This letter responds to a recent study on violence among schizophrenic inpatients at Al-Rashad Training Hospital. While commending the authors’ important contribution, the letter highlights key areas needing further attention to strengthen the findings. It discusses the exclusion of female patients, the limitations of a cross-sectional design, and the need to analyze systemic factors such as staffing, training, and ward environment. The letter advocates for the use of comprehensive diagnostic and risk assessment tools beyond standard criteria to better understand symptom patterns and predict aggression. Suggestions include using validated scales like PANSS, BPRS, and HCR-20. The author calls for broader, longitudinal research that considers gender differences, symptom severity, and institutional variables to improve patient care and safety. By addressing these gaps, future studies can provide stronger evidence to guide clinical practice and policy in psychiatric inpatient settings.
History of medicine. Medical expeditions, General works
Katharina Eckstein, Constantin Ulrich, Michael Baumgartner
et al.
Large-scale pre-training holds the promise to advance 3D medical object detection, a crucial component of accurate computer-aided diagnosis. Yet, it remains underexplored compared to segmentation, where pre-training has already demonstrated significant benefits. Existing pre-training approaches for 3D object detection rely on 2D medical data or natural image pre-training, failing to fully leverage 3D volumetric information. In this work, we present the first systematic study of how existing pre-training methods can be integrated into state-of-the-art detection architectures, covering both CNNs and Transformers. Our results show that pre-training consistently improves detection performance across various tasks and datasets. Notably, reconstruction-based self-supervised pre-training outperforms supervised pre-training, while contrastive pre-training provides no clear benefit for 3D medical object detection. Our code is publicly available at: https://github.com/MIC-DKFZ/nnDetection-finetuning.
Artificial intelligence (AI) has become increasingly central to precision medicine by enabling the integration and interpretation of multimodal data, yet implementation in clinical settings remains limited. This paper provides a scoping review of literature from 2019-2024 on the implementation of AI in precision medicine, identifying key barriers and enablers across data quality, clinical reliability, workflow integration, and governance. Through an ecosystem-based framework, we highlight the interdependent relationships shaping real-world translation and propose future directions to support trustworthy and sustainable implementation.
Following the Second World War, structural changes shadowed Iran’s higher education system in medicine. Major strategies, programs, teaching methods, etc., underwent significant changes during the second Pahlavi era. This study aimed to examine, academically and historically, the transformations in medical education during the second Pahlavi era. Regardless of its intrinsic value, it elucidates the roots of many contemporary problems and issues in medical education.This study utilizes a descriptive-analytical method within the framework of historical studies, relying on archival documents and library resources from the second Pahlavi era (statistical yearbooks, guidelines, executive programs of organizations and ministries, and historical texts of that period) in an attempt to address research questions. The findings indicate that significant international developments, domestic public demands, fundamental structure weaknesses, and the absence of centralized policies have led to a discourse inclined towards change and improvement in medical education during the second Pahlavi era.As a historical and continuous process, medical education is observable and dynamic. In the second Pahlavi era, it was presented and introduced as a necessary issue requiring change, directly impacting the quality of public health. This approach later led to integrating medical education into the service delivery system within the revolutionary discourse.
Medicine, History of medicine. Medical expeditions
Konstantinos Vilouras, Pedro Sanchez, Alison Q. O'Neil
et al.
Localizing the exact pathological regions in a given medical scan is an important imaging problem that traditionally requires a large amount of bounding box ground truth annotations to be accurately solved. However, there exist alternative, potentially weaker, forms of supervision, such as accompanying free-text reports, which are readily available. The task of performing localization with textual guidance is commonly referred to as phrase grounding. In this work, we use a publicly available Foundation Model, namely the Latent Diffusion Model, to perform this challenging task. This choice is supported by the fact that the Latent Diffusion Model, despite being generative in nature, contains cross-attention mechanisms that implicitly align visual and textual features, thus leading to intermediate representations that are suitable for the task at hand. In addition, we aim to perform this task in a zero-shot manner, i.e., without any training on the target task, meaning that the model's weights remain frozen. To this end, we devise strategies to select features and also refine them via post-processing without extra learnable parameters. We compare our proposed method with state-of-the-art approaches which explicitly enforce image-text alignment in a joint embedding space via contrastive learning. Results on a popular chest X-ray benchmark indicate that our method is competitive with SOTA on different types of pathology, and even outperforms them on average in terms of two metrics (mean IoU and AUC-ROC). Source code will be released upon acceptance at https://github.com/vios-s.
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
History of medicine. Medical expeditions, Social Sciences
Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade. The initial developments, such as regression-based and U-Net-based networks, established the foundation for deep learning in image registration. Subsequent progress has been made in various aspects of deep learning-based registration, including similarity measures, deformation regularizations, network architectures, and uncertainty estimation. These advancements have not only enriched the field of image registration but have also facilitated its application in a wide range of tasks, including atlas construction, multi-atlas segmentation, motion estimation, and 2D-3D registration. In this paper, we present a comprehensive overview of the most recent advancements in deep learning-based image registration. We begin with a concise introduction to the core concepts of deep learning-based image registration. Then, we delve into innovative network architectures, loss functions specific to registration, and methods for estimating registration uncertainty. Additionally, this paper explores appropriate evaluation metrics for assessing the performance of deep learning models in registration tasks. Finally, we highlight the practical applications of these novel techniques in medical imaging and discuss the future prospects of deep learning-based image registration.
