using diverse multi-center big data. The open-source Matlab tool is available at https://github.com/cailiemed/image-threshold-adapting.

Зоя Куделько

Діджиталізація активно впроваджується в усіх сферах суспільної діяльності, трансформуючи традиційні способи створення, розповсюдження та аналізу інформації. Дипломатична діяльність не стала винятком у процесах актуалізації цифрового простору, поступово залучаючи до своєї практики засоби ІКТ. Дослідження зосереджено на висвітленні впровадження цифрових інновацій у дипломатії в умовах постійного впливу соціокультурних факторів. Сучасний світ динамічний і швидко розвивається; тому тенденції сприяння або перешкоджання оцифровці, яка використовується безпосередньо в перекладі дипломатичних текстів, швидко змінюються залежно від багатьох факторів (соціально-політичних, соціально-економічних, культурних тощо). Мета дослідження – зрозуміти ефективність використання інформаційно-цифрових технологій у перекладі дипломатичних текстів. Відзначається потреба в нових медіа, перекладачах і засобах перекладу, які будуть затребувані в умовах тотальної глобалізації та інформатизації сучасного соціокультурного простору. Цифровізація продемонструвала доцільність її використання в дипломатичній діяльності загалом, тож очевидно, що цей процес поширюватиметься на вузькоспеціалізовані кластери, зокрема перекладу дипломатичних текстів. Методологія дослідження ґрунтується на поєднанні використання загальнонаукових і науково-лінгвістичних методів, завдяки чому здійснено комплексне дослідження алгоритмів оцифрування дипломатичного перекладу. Крім того, активно використовується науково-синергетичний підхід, який забезпечує взаємодію цифрового, мовного, дипломатичного та культурно-історичного вимірів, які актуалізуються в науковій розвідці. Елементи новизни дослідження зосереджені на інтерпретації інформаційно-цифрових технологій у контексті їх залучення до процесу дипломатичного перекладу. Висновок. Тому сегмент ІКТ активно завойовує позиції в дипломатичній діяльності, повною мірою відповідаючи тенденціям сучасного соціокультурного розвитку. У практично-орієнтованому середовищі дипломатична перекладацька діяльність успішно використовує онлайн-ресурси для покращення якості та доступності перекладених матеріалів. Однак цифровий сегмент позиціонується як допоміжний елемент або альтернатива традиційним моделям перекладу, використовуючи широкий спектр цифрових інструментів: онлайн-перекладачі, штучний інтелект, інтерактивні словники тощо.

Лариса Шутак, Галина Навчук

The study of medical discourse is one of the key problems of cognitive-communicative grammar, since the sublanguage of medicine - with all its forms and means of expression and general use - is an integral part of any national language. The analysis of professional speech in various communicative situations is of interest to both Ukrainian linguists and researchers of other Slavic languages. The goal of scientific research is to investigate the ways of creating secondary nominative units in medical discourse and to establish their types according to various characteristics. The article summarizes various reasons for the creation of secondary names in modern linguistics, defines the role and significance of the secondary nomination in the process of replenishing the vocabulary of the modern Ukrainian language. The emergence of secondary nominations is caused by both intra-linguistic and extra-linguistic factors. The creation of such names is due mainly to changes in society, which contribute to the deepening of knowledge about objects and phenomena of the real world, the principle of linguistic economy when creating new words, and emotional and expressive factors. The primary nomination, based on object-sensory perception, is a generalization of social experience and the creation of a conceptual level of knowledge, the secondary nomination generalizes linguistic evidence. The main methods of research are: method of component analysis, method of modeling, method of associative experiment and method of cognitive analysis. Conclusions. The role of secondary nomination as a text category is defined, in particular in binary contrasts. It was found that metaphorization is the most productive means of creating secondary names in medical discourse. A typical way of creating secondary names of persons is suffixation as an ancient and traditional way of creating words. The advantage of secondary suffixed names over official foreign terms is that they are more understandable primarily to patients

Dongyeop Jang, Tae-Rim Yun, Choong-Yeol Lee et al.

