Preemptive medicine represents a paradigm shift from reactive treatment to proactive disease prevention. The integration of omics technologies, the Internet of Things (IoT), and artificial intelligence (AI) has facilitated the development of personalized, predictive, and preemptive healthcare strategies. Omic technologies, such as genomics, proteomics, and metabolomics, provide comprehensive insights into molecular profile of an individual, revealing potential disease predispositions and health trajectories. IoT devices, such as wearables and smartphones, enable continuous and periodic monitoring of physiological parameters, thus providing a dynamic view of an individual's health status. AI algorithms analyze comprehensive and complex data from omics and IoT technologies to identify patterns and correlations that inform predictive models of disease risk, progression, and response to interventions. Medical digital twins, or virtual replicas of an individual's biological processes, have emerged as the cornerstone of preemptive medicine. The integration of omics, IoT, and AI enables the development of medical digital twins, which in turn allows for precise simulation of human physiological profiles, prediction of future health outcomes, and virtual individual clinical trials, facilitating personalized proactive interventions and preemptive disease control. This review demonstrates the convergence of omics, IoT, and AI in preemptive medicine, highlighting their potential to revolutionize healthcare by enabling early disease detection, personalized treatment strategies, and chronic disease prevention. We show how AI leverages omics and IoT in preemptive medicine through several case studies while also discussing the necessary data for developing medical digital twins and addressing ethical and social aspects that warrant consideration. Medical digital twins signify a fundamental transformation in health management, shifting from treating diseases after their occurrence to controlling them before their occurrence. This approach enhances the effectiveness of medical interventions and improves overall health outcomes, preparing for a healthier future.

Background: Aspiration pneumonia is a prevalent condition, and understanding the risk factors associated with discontinuation of oral intake upon discharge is crucial. This study aimed to identify such factors, thereby providing valuable insights for optimizing the use of limited healthcare resources and enhancing patient and family care.

Methods: In this scoping review, data were collected through ICHUSHI using the search formula "Pneumonia-Aspiration/Thesaurus or Aspiration Pneumonia/All) and (Prognosis/Thesaurus or Prognosis/All)." The inclusion criteria encompassed Japanese patients hospitalized for aspiration pneumonia, with a clear outcome focused on the availability of oral intake. The exclusion criteria included text unavailability, studies from foreign countries, and cases involving not hospitalized patients. The risk of bias for each study was assessed using the Newcastle-Ottawa scale.

Results: Using this search formula, 1,646 articles were initially identified, culminating in the inclusion of six articles for analysis. The investigation revealed five significant risk factors: social status (age and gender), nutritional status (body mass index, Controlling Nutritional Status score, serum albumin, Basal Energy Expenditure, and low body weight), physical swallowing function (ambulatory ability before admission, Food Intake LEVEL scale (FILS), admission origin, bedridden status, Penetration-Aspiration scale, presence of residual pharyngeal material, and Basal Index), pneumonia severity (A-DROP score, a classification tool incorporating age, dehydration, oxygen demand, impaired consciousness, and hypotension), and comorbidities (pneumonia, dementia, mental illness, malignancy, chronic lower respiratory tract involvement, and renal failure).

Conclusions: This scoping review identified five key risk factors associated with oral intake discontinuation upon discharge in patients hospitalized for aspiration pneumonia, providing valuable evidence for future clinical practice.

Ultrasound (US) imaging is a widely used tool in oncology because of its noninvasiveness and real-time performance. However, its diagnostic accuracy can be limited by the skills of the examiner when performing manual scanning and by the presence of acoustic shadows that degrade image quality. Artificial intelligence (AI) technologies can support examiners in cancer screening and diagnosis by addressing these limitations. Here, we examine recent advances in AI research and development for US imaging in oncology. Breast cancer has been the most extensively studied cancer, with research predominantly focusing on tumor detection, differentiation between benign and malignant lesions, and prediction of lymph node metastasis. The American College of Radiology developed a medical imaging reporting and data system for various cancers that is often used to evaluate the accuracy of AI models. We will also explore the application of AI in clinical settings for US imaging in oncology. Despite progress, the number of approved AI-equipped software as medical devices for US imaging remains limited in Japan, the United States, and Europe. Practical issues that need to be addressed for clinical application include domain shifts, black boxes, and acoustic shadows. To address these issues, advances in image quality control, AI explainability, and preprocessing of acoustic shadows are essential.

Digital transformation of healthcare is rapidly progressing. Digital transformation improves the quality of services and access to health information for users, reduces the workload and associated costs for healthcare providers, and supports clinical decision-making. Data and artificial intelligence (AI) play a key role in this process. The AI used for this purpose is called medical AI. Medical AI is currently undergoing a shift from task-specific to general-purpose models. Large language models have the potential to systematize existing medical knowledge in a standardized way.

The usage of AI in medicine is not limited to digital transformation; it plays a pivotal role in fundamentally changing the state of medical science. This approach, known as "AI for Medical Science," focuses on pioneering a form of medical science that predicts the onset and progression of disease based on the underlying causes of disease. The key to such predictive medicine is the concept of "states," which can be sought through machine learning. Using states instead of symptoms not only dramatically improves the accuracy of identification (diagnosis) and prediction (prognosis) but also potentially pioneers P4 medicine by integrating it with empirical knowledge and theories based on natural principles.