Haider Sultan, Hafiza Farwa Mahmood, Noor Fatima
et al.
Like other fields of Traditional Medicines, Unani Medicines have been found as an effective medical practice for ages. It is still widely used in the subcontinent, particularly in Pakistan and India. However, Unani Medicines Practitioners are lacking modern IT applications in their everyday clinical practices. An Online Clinical Decision Support System may address this challenge to assist apprentice Unani Medicines practitioners in their diagnostic processes. The proposed system provides a web-based interface to enter the patient's symptoms, which are then automatically analyzed by our system to generate a list of probable diseases. The system allows practitioners to choose the most likely disease and inform patients about the associated treatment options remotely. The system consists of three modules: an Online Clinical Decision Support System, an Artificial Intelligence Inference Engine, and a comprehensive Unani Medicines Database. The system employs advanced AI techniques such as Decision Trees, Deep Learning, and Natural Language Processing. For system development, the project team used a technology stack that includes React, FastAPI, and MySQL. Data and functionality of the application is exposed using APIs for integration and extension with similar domain applications. The novelty of the project is that it addresses the challenge of diagnosing diseases accurately and efficiently in the context of Unani Medicines principles. By leveraging the power of technology, the proposed Clinical Decision Support System has the potential to ease access to healthcare services and information, reduce cost, boost practitioner and patient satisfaction, improve speed and accuracy of the diagnostic process, and provide effective treatments remotely. The application will be useful for Unani Medicines Practitioners, Patients, Government Drug Regulators, Software Developers, and Medical Researchers.
Augmented reality becomes popular in education gradually, which provides a contextual and adaptive learning experience. Here, we develop a Chinese herb medicine AR platform based the 3dsMax and the Unity that allows users to visualize and interact with the herb model and learn the related information. The users use their mobile camera to scan the 2D herb picture to trigger the presentation of 3D AR model and corresponding text information on the screen in real-time. The system shows good performance and has high accuracy for the identification of herbal medicine after interference test and occlusion test. Users can interact with the herb AR model by rotating, scaling, and viewing transformation, which effectively enhances learners' interest in Chinese herb medicine.
AI-driven medical history-taking is an important component in symptom checking, automated patient intake, triage, and other AI virtual care applications. As history-taking is extremely varied, machine learning models require a significant amount of data to train. To overcome this challenge, existing systems are developed using indirect data or expert knowledge. This leads to a training-inference gap as models are trained on different kinds of data than what they observe at inference time. In this work, we present a two-stage re-ranking approach that helps close the training-inference gap by re-ranking the first-stage question candidates using a dialogue-contextualized model. For this, we propose a new model, global re-ranker, which cross-encodes the dialogue with all questions simultaneously, and compare it with several existing neural baselines. We test both transformer and S4-based language model backbones. We find that relative to the expert system, the best performance is achieved by our proposed global re-ranker with a transformer backbone, resulting in a 30% higher normalized discount cumulative gain (nDCG) and a 77% higher mean average precision (mAP).
Yohann Le Bourlout, Gösta Ehnholm, Heikki J. Nieminen
Annually, more than 16 billion medical needles are consumed worldwide. However, the functions of the medical needle are still limited to cutting and delivering or drawing material through the needle to a target site. Ultrasound combined with hypodermic needle could potentially add value to many medical applications such as pain reduction, adding precision, deflection reduction in tissues and even improve tissue collection. In this study we introduce a waveguide construct enabling an efficient way to convert a longitudinal wave mode to flexural mode and to couple the converted wave mode to a conventional medial needle, while maintaining high electric-to-acoustic power efficiency. The structural optimization of the waveguide was realized in silico using the finite element method followed by prototyping the construct and experimental characterization of the prototypes. The experiments at 30 kHz demonstrate flexural tip displacement up to 200 μm, at low electrical power consumption (under 5 W), with up to 69% of efficiency. The high electric-to-acoustic efficiency and small size of the transducer would facilitate design of medical needle and biopsy devices, potentially enabling portability, batterization and high patient safety with low electric powers.
The practice of bloodletting gradually fell into disfavor as a growing body of scientific evidence showed its ineffectiveness and demonstrated the effectiveness of various pharmaceuticals for the prevention and treatment of certain diseases. At the same time, the patent medicine industry promoted ineffective remedies at medicine shows featuring entertainment, testimonials, and pseudo-scientific claims with all the trappings--but none of the methodology--of science. Today, many producing parties and eDiscovery vendors similarly promote obsolete technology as well as unvetted tools labeled "artificial intelligence" or "technology-assisted review," along with unsound validation protocols. This situation will end only when eDiscovery technologies and tools are subject to testing using the methods of information retrieval.