Traditional Korean medicine (TKM) emphasizes individualized diagnosis and treatment. This uniqueness makes AI modeling difficult due to limited data and implicit processes. Large language models (LLMs) have demonstrated impressive medical inference, even without advanced training in medical texts. This study assessed the capabilities of GPT-4 in TKM, using the Korean National Licensing Examination for Korean Medicine Doctors (K-NLEKMD) as a benchmark. The K-NLEKMD, administered by a national organization, encompasses 12 major subjects in TKM. We optimized prompts with Chinese-term annotation, English translation for questions and instruction, exam-optimized instruction, and self-consistency. GPT-4 with optimized prompts achieved 66.18% accuracy, surpassing both the examination's average pass mark of 60% and the 40% minimum for each subject. The gradual introduction of language-related prompts and prompting techniques enhanced the accuracy from 51.82% to its maximum accuracy. GPT-4 showed low accuracy in subjects including public health & medicine-related law, internal medicine (2) which are localized in Korea and TKM. The model's accuracy was lower for questions requiring TKM-specialized knowledge. It exhibited higher accuracy in diagnosis-based and recall-based questions than in intervention-based questions. A positive correlation was observed between the consistency and accuracy of GPT-4's responses. This study unveils both the potential and challenges of applying LLMs to TKM. These findings underline the potential of LLMs like GPT-4 in culturally adapted medicine, especially TKM, for tasks such as clinical assistance, medical education, and research. But they also point towards the necessity for the development of methods to mitigate cultural bias inherent in large language models and validate their efficacy in real-world clinical settings.

Harry Anthony, Konstantinos Kamnitsas

Implementing neural networks for clinical use in medical applications necessitates the ability for the network to detect when input data differs significantly from the training data, with the aim of preventing unreliable predictions. The community has developed several methods for out-of-distribution (OOD) detection, within which distance-based approaches - such as Mahalanobis distance - have shown potential. This paper challenges the prevailing community understanding that there is an optimal layer, or combination of layers, of a neural network for applying Mahalanobis distance for detection of any OOD pattern. Using synthetic artefacts to emulate OOD patterns, this paper shows the optimum layer to apply Mahalanobis distance changes with the type of OOD pattern, showing there is no one-fits-all solution. This paper also shows that separating this OOD detector into multiple detectors at different depths of the network can enhance the robustness for detecting different OOD patterns. These insights were validated on real-world OOD tasks, training models on CheXpert chest X-rays with no support devices, then using scans with unseen pacemakers (we manually labelled 50% of CheXpert for this research) and unseen sex as OOD cases. The results inform best-practices for the use of Mahalanobis distance for OOD detection. The manually annotated pacemaker labels and the project's code are available at: https://github.com/HarryAnthony/Mahalanobis-OOD-detection.

Yizhe Zhang, Shuo Wang, Yejia Zhang et al.

Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e.g., 99.5\% of the time). CP provides comprehensive predictions on possible labels of a given test sample, and the size of the set indicates how certain the predictions are (e.g., a set larger than one is `uncertain'). Such distinct properties of CP enable effective collaborations between human experts and medical AI models, allowing efficient intervention and quality check in clinical decision-making. In this paper, we propose a new method called Reliable-Region-Based Conformal Prediction (RR-CP), which aims to impose a stronger statistical guarantee so that the user-specified error rate (e.g., 0.5\%) can be achieved in the test time, and under this constraint, the size of the prediction set is optimized (to be small). We consider a small prediction set size an important measure only when the user-specified error rate is achieved. Experiments on five public datasets show that our RR-CP performs well: with a reasonably small-sized prediction set, it achieves the user-specified error rate (e.g., 0.5\%) significantly more frequently than exiting CP methods.

Danilo Avola, Luigi Cinque, Alessio Fagioli et al.