CD4⁺ T cells, the so-called T helper cells, are one of the main players in the human immune system, which can regulate acquired immunity. Dysfunction of the acquired immune system induces various chronic inflammatory diseases such as malignancies and autoimmune diseases. IgG4-related disease (IgG4-RD) is also a chronic inflammatory disease that is characterized by elevated serum IgG4 concentration and infiltration of IgG4-positive plasma cells in affected tissues. Despite that remarkable advances in understanding the pathogenesis of IgG4-RD have been on the rise, the detailed mechanisms by which IgG4-RD develops are still unknown. In fact, CD4⁺ T cells abundantly infiltrate at lesions of IgG4-RD, and they are also associated with the pathogenesis of other refractory chronic inflammatory diseases. Therefore, our focus was on CD4⁺ T cells, and we previously reported the roles of their subsets including regulatory T cells, CD4 cytotoxic T lymphocytes, T follicular helper (Tfh) cells, T follicular regulatory cells, and T peripheral helper (Tph) cells in IgG4-RD. Among the subsets, Tph cells play an important role in generating ectopic lymphoid structures at inflammatory sites. Moreover, we found that circulating Tph cells are increased in IgG4-RD patients. Unlike Tfh cells, Tph cells express high levels of chemokine receptors and cytotoxic molecules. Thus, they can infiltrate affected tissues and exert a cytotoxic function. Additionally, our latest observations demonstrated that Tph cells interact with extrafollicular B cells in affected tissues. Hence, Tph cells may collaborate with a specific B-cell subset, and they play a role in the maintenance of persistent fibroinflammation in lesions of IgG4-RD. Tph cells may have an important role to play in the pathogenesis of not only IgG4-RD but also other chronic inflammatory diseases. This review summarizes and discusses the possible pathologic roles of CD4⁺ T cell subsets including Tph cells in IgG4-RD.

Chronic kidney disease (CKD) is a complex disease that is related not only to dialysis but also to the onset of cardiovascular disease and life prognosis. As renal function declines with age and depending on lifestyle, the number of patients with CKD is rapidly increasing in Japan. Accurate prognosis prediction for patients with CKD in clinical settings is important for selecting treatment methods and screening patients with high-risk. In recent years, big databases on CKD and dialysis have been constructed through the use of data science technology, and the pathology of CKD is being elucidated. Therefore, we developed an artificial intelligence (AI) system that can accurately predict the prognosis of CKD such as its progression, the timing of dialysis introduction, and death. Aiming for its social implementation, the prognosis prediction system developed for patients with CKD was released on the website. We then developed a clinical practice guideline creation support system called Doctor K as an AI system. When creating clinical practice guidelines, huge amounts of manpower and time are required to conduct a systematic review of thousands of papers. Therefore, we developed a natural language processing (NLP) AI system to significantly improve work efficiency. Doctor K was used in the preparation of the guidelines of the Japanese Society of Nephrology. Furthermore, by comparing and analyzing the medical word virtual space constructed by the NLP AI system based on patient big data, we proved using the latest mathematical theory (category theory) that this system reflects the pathology of CKD. This suggests the possibility that the NLP AI system can predict patient prognosis. We hope that, through these studies, the use of AI based on big data will lead to the development of new treatments and improvement in patient prognosis.

This review outlines the implementation of artificial intelligence (AI) into colonoscopy procedures which includes its history, processes, and challenges. We highlight the importance of the collaborative effort between medical and computer science researchers in the development of AI tools in colonoscopy, particularly focusing on the roles of computer-aided detection (CADe) and computer-aided characterization (CADx) in a real time analysis of colonoscopy videos. Some of the proposed technologies are considered to improve the important clinical outcomes of patients such as adenoma detection rate in colonoscopy. Regulatory approval is considered mandatory before introducing AI tools into the market owing to the potential risks associated with the introduction of AI tools in healthcare. We share the experience of obtaining regulatory approval for EndoBRAIN in Japan, emphasizing the challenges in establishing examination criteria and performance levels at the period. Reimbursement is also identified as necessary for the widespread adoption of medical innovation. With the introduction of reimbursement for a CADe tool in Japan in 2024, we expect to accelerate implementation of AI in colonoscopy in general. Despite regulatory approval and reimbursement, concerns are raised with regard to the assessment of the balance between benefits and harms of AI in colonoscopy. Questions about its impact on cancer prevention, healthcare burden, patient acceptance, and effectiveness across different populations remain unsolved. The lack of clinical guidelines for AI in colonoscopy emphasizes the need for a rigorous assessment of available evidence in optimizing the adoption of AI in colonoscopy practice. While it is always exciting to strive for medical innovation, ensuring rigorous evaluation to optimize patient care is mandatory to improve the quality of health and society.

Ophthalmology is well suited for the integration of artificial intelligence (AI) owing to its reliance on various imaging modalities, such as anterior segment photography, fundus photography, and optical coherence tomography (OCT), which generate large volumes of high-resolution digital images. These images provide rich datasets for training AI algorithms, which enables precise diagnosis and monitoring of various ocular conditions. Retinal disease management heavily relies on image recognition. Limited access to ophthalmologists in underdeveloped areas and high image volumes in developed countries make AI a promising, cost-effective solution for screening and diagnosis. In corneal diseases, differential diagnosis is critical yet challenging because of the wide range of potential etiologies. AI and diagnostic technologies offer promise for improving the accuracy and speed of these diagnoses, including the differentiation between infectious and noninfectious conditions. Smartphone imaging coupled with AI technology can advance the diagnosis of anterior segment diseases, democratizing access to eye care and providing rapid and reliable diagnostic results. Other potential areas for AI applications include cataract and vitreous surgeries as well as the use of generative AI in training ophthalmologists. While AI offers substantial benefits, challenges remain, including the need for high-quality images, accurate manual annotations, patient heterogeneity considerations, and the "black-box phenomenon". Addressing these issues is crucial for enhancing the effectiveness of AI and ensuring its successful integration into clinical practice. AI is poised to transform ophthalmology by increasing diagnostic accuracy, optimizing treatment strategies, and improving patient care, particularly in high-risk or underserved populations.

