JMA journal最新文献

{"title":"Artificial Intelligence for Patient Safety and Surgical Education in Neurosurgery.","authors":"Taku Sugiyama, Hiroyuki Sugimori, Minghui Tang, Miki Fujimura","doi":"10.31662/jmaj.2024-0141","DOIUrl":"10.31662/jmaj.2024-0141","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":73550,"journal":{"name":"JMA journal","volume":"8 1","pages":"76-85"},"PeriodicalIF":1.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799567/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Academic Writing.","authors":"Soichiro Saeki","doi":"10.31662/jmaj.2024-0224","DOIUrl":"10.31662/jmaj.2024-0224","url":null,"abstract":"","PeriodicalId":73550,"journal":{"name":"JMA journal","volume":"8 1","pages":"314-315"},"PeriodicalIF":1.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799597/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathology Foundation Models.","authors":"Mieko Ochi, Daisuke Komura, Shumpei Ishikawa","doi":"10.31662/jmaj.2024-0206","DOIUrl":"10.31662/jmaj.2024-0206","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":73550,"journal":{"name":"JMA journal","volume":"8 1","pages":"121-130"},"PeriodicalIF":1.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Return to Work after Upper or Lower Extremity Long Bone Fractures: Social Factors in a Japanese Multicenter Cohort Study.","authors":"Keisuke Ishii, Hiroyuki Oka, Koichi Inokuchi, Takashi Maehara, Akiro Higashikawa, Yoshiaki Sasashige, Hiroaki Konishi, Fumitake Nakajima, Yoshimasa Tomita, Shingo Nobuta, Yoji Mikami","doi":"10.31662/jmaj.2024-0157","DOIUrl":"10.31662/jmaj.2024-0157","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":73550,"journal":{"name":"JMA journal","volume":"8 1","pages":"234-241"},"PeriodicalIF":1.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Change in Breast Cancer Screening Participation during COVID-19 Based on the 2019 and 2022 Comprehensive Survey of Living Conditions in Japan.","authors":"Chitose Kawamura, Masao Iwagami, Jun Komiyama, Yuta Taniguchi, Yu Sun, Rie Masuda, Takehiro Sugiyama, Hiroko Bando, Tomomi Kihara, Hiroyasu Iso, Nanako Tamiya","doi":"10.31662/jmaj.2024-0234","DOIUrl":"10.31662/jmaj.2024-0234","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":73550,"journal":{"name":"JMA journal","volume":"8 1","pages":"183-190"},"PeriodicalIF":1.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Healthy Lifestyle Habits Have Interacted with SARS-CoV-2 Infection and the Effectiveness of COVID-19 Vaccinations: A Comment.","authors":"Hinpetch Daungsupawong, Viroj Wiwanitkit","doi":"10.31662/jmaj.2024-0216","DOIUrl":"10.31662/jmaj.2024-0216","url":null,"abstract":"","PeriodicalId":73550,"journal":{"name":"JMA journal","volume":"8 1","pages":"312-313"},"PeriodicalIF":1.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143383791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resolution of Dependent Rubor after Revascularization for Peripheral Artery Disease.","authors":"Kazuhiko Hirokawa, Hiroki Kojima, Eiji Hiraoka","doi":"10.31662/jmaj.2024-0237","DOIUrl":"10.31662/jmaj.2024-0237","url":null,"abstract":"","PeriodicalId":73550,"journal":{"name":"JMA journal","volume":"8 1","pages":"300-301"},"PeriodicalIF":1.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799383/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain Magnetic Resonance Imaging Findings of Shiga Toxin-producing <i>Escherichia coli</i> Hemolytic Uremic Syndrome-associated Encephalopathy.","authors":"Takashi Nakamura, Hirohisa Fujikawa, Norimichi Uenishi","doi":"10.31662/jmaj.2024-0201","DOIUrl":"10.31662/jmaj.2024-0201","url":null,"abstract":"","PeriodicalId":73550,"journal":{"name":"JMA journal","volume":"8 1","pages":"298-299"},"PeriodicalIF":1.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emerging New Era of Artificial Intelligence and Digital Medicine-directed Management of Chronic Kidney Disease.","authors":"Kouichi Tamura, Masashi Sakai, Tamio Iwamoto, Shin-Ichiro Yoshida, Jin Oshikawa","doi":"10.31662/jmaj.2024-0221","DOIUrl":"10.31662/jmaj.2024-0221","url":null,"abstract":"","PeriodicalId":73550,"journal":{"name":"JMA journal","volume":"8 1","pages":"57-59"},"PeriodicalIF":1.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI Era Is Coming: The Implementation of AI Medical Devices to Endoscopy.","authors":"Yosuke Tsuji, Mitsuhiro Fujishiro","doi":"10.31662/jmaj.2024-0205","DOIUrl":"10.31662/jmaj.2024-0205","url":null,"abstract":"","PeriodicalId":73550,"journal":{"name":"JMA journal","volume":"8 1","pages":"64-65"},"PeriodicalIF":1.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143384101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}