Taking medicines is a fundamental aspect to cure illnesses. However, studies have shown that it can be hard for patients to remember the correct posology. More aggravating, a wrong dosage generally causes the disease to worsen. Although, all relevant instructions for a medicine are summarized in the corresponding patient information leaflet, the latter is generally difficult to navigate and understand. To address this problem and help patients with their medication, in this paper we introduce an augmented reality mobile application that can present to the user important details on the framed medicine. In particular, the app implements an inference engine based on a deep neural network, i.e., a densenet, fine-tuned to recognize a medicinal from its package. Subsequently, relevant information, such as posology or a simplified leaflet, is overlaid on the camera feed to help a patient when taking a medicine. Extensive experiments to select the best hyperparameters were performed on a dataset specifically collected to address this task; ultimately obtaining up to 91.30\% accuracy as well as real-time capabilities.

Isabela de Oliveira Dornelas

Alejandro Gutierrez-Castillo, Javier Gutierrez-Castillo, Francisco Guadarrama-Conzuelo et al.

This study aimed at examining the approval rate of the medical students’ regarding active euthanasia, passive euthanasia, and physician-assisted-suicide over the last ten years. To do so, the arguments and variables affecting students’ choices were examined and a systematic review was conducted, using PubMed and Web of Science databases, including articles from January 2009 to December 2018. From 135 identified articles, 13 met the inclusion criteria. The highest acceptance rates for euthanasia and physician-assisted suicide were from European countries. The most common arguments supporting euthanasia and physician-assisted suicide were the followings: (i) patient’s autonomy (n = 6), (ii) relief of suffering (n = 4), and (ii) the thought that terminally-ill patients are additional burden (n = 2). The most common arguments against euthanasia were as follows: (i) religious and personal beliefs (n = 4), (ii) the “slippery slope” argument and the risk of abuse (n = 4), and (iii) the physician’s role in preserving life (n = 2). Religion (n = 7), religiosity (n = 5), and the attributes of the medical school of origin (n = 3) were the most significant variables to influence the students’ attitude. However, age, previous academic experience, family income, and place of residence had no significant impact. Medical students' opinions on euthanasia and physician-assisted suicide should be appropriately addressed and evaluated because their moral compass, under the influence of such opinions, will guide them in solving future ethical and therapeutic dilemmas in the medical field.

Alireza Monajemi, Hamidreza Namazi

In this paper, we reflect on the COVID-19 pandemic based on medical philosophy. A critical examination of the Corona crisis uncovers that in order to understand and explain the unpreparedness of the health systems, we need a new conceptual framework. This helps us to look at this phenomenon in a new way, address new problems, and come up with creative solutions. Our proposal is that “health lag” is a concept that could help frame and explain this unpreparedness and unreadiness. The term “health lag” refers to the failure of health systems to keep up with clinical medicine. In other words, health issues in most situations fall behind clinical medicine, leading to social, cultural, and economic problems. In the first step to define health lag, we have to explain the distinction between clinical medicine and health and address the role of individual health, public health, and epidemic in this dichotomy. Thereafter, the reasons behind health lag will be analyzed in three levels: theoretical, practical, and institutional. In the third step, we will point out the most important consequences of health lag: the medicalization of health, the inconsistency of biopolitics, inadequate ethical frameworks, and public sphere vulnerabilities. Finally, we try to come up with a set of recommendations based on this philosophical-conceptual analysis.

Bobak Toussi Kiani, Agnes Villanyi, Seth Lloyd

A central task in medical imaging is the reconstruction of an image or function from data collected by medical devices (e.g., CT, MRI, and PET scanners). We provide quantum algorithms for image reconstruction with exponential speedup over classical counterparts when data is input as a quantum state. Since outputs of our algorithms are stored in quantum states, individual pixels of reconstructed images may not be efficiently accessed classically; instead, we discuss various methods to extract information from outputs using a variety of quantum post-processing algorithms.

