Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows rais...Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.展开更多
China implemented its Action Plan of Air Pollution Prevention and Control(APAPPC)in 2013 as a major step in national air-quality management.This study treated the APAPPC as a quasi-experiment,drawing on the Grossman a...China implemented its Action Plan of Air Pollution Prevention and Control(APAPPC)in 2013 as a major step in national air-quality management.This study treated the APAPPC as a quasi-experiment,drawing on the Grossman and Cropper models,to examine how air pollution affected individual health capital and medical service demand.Using panel data from the China Health and Retirement Longitudinal Study for 2011,2013,2015,and 2018,the analysis applied a Heckman two-stage model and difference-in-differences estimation to identify the policy's effects on medical expenditure.The results showed that the APAPPC significantly reduced annual health spending,with stronger effects among women,older adults,and rural residents.The mechanism analysis indicated that the reduction in respiratory diseases played a key role.This study provides evidence that supports further airpollution control in China and offers useful insights for other developing countries.展开更多
Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation...Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment.展开更多
BACKGROUND Drug utilization research has an important role in assisting the healthcare administration to know,compute,and refine the prescription whose principal objective is to enable the rational use of drugs.Resear...BACKGROUND Drug utilization research has an important role in assisting the healthcare administration to know,compute,and refine the prescription whose principal objective is to enable the rational use of drugs.Research in developing nations relating to the cost of treatment is scarce when compared with developed countries.Thus,the drug utilization research studies from developing nations are most needed,and their number has been growing.AIM To evaluate patterns of utilization of antipsychotic drugs and direct medical cost analysis in patients newly diagnosed with schizophrenia.METHODS The present study was observational in type and based on a retrospective cohort to evaluate patterns of utilization of antipsychotic drugs using World Health Organization(WHO)core prescribing indicators and anatomical therapeutic chemical/defined daily dose indicators.We also calculated direct medical costs for a period of 6 months.RESULTS This study has found that atypical antipsychotics are the mainstay of treatment for schizophrenia in every age group and subcategories of schizophrenia.The evaluation based on WHO prescribing indicators showed a low average number of drugs per prescription and low prescribing frequency of antipsychotics from the National List of Essential Medicines 2015 and the WHO Essential Medicines List 2019.The total mean drug cost of our study was 1396 Indian rupees.The total mean cost due to the investigation in our study was 1017.34 Indian rupees.Therefore,the total mean direct medical cost incurred on patients in our study was 4337.28 Indian rupees.CONCLUSION The information from the present study can be used for reviewing and updating treatment policy at the institutional level.展开更多
Background:Medical imaging advancements are constrained by fundamental trade-offs between acquisition speed,radiation dose,and image quality,forcing clinicians to work with noisy,incomplete data.Existing reconstructio...Background:Medical imaging advancements are constrained by fundamental trade-offs between acquisition speed,radiation dose,and image quality,forcing clinicians to work with noisy,incomplete data.Existing reconstruction methods either compromise on accuracy with iterative algorithms or suffer from limited generalizability with task-specific deep learning approaches.Methods:We present LDM-PIR,a lightweight physics-conditioned diffusion multi-model for medical image reconstruction that addresses key challenges in magnetic resonance imaging(MRI),CT,and low-photon imaging.Unlike traditional iterative methods,which are computationally expensive,or task-specific deep learning approaches lacking generalizability,integrates three innovations.A physics-conditioned diffusion framework that embeds acquisition operators(Fourier/Radon transforms)and noise models directly into the reconstruction process.A multi-model architecture that unifies denoising,inpainting,and super-resolution via shared weight conditioning.A lightweight design(2.1M parameters)enabling rapid inference(0.8s/image on GPU).Through self-supervised fine-tuning with measurement consistency losses adapts to new imaging modalities using fewer annotated samples.Results:Achieves state-of-the-art performance on fastMRI(peak signal-to-noise ratio(PSNR):34.04 for single-coil/31.50 for multi-coil)and Lung Image Database Consortium and Image Database Resource Initiative(28.83 PSNR under Poisson noise).Clinical evaluations demonstrate superior preservation of anatomical structures,with SSIM improvements of 8.8%for single-coil and 4.36%for multi-coil MRI over uDPIR.Conclusion:It offers a flexible,efficient,and scalable solution for medical image reconstruction,addressing the challenges of noise,undersampling,and modality generalization.The model’s lightweight design allows for rapid inference,while its self-supervised fine-tuning capability minimizes reliance on large annotated datasets,making it suitable for real-world clinical applications.展开更多