Neurosurgery has evolved alongside technological innovations; however, these advances have also introduced greater complexity into clinical practice. Neurosurgery remains a demanding and high-risk field that requires a broad range of skills. Artificial intelligence (AI) has immense potential in neurosurgery given its ability to rapidly analyze large volumes of clinical data generated in modern clinical environments. An expanding body of literature has demonstrated that AI enhances various aspects of neurosurgery, including diagnostics, prognostication, decision-making, data management, education, and clinical studies. AI applications are expected to reduce medical errors and costs, broaden healthcare accessibility, and ultimately boost patient safety and surgical education. Nevertheless, AI application in neurosurgery remains practically limited because of several challenges, such as the diversity and volume of clinical training data collection, concerns regarding data quality, algorithmic bias, transparency (explainability and interpretability), ethical issues, and regulatory implications. To comprehensively discuss the potential benefits, future directions, and limitations of AI in neurosurgery, this review examined recent studies on AI technology and its applications in this field, focusing on intraoperative decision support and surgical education.

Recent advancements in artificial intelligence (AI) have markedly affected various fields, with notable progress in surgery. This study explores the integration of AI in surgery, particularly focusing on minimally invasive surgery (MIS), where high-quality surgical videos provide fertile ground for computer vision (CV) technology applications. CV plays an important role in enhancing intraoperative decision-making through real-time image recognition. This study considers the challenges in clinical applications and future perspectives by reviewing the current state of AI in navigation during surgery, postoperative analysis, and automated surgical skill assessment.

In Japan, mammography is commonly used for breast cancer screening. However, the mortality rate has not decreased, possibly due to the low screening uptake and the high prevalence of dense breast tissue among Japanese women, which reduces mammography's effectiveness. A recent prospective study in Japan, J-START, demonstrated that combining mammography with ultrasonography increases detection rates and reduces the incidence of interval cancers, highlighting the significance of ultrasound examinations.

Artificial Intelligence (AI) technologies, particularly in machine learning and deep learning, offer promising solutions to enhance the accuracy and efficiency of breast ultrasound diagnostics. This review explores AI's current capabilities in breast ultrasound imaging, emphasizing key advancements in breast lesion detection and diagnosis. Additionally, we introduce AI-based breast ultrasound diagnostic support programs approved by the Pharmaceuticals and Medical Devices Agency, which include programs for detecting lesion candidate regions and diagnosing the necessity of further examination based on detected lesion candidates. These AI tools are expected to improve diagnostic accuracy and efficiency.

While AI holds significant promise, several challenges remain. It is essential for physicians to oversee its use responsibly, as there are concerns regarding patient acceptance and environmental impact. This review underscores the revolutionary potential of AI in breast cancer diagnostics and emphasizes the importance of ongoing research and development to overcome existing limitations.

Artificial intelligence (AI), empowered by advances in deep learning technology, has demonstrated its capabilities in the medical field to automate tedious tasks that are otherwise performed by humans or to detect or predict diseases with higher accuracy compared with experts. Given the ability to take complex multidimensional data as input, AI models have primarily been applied to complex medical imaging and time-series data. Another prominent strength of AI applications is its large scalability. The field of cardiovascular medicine uses various noninvasive and accessible metrics that produce a large amount of complex multidimensional data, such as electrocardiograms (ECGs) and echocardiograms. AI models can increase the utility of such modalities. Simple automation of conventional tasks using AI models provides significant opportunities for cost reduction and capacity expansion. The ability to improve disease detection or prediction at scale may provide novel opportunities for disease screening, enabling early intervention in asymptomatic patients. For example, AI-enabled pipelines can accurately identify cardiomyopathies and congenital heart diseases from a single ECG or echocardiogram recording. The detection of these diseases using the conventional approach usually requires complicated diagnostic strategies or expensive tests. Therefore, underdiagnosis is a huge problem. Using AI models to screen these diseases will provide opportunities for reducing missed cases. The utility of AI models in the medical field is not limited to the development of clinically useful models. Recent research has shown the promise of AI models in mechanism research by combining them with genetic and structural analyses. In this review, we provide an update on the current achievements of the innovative AI application for ECG and echocardiogram and provide insights into the future direction of AI in cardiovascular care and research settings.

In recent years, artificial intelligence (AI) research in the medical field has been actively conducted owing to the evolution of algorithms, such as deep learning, and advances in hardware, such as graphics processing units, and some such medical devices have been used in clinics. AI research in obstetrics and gynecology has also increased. This review discusses the latest studies in each field. In the perinatal field, there are reports on cardiotocography, studies on the diagnosis of fetal abnormalities using ultrasound scans, and studies on placenta previa using magnetic resonance imaging (MRI). In the reproduction field, numerous studies have been conducted on the efficiency of assisted reproductive technology as well as selection of suitable oocyte and good embryos. As regards gynecologic cancers, there are many reports on diagnosis using MRI and prognosis prediction using histopathology in cervical cancer, diagnosis using hysteroscopy and prediction of molecular subtypes based on histopathology in endometrial cancer, and diagnosis using MRI and ultrasound as well as prediction of anticancer drug efficacy in ovarian cancer. However, concerns related to AI research include handling of personal information, lack of governing laws, and transparency. These must be addressed to facilitate advanced AI research.