Xudong Wang, Shizhong Han, Yunqiang Chen et al.

A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D networks to leverage context information along the z direction, and allows the use of pretrained 2D detection models when training data is limited, as is often the case for medical applications. Its integration in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver Tumor Segmentation (LiTS) Challenge, outperforming the previous challenge winner by 3.9 points and achieving top performance on the LiTS leader board at the time of paper submission. Detection experiments on the DeepLesion dataset also show that the addition of VA to existing object detectors enables a 69.1 sensitivity at 0.5 false positive per image, outperforming the best published results by 6.6 points.

Oh Young KWON

Tuberculosis (TB) was called “ruinous disease” in colonial Korea. However, it is no longer a threat to the lives of the Korean people. Public Health Centers (PHC) have played a role in the reduction of TB prevalence by providing free medical treatment and vaccination. PHCs are valued highly for suggesting the possibility of TB suppression. Despite these outcomes, the achievements of PHCs may be slightly overstated from a therapeutic perspective. PHCs could not prevent and treat TB well in their conditions at the time in Korea. The concept of PHC in Korea that emphasizes prevention rather than treatment came from the US. There is a need to reevaluate the achievements of PHCs in TB control. The South Korean government established an anti-TB network system named “Health-Net” in 1962. PHCs were the primary institutions against TB. The “100,000 Tuberculosis Patients Registration Program” was conducted by the government through PHCs, which was an effective anti-TB program. The success of the registration program was a result of the effort by PHCs and anti-TB private organizations. Free medications distributed by PHCs helped to decrease mortality due to TB. The implementation of the “Tuberculosis Prevention Act” in 1968 strengthened the management function of PHCs. A larger anti-TB budget by the law made new prescriptions possible, including second-generation medications. It also enabled the recruitmen of more manpower for TB control, finding TB patients, and BCG vaccination. However, there were some limits of PHCs’ therapeutic role in these achievements. At first, the lower cure rate in patients receiving medical care at PHCs was a main problem. The fact that PHCs accounted for nationwide TB patients was another problem. It is unclear that PHCs had an active role in TB management. There were no specific TB treatment programs except the follow-up treatment dependent on the only one medication. PHCs in the 1960s and 1970s achieved the results of patient registration and free treatment in TB control, but there was a limit to their therapeutic function.

Anzhela BIDUCHAK, Маria MANDRYK-MELNYCHUK

Целью работы являются актуальные вопросы современного значения истории медицины в системе высшего медицинс- кого образования и формировании профессиональных компетенций будущих медицинских работников. Научная новизна. Авторы выделяют современные подходы к преподаванию истории медицины иностранным студентам, которые будут спо- собствовать наиболее полному формированию профессионального мышления и развития их личностных качеств. Методы исследования. Методология исследования. В статье использованы описательный метод и метод сопоставления. Выводы. В научном исследовании рассмотрены проблемные вопросы изучения истории медицины иностранными студентами. В частно- сти, акцентировано внимание на том, что наибольшие трудности студенты-иностранцы испытывают из-за влияния многочис- ленных факторов: географических, экономических, национальных, религиозных и тому подобное.

Dominik Müller, Frank Kramer

The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemented pipelines are commonly standalone software, optimized on a specific public data set. Therefore, this paper introduces the open-source Python library MIScnn. The aim of MIScnn is to provide an intuitive API allowing fast building of medical image segmentation pipelines including data I/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art deep learning models and model utilization like training, prediction, as well as fully automatic evaluation (e.g. cross-validation). Similarly, high configurability and multiple open interfaces allow full pipeline customization. Running a cross-validation with MIScnn on the Kidney Tumor Segmentation Challenge 2019 data set (multi-class semantic segmentation with 300 CT scans) resulted into a powerful predictor based on the standard 3D U-Net model. With this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. The source code for MIScnn is available in the Git repository: https://github.com/frankkramer-lab/MIScnn.