Objectives This study aimed to design and evaluate a detection system for the accidental dislodgement of head-and-neck medical supplies through hand position recognition and tracking in Intensive Care Unit(ICU)patient...Objectives This study aimed to design and evaluate a detection system for the accidental dislodgement of head-and-neck medical supplies through hand position recognition and tracking in Intensive Care Unit(ICU)patients.Methods We conducted a single-center,prospective,parallel-group feasibility randomized controlled trial.We recruited 80 participants using convenience sampling from the ICU of a hospital in Ningbo City,Zhejiang Province,between March 2025 and June 2025,and they were randomly assigned to either the control group(routine care)or the intervention group(routine care plus image recognition-based detection system).The system continuously tracked patients’hand positions via bedside cameras and generated real-time alarms when hands entered predefined risk zones,notifying on-duty nurses to enable early intervention.System stability was assessed by continuous system uptime;system performance and clinical feasibility were evaluated by the frequencies of risk actions and accidental dislodgement of medical supplies(ADMS).Results All 80 participants completed the intervention,with 40 patients in each group.The baseline characteristics and median observation time of the two groups were balanced(intervention group:48 h/patient vs.control group:49 h/patient).Compared with the control group,the intervention group showed fewer ADMS(2/40 vs.9/40)and detected more risk actions per 100 h(36 vs.25);all system-detected events had corroborating images with complete concordance on manual review,and all nurse-recorded hand-contact events were accurately captured.Conclusions The study demonstrated that the image recognition-based detection system can function stably in clinical settings,providing accurate and continuous surveillance while supporting the early detection of risk actions.By reducing the observation burden and offering real-time cognitive support,the system complements routine nursing care and serves as an additional safety measure in ICU practice.With further optimization and larger multicenter validation,this approach could have the potential to make a significant contribution to the development of smart ICUs and the broader digital transformation of nursing care.展开更多
Background:Medical artificial intelligence(MAI)is a synthesis of medical science and artificial intelligence development,serving as a crucial field in the current advancement and application of AI.In the process of de...Background:Medical artificial intelligence(MAI)is a synthesis of medical science and artificial intelligence development,serving as a crucial field in the current advancement and application of AI.In the process of developing medical AI,there may arise not only legal risks such as infringement of privacy rights and health rights but also ethical risks stemming from violations of the principles of beneficence and non-maleficence.Methods:To effectively address the damages caused by MAI in the future,it is necessary to establish a hierarchical governance system with MAI.This paper examines the systematic collection of local practices in China and the induction and integration of legal remedies for the damage of MAI.Results:To effectively address the ethical and legal challenges of medical artificial intelligence,a hierarchical regulatory system should be established,which based on the impact of intervention measures on natural rights and differences in intervention timing.This paper finally obtains a legal hierarchical governance system corresponding to the ethical risks and legal risks of MAI in China.Conclusion:The Chinese government has formed a multi-agent governance system based on the impact of risks on rights and the timing of legal intervention,which provides a reference for other countries to follow up on the research on MAI risk management.展开更多
In recent years,with the accelerating aging process of the population,China has entered an aging society,and the number of elderly patients with chronic diseases has been increasing.The traditional medical and elderly...In recent years,with the accelerating aging process of the population,China has entered an aging society,and the number of elderly patients with chronic diseases has been increasing.The traditional medical and elderly care service models can no longer fully meet their needs.The integrated medical and elderly care model has emerged as the times require.It organically combines medical resources with elderly care resources to provide comprehensive and continuous health management services for the elderly,becoming an important approach to solving the problems of chronic disease management among the elderly.In this regard,this paper first elaborates on the role of integrated medical and elderly care in the management of chronic diseases among the elderly,and then puts forward application strategies of integrated medical and elderly care in the management of chronic diseases among the elderly,in order to provide certain reference for relevant researchers.展开更多
Mongolian medicine posits that disruptions to the natural balance of the three roots and seven elements within the human body may lead to ocular disorders,vision impairment,and ultimately myopia.China’s children and ...Mongolian medicine posits that disruptions to the natural balance of the three roots and seven elements within the human body may lead to ocular disorders,vision impairment,and ultimately myopia.China’s children and adolescents not only exhibit high myopia rates but also face increasingly prominent issues of younger onset and severe progression,which critically impact the nation’s future and require urgent attention.Myopia prevention constitutes a systematic project.Traditional Mongolian moxibustion therapy works by applying heat stimulation to specific acupoints to warm meridians,harmonize Qi-blood circulation,regulate elemental balance,thereby enhancing immunity for disease prevention.This holistic approach features non-invasive application with minimal side effects.However,current interventions in myopia management through this method still face challenges including inconsistent operational protocols and insufficiently systematic collaborative research.This paper reviews recent advancements in early intervention using Mongolian moxibustion therapy for myopia,providing insights to optimize myopia prevention strategies.展开更多
On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th Nation...On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th National Congress of the Communist Party of China,China has vigorously promoted the integration and implementation of the Healthy China and Digital China strategies.The National Health Commission has prioritized the development of health and medical big data,issuing policies to promote standardized applica-tions and foster innovation in"Internet+Healthcare."Biomedical data has significantly contributed to preci-sion medicine,personalized health management,drug development,disease diagnosis,public health monitor-ing,and epidemic prediction capabilities.展开更多