Pathology plays a crucial role in diagnosing and evaluating patient tissue samples obtained via surgeries and biopsies. The advent of whole slide scanners and the development of deep learning technologies have considerably advanced this field, promoting extensive research and development in pathology artificial intelligence (AI). These advancements have contributed to reduced workload of pathologists and supported decision-making in treatment plans. Large-scale AI models, known as foundation models (FMs), are more accurate and applicable to various tasks than traditional AI. Such models have recently emerged and expanded their application scope in healthcare. Numerous FMs have been developed in pathology, with reported applications in various tasks, such as disease and rare cancer diagnoses, patient survival prognosis prediction, biomarker expression prediction, and scoring of the immunohistochemical expression intensity. However, several challenges persist in the clinical application of FMs, which healthcare professionals, as users, must be aware of. Research to address these challenges is ongoing. In the future, the development of generalist medical AI, which integrates pathology FMs with FMs from other medical domains, is expected to progress, effectively utilizing AI in real clinical settings to promote precision and personalized medicine.

In recent years, every aspect of the society has rapidly transformed because of the emergence of artificial intelligence (AI) technologies. AI excels not only in image and voice recognition and analysis but also in achieving near-natural conversations through the development of large language models. These technological innovations are steadily being integrated into healthcare settings and can significantly change the way physicians work in clinics in the near future. Patient interviews will predominantly be performed by AI. Physicians will discuss the findings of traditional tests like electrocardiograms and chest X-rays with AI, providing beyond-human interpretation. Additionally, AI is changing areas that have seen little development for a long time, such as auscultation and phonocardiography, and the recognition and quantification of previously challenging observations like the gait analysis. Although barriers to real-world implementation exist, in the near future, a majority of physicians will collaborate with AIs supporting various aspects of clinical practice, consequently enabling more accurate and appropriate diagnosis and treatment of cardiovascular diseases, including ischemic and valvular heart diseases, arrhythmias, and heart failure. This review focuses on AI application in the field of cardiology, specifically on how it can improve the workflow in clinical settings. We examine various examples of AI integration in cardiology to demonstrate how these technologies can lead to more accurate and efficient patient care. Understanding the advancements in AI can lead to more appropriate and streamlined medical practices, which will ultimately benefit both healthcare providers and patients.

The artificial intelligence (AI) technology in automated measurements has seen remarkable advancements across various vendors, thereby offering new opportunities in echocardiography. Fully automated software particularly has the potential to elevate the analysis and the interpretation of medical images to a new level compared to previous algorithms. Tasks that traditionally required significant time, such as ventricular and atrial volume measurements and Doppler tracing, can now be performed swiftly through AI's automated phase setting and waveform tracing capabilities.

The benefits of AI-driven systems include high-precision and reliable measurements, significant time savings, and enhanced workflow efficiency. By automating routine tasks, AI can reduce the burden on clinicians, allowing them to gather additional information, perform additional tests, and improve patient care. While many studies confirm the accuracy and the reproducibility of AI-driven techniques, it is crucial for clinicians to verify AI-generated measurements and ensure high-quality imaging and Doppler waveforms to fully take advantage of the benefits from these technologies. This review discusses the current state of AI-driven automated measurements in echocardiography, their impact on clinical practice, and the strategies required for the effective integration of AI into clinical workflows.

Introduction: Patients with Behçet's disease (BD) have a variety of symptoms, and the exacerbation of these symptoms affects their daily life and social participation and reduces their quality of life (QOL). This study aimed to clarify the relationship between social participation and QOL in BD patients.

Methods: The BD-checklist 92 and 36-item Short Form Survey (SF-36) questionnaires were mailed to 10 affiliates. A total of 174 patients with BD completed the questionnaire. The patients were divided into two groups according to the presence or absence of problems in each "participation" category of the BD-checklist 92, and the SF-36 scores were compared. Subsequently, a correlational analysis was used to examine the relationship between the number of problem categories extracted from "participation" and scores on the eight subscales of the SF-36. Multiple regression analyses were performed to identify factors associated with SF-36 scores.

Results: The SF-36 subscale scores were significantly lower in patients with problems in the participation category, particularly in those with difficulties in shopping, housework, relationships with friends and family, and community activities. A multiple regression analysis revealed that "basic interpersonal relationships" and "community life" were associated with the SF-36 subscales "role physical", "social functioning", "role emotional", and "mental health".

Conclusions: This study showed that despite excluding the effects of BD-specific primary and secondary symptoms, problems with basic interpersonal relationships, such as those with friends and family, and restricted community activities were associated with reduced QOL.

Introduction: This study aimed to evaluate the characteristics of private long-term care (LTC) service users provided by a company independent from public LTC insurance (LTCI) and to analyze the usage patterns across different types of services.

Methods: We utilized data from 8,046 consultations from the administration data of a private LTC service in Suginami Ward, Tokyo, Japan. We focused on older adults enrolled from February 2016 to October 2019 with follow-up until June 2020. The descriptions included users' demographics, LTCI-certified care levels, living situations, and reasons for choosing private LTC services. Furthermore, we examined the frequencies and minutes of each type of service used, such as shopping, meal, cleaning, outing, and social participation assistance, stratified by solitary living and LTCI certification.

Results: The study included 51 older adults, including 35 (69%) women, 28 (55%) solitary living individuals, 23 (45%) public LTCI-certified individuals, and 45 (88%) participants residing in detached houses. The primary motive for private service use was the absence of informal caregiving in 55% of the participants. Cleaning assistance was the most frequently used. Solitary living residents used various types of assistance, not only cleaning, and LTCI-certified individuals more frequently used meal and outing assistance than those without LTCI certification.

Conclusions: These findings indicate that older adults using private LTC services predominantly lived alone, lived in detached houses, or had no informal care support. Our findings provide an opportunity to examine the appropriateness of the complementary relationship between public and private LTC services.