In recent years, deep-learning-based image processing has emerged as a valuable tool for medical imaging owing to its high performance. However, the quality of deep-learning-based methods heavily relies on the amount of training data; the high cost of acquiring a large dataset is a limitation to their utilization in medical fields. Herein, based on deep learning, we developed a computed tomography (CT) modality conversion method requiring only a few unsupervised images. The proposed method is based on CycleGAN with several extensions tailored for CT images, which aims at preserving the structure in the processed images and reducing the amount of training data. This method was applied to realize the conversion of megavoltage computed tomography (MVCT) to kilovoltage computed tomography (kVCT) images. Training was conducted using several datasets acquired from patients with head and neck cancer. The size of the datasets ranged from 16 slices (two patients) to 2745 slices (137 patients) for MVCT and 2824 slices (98 patients) for kVCT. The required size of the training data was found to be as small as a few hundred slices. By statistical and visual evaluations, the quality improvement and structure preservation of the MVCT images converted by the proposed model were investigated. As a clinical benefit, it was observed by medical doctors that the converted images enhanced the precision of contouring. We developed an MVCT to kVCT conversion model based on deep learning, which can be trained using only a few hundred unpaired images. The stability of the model against changes in data size was demonstrated. This study promotes the reliable use of deep learning in clinical medicine by partially answering commonly asked questions, such as "Is our data sufficient?" and "How much data should we acquire?"
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.
History of medicine. Medical expeditions, Medical philosophy. Medical ethics
How did the Japanese establish a medical welfare system? In answering this question, historians of modern Japan have accentuated the assertive role of state bureaucrats, especially from those of the Home Ministry (naimushō). Historians of Japanese medicine also emphasized the role of the state. William Johnston, in his pioneering work on tuberculosis in Japan, explored the rise of a hygiene administration on this disease as a state enterprise. In the medical history of Japan, scholars highlighted the significance of the wartime period in the birth of this system. The emphasis on the Japanese wartime state is justified. The Japanese government managed to establish a national health insurance in 1935, while the United States government has not been able to establish a medical insurance for every citizen to this day. However, these scholars have not explored how welfare benefits were distributed to members of Japanese society. This article seeks to fill this historiographical gap by looking at the Student Health Center of Tokyo Imperial University (Tōdai), Japan’s first state-established university founded in 1886. This university, I contend, was a critical locus in the birth of medical welfare in Japan. At this university were the most privileged medical facilities and practitioners who could provide medical services, as well as students without stable incomes of their own, thus in need of welfare support. The demand of staff of Tōdai’s Student Association to establish a Student Health Center was accepted and realized in 1926, and Tōdai students became the beneficiaries of state-managed medical support. The Tōdai Student Health Center was different from other medicare facilities in that its role was not limited to save students from poverty. Student Health Center practitioners helped students check health for university admission, campus life, and job placement to be white-collar elites. Student Health Center practitioners evaluated students’ health when they tried to enter Tōdai and get jobs and inculcated students in how to manage living as mental-worker “gentlemen,” in coping with tuberculosis, venereal diseases, and neurotic breakdown. Also, they produced statistics about the health condition of Tōdai students, which immediately stimulated further investment in the facilities of Tōdai authorities for the center. Based on statistical data, Tōdai authorities developed a hygiene campaign against tuberculosis so that students could take advantage the of state-of-the-art treatments inexpensively. As such, Tōdai students became among the biggest beneficiaries of this process. In other words, the Student Health Center had a dual significance at Tōdai: a medicare institution as well as part of privileged campus culture. Tōdai was a symbolic locus that reveals the uneven diffusion of medical welfare benefits in Japanese society. Through the lens of this facility, this article seeks to explore the paradox of welfare in meritocracy that contributed to the formation of the elite class in modern Japan.
Hongxu Yang, Caifeng Shan, Alexander F. Kolen
et al.
Medical instrument detection is essential for computer-assisted interventions since it would facilitate the surgeons to find the instrument efficiently with a better interpretation, which leads to a better outcome. This article reviews medical instrument detection methods in the ultrasound-guided intervention. First, we present a comprehensive review of instrument detection methodologies, which include traditional non-data-driven methods and data-driven methods. The non-data-driven methods were extensively studied prior to the era of machine learning, i.e. data-driven approaches. We discuss the main clinical applications of medical instrument detection in ultrasound, including anesthesia, biopsy, prostate brachytherapy, and cardiac catheterization, which were validated on clinical datasets. Finally, we selected several principal publications to summarize the key issues and potential research directions for the computer-assisted intervention community.
Resumo Um elevado número de escolas secundárias portuguesas possui coleções científicas, elementos centrais para a memória e identidade das instituições que as preservam, concorrendo para a compreensão das características que foram definindo a instrução pública de nível secundário em Portugal ao longo do tempo. Porém, esse património é ainda largamente desconhecido. O artigo chama a atenção para o debate historiográfico que envolve o uso de fontes materiais na história do ensino das ciências, dando a conhecer a relevância das coleções de história natural dos liceus de Portugal, bem como sua vulnerabilidade, dada a ausência de uma política geral ou diretrizes orientando sua conservação.