For the history of medical culture in the world,the exchange and transmission of medical knowledge has formed an important part of mutual learning among different cultures,which has also increasingly shown unique acad...For the history of medical culture in the world,the exchange and transmission of medical knowledge has formed an important part of mutual learning among different cultures,which has also increasingly shown unique academic value in the study of knowledge history.Traditional Eastern medicine(such as Chinese medicine,Indian ayurvedic medicine,Persian medicine,Arabic medicine),and other medical systems in the ancient Western world(including Greek medicine and Roman medicine)have left precious literature/texts,cultural relics(for example,pills,preparations,medical instruments),folklore and legends,which truly record the process of learning,transplantation,fusion and succession after the encounter of different medical systems at least for the past two thousand years.展开更多
This paper explores increased use of the concept of“medical bilingualism”since 2015 as scholars,especially of East Asian medical history and anthropology,have applied it to engagements between two medical systems.It...This paper explores increased use of the concept of“medical bilingualism”since 2015 as scholars,especially of East Asian medical history and anthropology,have applied it to engagements between two medical systems.It reveals an ongoing evolution in the way that scholars understand what a medical system is and how medical systems are differentiated and compared with one another.The image of culturally homogeneous systems of meaning and practice that dominated mid-twentieth-century scholarship on medical systems(especially using the category of ethnomedicines)has been giving way to a more culturally heterogeneous and cosmopolitan picture of how medical practitioners evolve,integrate,and differentiate medical concepts and practices in the context of contemporary societies and the new forms of life they engender.This reformulated concept of medical bilingualism emphasizes the ways in which medical systems overlap yet remain distinct.First,the paper summarizes results of an experiment with AI searches on medical bilingualism,then narrates its historiography both pre-COVID-19 and during COVID-19,and finally concludes with some reflections on language ideology,multilingualism,and medical pluralism.展开更多
Welcome to the 4th volume of Biomedical Engineering Communications the first issue of 2025!Biomedical engineering is a rapidly evolving field that combines engineering principles with medical and biological sciences t...Welcome to the 4th volume of Biomedical Engineering Communications the first issue of 2025!Biomedical engineering is a rapidly evolving field that combines engineering principles with medical and biological sciences to create innovative healthcare technologies.Biomedical engineering brings an interdisciplinary,problem-solving approach to bioengineering,biology and medicine.This interdisciplinary field is essential for developing advanced medical devices,diagnostic tools,and therapeutic solutions that enhance patient care and improve health outcomes.It allows them to develop technologies and systems that directly contribute to diagnosing,treating and preventing diseases.展开更多
The medical education of the Song dynasty constitutes a pivotal aspect within the broader framework of ancient Chinese medical education. The advent of the imperial examination system coincided with the emergence of a...The medical education of the Song dynasty constitutes a pivotal aspect within the broader framework of ancient Chinese medical education. The advent of the imperial examination system coincided with the emergence of a medical examination system, which served as the cornerstone for the subsequent evolution of medical education. According to historical records, the Song government established dedicated medical departments, along with comprehensive systems encompassing medical professors, students, and examinations. By examining extant medical historical documents, such as Tai Yi Ju Zhu Ke Cheng Wen Ge(《太医局诸科程文格》 Examination Answers and Standards of the Imperial Medical Bureau), researchers and readers can obtain a comprehensive understanding of the medical system that prevailed in the Song dynasty. While the intricate details of medical education during this era are not explicitly documented in historical records, modern researchers have the opportunity to uncover the entire view of medical education, particularly the medical examination system, through rigorous analysis of these extant historical medical documents. Such studies offer valuable insights into the developmental trajectory of the ancient Chinese medical examination system and provide crucial references for contemporary medical education. By conducting in-depth literature research and analysis of Tai Yi Ju Zhu Ke Cheng Wen Ge, this study endeavors to reconstruct the authentic scenario of medical examinations in the Song dynasty, as presented in the document, for the benefit of modern readers and researchers.展开更多
This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain an...This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical content.The participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security weaknesses.This multi faceted exploration led to an indepth evaluation of the model’s performance and security.Notably,the correlation between accuracy,detection rate,and error rate was scrutinized.This analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance variability.The study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications.Applying blockchain to generative AI-created medical content addresses key personal information protection issues.By utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical images.This approach not only enhances security but also enables transparent and tamperproof record-keeping.Additionally,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient privacy.In conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient privacy.The proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content management.Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems,and visual diagnostic systems.展开更多