Introduction: Despite a dramatic increase in the incidence of mild-cognitive impairment (MCI) and early dementia, accessible and engaging screening methods for older adults are lacking. Gamification has gained attention in the self-management of various health conditions, making it a promising avenue for dementia screening. This study aimed to evaluate a gamified mobile application for the early detection of cognitive impairment associated with dementia.

Methods: The gamified app and the Mini-Mental State Examination (MMSE) were administered to 138 participants. The game, based on the N-back working memory task, simulates a restaurant scenario where players cook curries with hidden ingredients to fulfill customer orders, with the difficulty increasing in each round. The correlations between MMSE scores and game metrics were analyzed, and the game metrics were compared between the normal and impaired groups.

Results: Among the 138 older adult participants, the game metrics such as level reached, accuracy, response times, tap times, and swipe times exhibited significant correlations with scores on the MMSE, a standard cognitive screening tool (r = 0.42, 0.419, −0.575, −0.484, and −0.667, respectively; P < 0.05 for all). The participants were divided into the normal (≥28) and impaired (<28) groups based on the MMSE cutoff values. The impaired group had significantly worse performance on all game metrics. After multivariate adjustment, average swipe time emerged as the strongest predictor, achieving 70.8% sensitivity and 80.6% specificity in detecting impairment using a 3.31-s cutoff (area under the curve = 0.820).

Conclusions: This classification accuracy was comparable to standard dementia screening tests. These results indicate the potential use of gamification with joyous experience for older adults to enable scalable cognitive screening beyond conventional testing paradigms.

Introduction: The breast cancer screening rate declined worldwide during the COVID-19 pandemic. This cross-sectional study examined the changes in breast cancer screening participation rates in Japan before and during the pandemic and identified subgroups with a larger decline.

Methods: We used data from a 2019 survey evaluating 2017-2018 (pre-pandemic) and a 2022 survey evaluating 2020-2021 (during the pandemic) in the Comprehensive Survey of Living Conditions to describe the breast cancer screening rates by screening settings among women aged 40-74 years. We calculated the changes in the overall participation rate and by subgroup with and without adjustment for other variables (i.e., age, living area, educational level, and health insurance).

Results: The participation rates in breast cancer screening in 2017-2018 and 2020-2021 were 48.3% (51,428/106,446, municipality-based 18.7%, worksite-based 17.0%, and others 12.6%) and 47.1% (45,006/95,610, municipality-based 17.2%, worksite-based 17.5%, and others 12.4%), respectively. The crude difference from 2017-2018 to 2020-2021 was −1.2% (95% confidence interval [CI], −1.7 to −0.8), and the adjusted difference was −1.7% (−2.2 to −1.4). By subgroup, the adjusted difference was the largest in the 45-49 age subgroup (−2.2% [−3.3 to −1.1]) among the age subgroups, in the town/village subgroup (−2.4% [−3.6 to −1.2]) among the living area subgroups, in the high school subgroup (−1.8% [−2.4 to −1.2]) and vocational school/junior or technical college subgroup (−1.8% [−2.6 to −1.0]) among the educational level subgroups, and in the employee insurance (dependent person) subgroup (−2.5% [−3.3 to −1.7]) among the health insurance subgroups.

Conclusions: The breast cancer screening participation rates decreased during the pandemic in Japan, with some variations by subgroup. For the screening setting, the participation rate of the municipality-based screening decreased, while that of the worksite-based screening increased.

Introduction: Organoids are miniature organs developed through technology. Kidney organoids that originate from human inducible pluripotent stem cells (iPSCs) were developed to recreate renal diseases. However, it is impossible to simultaneously produce kidney organoids from iPSCs of multiple individuals and in a single medium. We herein report the development of adult renal tubular organoids, namely, "tubuloids," from primary renal epithelial cells from multiple human individuals in a single medium.

Methods: Kidneys from eight patients who underwent nephrectomy due to malignancy were sectioned, and primary renal epithelial tubule cells were cultured; four had normal kidney function, and four had mild chronic kidney disease (CKD). Growth factors and Matrigel were added to the primary culture.

Results: Primary cultured renal epithelial cells from normal kidneys exhibited a fine and swollen epithelial appearance, whereas those from kidneys with mild CKD were smaller and slightly elongated. Growth was faster in normal kidney cells than in mild CKD cells. At the beginning of the three-dimensionalization (day 0), normal renal tubuloids grew faster than mild CKD tubuloids. The difference in size between normal and mild CKD tubuloids was not obvious by day 5. Both tubuloid types had comparable sizes by day 21.

Conclusions: Renal tubular organoids can be developed simultaneously and in a single medium. Our method is expected to be used as a human pathological model.

Introduction: The integration of artificial intelligence (AI) into medical practices has transformed fields like gastroenterological surgery. AI predicts patient prognoses using clinical and pathological data and develops technologies that create three-dimensional (3D) models for surgical simulations, thereby enhancing surgical precision and care quality.

Methods: At our facility, AI-driven diagnostic and treatment systems have been developed under the "Strategic Innovation Creation Program" by the Cabinet Office. Our research focuses on perioperative care by constructing 3D models from preoperative imaging data to develop surgical support systems for preoperative simulations and navigation during surgery. Additionally, we use deep learning to predict disease progression and complications and natural language processing to analyze electronic medical records to predict postoperative complications.

Results: AI-based surgical support systems effectively convert two-dimensional imaging data into 3D models, thereby improving surgical precision. Predictive models for disease progression and complications developed using deep learning have high accuracy. AI applications in diagnostic imaging enable early detection and improved treatment planning. AI-based tools for informed consent and patient support enhance patient understanding and satisfaction.

Conclusions: AI revolutionizes medical practices by improving diagnostic accuracy, surgical precision, and patient outcomes. Future projects will integrate remote diagnostic and treatment planning; leverage AI for comprehensive, high-quality care; and support work-style reforms for healthcare professionals. Advancements in AI will overcome current medical challenges and enhance the communication between physicians and patients.

Introduction: Hypophosphatasia has been reported to develop nephrocalcinosis, renal stone, and chronic kidney failure. We investigated their renal impairments in the adults with hypophosphatasia to know the phenotype-genotype correlation.

Methods: We subjected 11 patients with hypophosphatasia who were diagnosed by chance in the routine medical health checkup. Most cases had past history of fracture. Bone mineral density showed low or lower normal limit.

Results: Four of six patients also had high levels of ionized Ca. In subjected six cases, four showed high urinary Ca excretion. Nephrocalcinosis is found in five cases even if the symptoms of hypophosphatasia are mild. Four out of five patients with a mutation of c.1559del in ALPL had nephrocalcinosis and/or kidney stones. One patient already developed hydronephrosis. One of six patients with other mutations showed nephrocalcinosis.

Conclusions: The phenotype-genotype correlation between renal impairment and c.1559del of ALPL gene was suggested.

Introduction: Excessive workload among medical residents remains a social issue, particularly in Japan. The government requires management of overtime work in health institutions. Among young healthcare workers, the demand for sustainable work-life balance is increasing. This study evaluated the current workload and work allocation of postgraduate residents using a mobile application.

Methods: A cross-sectional study including postgraduate trainees from three major teaching hospitals was conducted in 2021 using a mobile application. The residents recorded their work (direct patient care, indirect patient care, education, research, administration, personal time, and others) using the application. The data were descriptively analyzed.

Results: A total of 69 residents participated in the survey. Their mean working hours was 11 h and 45 min, and their mean sleep time was 6 h and 18 min. The proportions of work allocation time by category were 35.5% for direct patient care; 35.5%, indirect patient care; 10.1%, personal time; 9.4%, education; 8.6%, administration; and 1%, research.

Conclusions: The development of a mobile application enabled us to measure the residents' workload and work allocation. The time spent on direct and indirect patient care increased over a decade, whereas the time spent on educational activities and research remained limited.

Introduction: The prognosis for activities of daily living (ADL) ability after stroke is negatively influenced by undernutrition and impaired balance. However, the association between undernutrition and balance improvement has not yet been elucidated. This study aimed to investigate the influence of undernutrition on balance function improvement in patients with stroke.

Methods: This retrospective observational study included patients with cerebral infarction aged ≥65 years. The study period was from May 2018 to May 2022. The patients were divided into undernutrition and intact nutrition groups according to the Global Leadership Initiative on Malnutrition criteria. The primary outcome was the change in the Berg Balance Scale (BBS) score (BBS score at discharge − BBS score at admission).

Results: This study included 304 patients (mean age, 79.2 ± 8.1 years; 173 men and 131 women). These patients were divided into the undernutrition (N = 114) and intact nutrition (N = 190) groups. The undernutrition group demonstrated lower BBS scores at admission (16.0 ± 17.1 vs. 28.3 ± 18.4, p < 0.001) and at discharge (24.2 ± 19.6 vs. 40.0 ± 16.9, p < 0.001) than the intact nutrition group. After adjusting for confounding factors, undernutrition was associated with a smaller change in the BBS score (coefficient = −2.988, 95% confidence interval = −5.481 to −0.495, p = 0.019).

Conclusions: Undernutrition negatively influences balance function recovery in post-stroke patients. A strategy aimed at improving nutritional status could have beneficial effects on patients' balance function.

Introduction: This study examined the association of trunk function evaluated using Functional Assessment for Control of Trunk (FACT) with independent walking. It aimed to determine the effectiveness of the FACT cutoff score in predicting independent walking at hospital discharge.

Methods: This retrospective observational study included patients with cerebral infarction. The patients were categorized into the independent (Functional Independence Measure [FIM] locomotion walking score of the patient was ≥6; n = 102) and dependent (≤5; n = 111) groups based on the FIM locomotion scale at discharge. Multivariate logistic regression analysis was employed to determine the significant independent variables on admission for predicting independent walking at discharge. Furthermore, the receiver operating characteristic was used to calculate the cutoff value for admission status.

Results: A total of 213 patients (122 men and 91 women) were included in this study. The independent group had higher scores in FACT (15.0 [12.0-20.0] vs. 6.0 [2.0-12.0], P < 0.001) on admission than the dependent group. The results of the multivariate logistic regression analysis indicated that the factors associated with independent walking were the FACT and Mini-Mental State Examination-Japanese (MMSE-J) on admission. The optimal cutoff score for the FACT on admission was 8, and the area under the curve for the FACT scores on admission when discriminating between independent walking at discharge was 0.82.

Conclusions: The results of this study can facilitate the optimization of patient rehabilitation as early as possible. The effects of improved trunk function require further validation through prospective observational studies.

Introduction: Fractures cause serious impediments to employment. In Japan, there is insufficient evidence regarding social factors, such as nonregular employment, and return to work (RTW) after an injury. This study aimed to determine the association between social factors and RTW following injury.

Methods: This multicenter cohort study was conducted from 2015 to 2018 and included 674 patients aged 18-65 years who were workers at the time of injury and underwent surgery for long bone fractures of the upper or lower extremities. The primary outcome was the RTW rate within 2 years following injury. Data on RTW at 6 months, 1 year, and 2 years were collected. Observational data following RTW were not included. The association between RTW at 6 months and within 2 years following injury and social factors were evaluated via logistic regression and Cox proportional hazards regression analyses, respectively, after adjusting for patient- and fracture-related factors.

Results: Overall, 525 (77.9%) and 602 patients (89.3%) resumed work at 6 months and within 2 years, respectively, following injury. Physical labor, open fractures, and chronic pain were associated with the RTW at both 6 months and within 2 years. However, nonregular employment and workers' compensation insurance were only associated with RTW at 6 months. Social factors were associated with the RTW rate at 6 months but not within 2 years following injury.

Conclusions: Approximately 10% of patients with fractures did not resume work within 2 years following injury. This analysis points to social factors as a risk for delaying early RTW and has implications for interventions at the policy level.

Introduction: Obstetric hemorrhage is a leading cause of pregnancy-related mortality. Our hospital protocol states that patients with obstetric hemorrhage undergo initial imaging with contrast-enhanced dynamic computed tomography (CE-dCT) to ascertain the presence and location of active bleeding, followed by tailored therapeutic interventions. Herein, we aimed to elucidate the prevailing status and clinical outcomes of obstetric hemorrhage cases at our institution, which are characterized by a distinctive, methodical treatment approach.

Methods: This retrospective observational study included 150 patients with obstetric hemorrhage. Clinical information, including bleeding volume, hemorrhage etiology, therapeutic intervention, transfusion quantity, patient outcome, and CE-dCT findings, were explored.

Results: The leading cause of obstetric hemorrhage was atonic bleeding (55%), followed by vaginal hematoma (13%) and retained placenta (11%). The median amount of bleeding was 2,803 mL, and the median volume of red blood cells (RBC) and fresh frozen plasma (FFP) required was 6 units. Blood loss and transfusion volume were similar regardless of the cause of obstetric hemorrhage. Conservative management, such as uterotonics or balloon tamponade, achieved hemostasis in 57% of patients, whereas 43% required invasive interventions, such as transcatheter arterial embolization. CE-dCT was performed on 85% of patients, and extravasation was detected in 53%. Moreover, "PRACE," characterized by Postpartum hemorrhage, Resistance to treatment, and Arterial Contrast Extravasation on CE-dCT scans, potentially requires massive blood transfusions and invasive treatment.

Conclusions: Although obstetric hemorrhage encompasses a diverse array of pathologies, medical practitioners must recognize that approximately 3,000 mL of blood is lost and at least 6 units of RBC and FFP are required, irrespective of the cause. CE-dCT plays a pivotal role in elucidating the etiology of obstetric hemorrhage and guiding therapeutic interventions.

Introduction: We examined the clinical characteristics and perinatal outcomes of pregnancy with macrosomia (birth weight ≥ 4,000 g) compared with delivery with neonatal birth weight of 3,500-3,999 g.

Methods: This study analyzed data obtained from singleton pregnant women who delivered at ≥22 weeks of gestation from January 2002 to December 2010.

Results: During the study period, there were 12,497 singleton deliveries, of which 136 (1.1%) had macrosomia (average: 4,181 g, range: 4,000-4,726 g; macrosomia group) and 1,139 (9.1%) had neonatal birth weight of 3,500-3,999 g (average: 3,670 g; control group). Compared with the control group, the macrosomia group had advanced maternal age and births after 41 weeks of gestation. In addition, elective cesarean delivery was more common in the macrosomia group (P < 0.01). Furthermore, the rate of shoulder dystocia was higher in this group in cases of vaginal delivery (P < 0.01). A high rate of neonatal asphyxia was also observed in the macrosomia group (P < 0.01), although there were no significant differences in the rate of low umbilical artery pH or the incidence of neonatal hypoglycemia between the groups. Multivariate analysis revealed that the significant complications in the macrosomia group compared with the control group were shoulder dystocia (P = 0.01) and neonatal asphyxia (P = 0.03).

Conclusions: The results of this study indicate that particular attention should be paid to the possibility of shoulder dystocia during delivery of macrosomia.

Introduction: Inadequate management of low-density lipoprotein (LDL) cholesterol is more common in female patients than in male patients in the context of preventing atherosclerotic cardiovascular disease. Moreover, the effect of physician gender on patient outcomes has been acknowledged. However, to date, no study in Japan has investigated this issue or explored the potential interactions between patient sex and physician gender. This study aimed to assess disparities in achieving LDL cholesterol targets between male and female patients and examine the impact of the patient-physician gender dyad.

Methods: We conducted a cross-sectional study using electronic medical records from an urban Japanese clinic. Patients aged 40-79 years with coronary artery disease, noncardiogenic stroke, or diabetes mellitus were included in the study. The modified Poisson regression model with robust error variance was used, and patients were stratified by sex to evaluate the interaction between patient sex and physician gender.

Results: Among the 714 patients (44.1% women), female patients were less likely to achieve LDL cholesterol targets than male patients (70.7% male vs. 63.9% female). Adjusted analyses revealed that this trend persisted for female patients (adjusted prevalence ratio: 0.86, 95% confidence interval [CI]: 0.77-0.96). A notable interaction between patient sex and physician gender was observed; male patients managed by female physicians had lower LDL cholesterol target achievement than male patients managed by male physicians (adjusted prevalence ratio: 0.74 [95% CI: 0.62-0.88]).

Conclusions: Female patients were less likely to achieve LDL cholesterol targets, and patient-physician gender discordance was associated with poorer lipid management. These findings highlight the need for quality improvement interventions to address the disparity.

Introduction: This prospective, multicenter, single-arm Phase II trial investigates the feasibility and the safety of tailored axillary surgery (TAS) in patients with clinically node-positive breast cancer who are undergoing upfront surgery. The trial aims to establish the criteria for safely omitting axillary lymph node dissection (ALND) in these cases, potentially shifting breast cancer management by minimizing surgical complications and preserving the patients' quality of life (QOL).

Methods: The study includes patients who were diagnosed with invasive breast cancer, particularly those with limited metastatic lymph nodes. The primary objective of this work is to determine the specific combination of clinical and pathological factors that would result in a non-TAS lymph node metastasis proportion of less than 10%. The secondary objectives include assessing the identification rate of the metastatic lymph nodes, the incidence of upper limb lymphedema, and the QOL measures.

Results: The results will identify the patient eligibility criteria for the Phase III TAS trial, potentially allowing the omission of ALND in selected patients. This may lead to reduced surgical complications and better preservation of the QOL of patients with breast cancer.

Conclusions: The trial's outcome will contribute to the development of the criteria for safely omitting ALND in certain patients with clinically node-positive breast cancer. This approach aims to enhance breast cancer management by reducing surgical burden and improving the patient outcomes.

jRCTs: 061220113

On December 2, 2023, Japan's Ministry of Health, Labour and Welfare (MHLW) announced an ordinance regulating the possession, consumption, and distribution of hexahydrocannabihexol (HHCH) except for medical purposes. HHCH, a synthetic cannabinoid, has been linked to central nervous system symptoms, including nausea, dizziness, and numbness, presumably due to its structural similarity to tetrahydrocannabinol. This regulatory action reflects Japan's historical drug regulation approach, which has evolved to address synthetic substances not covered by earlier laws. The emergence of new psychoactive substances has led to increased poisoning cases and necessitated Japan to introduce a generic scheduling system and collectively regulate these compounds. Despite the reduction in designer drug-related arrests following system implementation, recent trends have shown a resurgence in arrests, partly because of the increased online accessibility of these substances. The persistence of HHCH gummy manufacturers highlights the limitations of current regulations. Thus, enhancing health literacy and social responsibility among consumers and proactive measures by healthcare professionals are essential to mitigate the public health risks associated with these emerging substances. Regulatory frameworks should prioritize public health over economic benefits.

The advantages and disadvantages of the use of generative artificial intelligence, such as ChatGPT, in medical writing have been widely discussed; however, two concerns remain largely unexplored. The first involves "human touch," such as personal anecdotes and experiences. This touch often distinguishes human-written papers from those generated by ChatGPT as ChatGPT cannot independently access personal experiences. Although ChatGPT may mimic humanlike behavior, including the incorporation of a human touch, it lacks genuine emotions. With the lack of established guidelines on the acceptable levels of ChatGPT use and imperfect detection tools, many authors fear that their work could be perceived as overly reliant on ChatGPT. I worry that writers may artificially insert forced personal touches simply to assert their own writing. The second concern is the authors' worry and doubt about whether to use ChatGPT and, if so, to what extent, which may disrupt their reflective and quiet writing process. While I acknowledge the lack of empirical data, I offer practical suggestions to balance the benefits of ChatGPT assistance and the preservation of the integrity of human writing in medical publications.

Pseudomeningocele (PMC) after spinal surgery involves cerebrospinal fluid (CSF) continuously leaking from a compromised dura mater and accumulating subcutaneously. PMC is a rare postcervical spine surgery that can be spontaneously resolved; therefore, asymptomatic cases are often observed. This report presents a case of communicating hydrocephalus resulting from PMC following posterior decompression at the craniocervical junction. A 77-year-old man with advanced dementia and a history of C2-T3 posterior fixation was admitted after a head injury, presenting quadriplegia at the MMT2 level. Magnetic resonance imaging (MRI) revealed severe spinal cord compression at C1/2. A posterior decompression of the craniocervical junction was performed. However, dura mater damage occurred during surgery, and the damaged area was repaired with artificial dura mater and fibrin glue. One month postsurgery, subcutaneous swelling was observed and an MRI identified PMC. As the patient was asymptomatic, observation was chosen. Four months postsurgery, the patient exhibited drowsiness and vomiting. An MRI was conducted, revealing the presence of communicating hydrocephalus. Ventricular drainage and ventriculoperitoneal shunt were performed, and the hydrocephalus and PMC improved. Dural injury during spinal surgery is a relatively common complication; but, if inadequately repaired, CSF leakage may persist and lead to PMC. Persistent leakage of CSF into the PMC may have hindered CSF absorption, leading to communicating hydrocephalus. Severe cognitive impairment and quadriplegia may have hindered neurological evaluation and delayed the detection of the hydrocephalus, and more careful follow-up would have been desirable.

Down syndrome (DS) is a risk factor for severe food protein-induced enterocolitis syndrome (FPIES), with DS patients tending to have multiple-food FPIES. This is the first case where a DS infant with a history of severe chronic FPIES to milk and soy could effectively be introduced with some untested high-risk foods through hospital-based oral food challenges (OFCs).

The infant is a 20-month-old girl with DS, who was diagnosed with milk- and soy-induced FPIES. Considering her history of intensive care unit care for severe FPIES reactions, we considered that introducing other high-risk foods, such as wheat and hen's egg (white and yolk), at home was not appropriate for her. We offered hospital-based OFCs effectively and safely by introducing wheat and hen's egg as high-risk foods against FPIES to the 20-month-old infant. As a result, she tolerated soy-based seasoning, wheat, and egg whites without any symptoms, but she developed frequent vomiting after ingesting egg yolk. We did a prompt intervention with intravenous fluid replacement to prevent severe adverse conditions. After discharge, she exhibited an FPIES symptom as a consequence of ingesting green peas and miso; hence, we recommended the elimination of peas, in addition to soy, milk, and egg yolk, from her diet. She remained symptom-free since adhering to this dietary regimen.

In severe FPIES children, it is encouraged to introduce unconsumed high-risk foods in the hospital safely to avoid severe reactions at home and prevent unnecessary food eliminations.