As the healthcare system advances and expands in its services,the challenges of remaining efficient become more important.Emergency medical services(EMS)are vital cornerstones of communities.In many countries,EMS is a...As the healthcare system advances and expands in its services,the challenges of remaining efficient become more important.Emergency medical services(EMS)are vital cornerstones of communities.In many countries,EMS is available for every individual,regardless of their social or insurance status,via a toll-free telephone number.Understanding the risk factors for busy days in EMSs might be helpful for improving the allocation of resources,which is the key to better care for all patients in the prehospital setting.[1]An important factor influencing ambulance call volume could be the interplay of public behavior and weather.展开更多
Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of ca...Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of care, a practice which has been found to reduce the time patients spend in hospitals, promote the quality of care and improve healthcare outcomes. Such tools include Medscape, VisualDx, Clinical Key, DynaMed, BMJ Best Practice and UpToDate. However, use of such tools has not yet been fully embraced in low-resource settings such as Uganda. Objective: This paper intends to collate data on the use and uptake of one such tool, UpToDate, which was provided at no cost to five medical schools in Uganda. Methods: Free access to UpToDate was granted through the IP addresses of five medical schools in Uganda in collaboration with Better Evidence at The Global Health Delivery Project at Harvard and Brigham and Women’s Hospital and Wolters Kluwer Health. Following the donation, medical librarians in the respective institutions conducted training sessions and created awareness of the tool. Usage data was aggregated, based on logins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows similar trends in increased usage over the period of August 2022 to August 2023 across the five medical schools. The most common topics viewed, mode of access (using either the computer or the mobile app), total usage by institution, ratio of uses to eligible users by institution and ratio of uses to students by institution are shared. Conclusion: The study revealed that the tool was used by various user categories across the institutions with similar steady improved usage over the year. These results can inform the librarians as they encourage their respective institutions to continue using the tool to support uptake of point-of-care tools in clinical practice.展开更多
The integration of traditional Chinese medicine(TCM)into clinical education presents an opportunity to enhance medical training by providing students with a more holistic approach to patient care.This study explores t...The integration of traditional Chinese medicine(TCM)into clinical education presents an opportunity to enhance medical training by providing students with a more holistic approach to patient care.This study explores the methods and challenges of integrating TCM theory into clinical internships for medical students at the First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine.A mixed-methods approach was employed,combining quantitative surveys and qualitative interviews with medical students,clinical instructors,and TCM practitioners.The results indicate that while students generally recognize the relevance and benefits of TCM in enhancing diagnostic skills and promoting a holistic understanding of health,several challenges remain.These include the lack of standardized TCM training,limited practical exposure to TCM diagnostic methods,and resistance from some clinical instructors.Despite these challenges,students expressed a strong desire for more structured TCM training and greater support from instructors.Based on the findings,the study recommends the standardization of TCM curricula,enhanced professional development for instructors,and increased collaboration between Western and TCM practitioners.The study concludes that the integration of TCM into medical education can significantly improve student clinical skills and patient care outcomes if appropriately structured and supported.展开更多
Since the start of the 21st century,health issues caused by exposure to geological materials,such as heavy metal pollution and/or imbalances in essential elements,have consistently affected about one billion people wo...Since the start of the 21st century,health issues caused by exposure to geological materials,such as heavy metal pollution and/or imbalances in essential elements,have consistently affected about one billion people worldwide.In response to this critical problem,the field of Medical Geology has developed through collaborative efforts from experts in various disciplines,including geologists,environmental scientists,biologists,biochemists,toxicologists,epidemiologists,and public health professionals.展开更多
A phase-aware cross-modal framework is presented that synthesizes UWF_FA from non-invasive UWF_RI for diabetic retinopathy(DR)stratification.A curated cohort of 1198 patients(2915 UWF_RI and 17,854 UWF_FA images)with ...A phase-aware cross-modal framework is presented that synthesizes UWF_FA from non-invasive UWF_RI for diabetic retinopathy(DR)stratification.A curated cohort of 1198 patients(2915 UWF_RI and 17,854 UWF_FA images)with strict registration quality supports training across three angiographic phases(initial,mid,final).The generator is based on a modified pix2pixHD with an added Gradient Variance Loss to better preserve microvasculature,and is evaluated using MAE,PSNR,SSIM,and MS-SSIM on held-out pairs.Quantitatively,the mid phase achieves the lowestMAE(98.76±42.67),while SSIM remains high across phases.Expert reviewshows substantial agreement(Cohen's κ=0.78–0.82)and Turing-stylemisclassification of 50%–70%of synthetic images as real,indicating strong perceptual realism.For downstream DR stratification,fusing multi-phase synthetic UWF_FA with UWF_RI in a Swin Transformer classifier yields significant gains over a UWF_RI-only baseline,with the full-phase setting(Set D)reaching AUC=0.910 and accuracy=0.829.These results support synthetic UWF_FA as a scalable,non-invasive complement to dye-based angiography that enhances screening accuracy while avoiding injection-related risks.展开更多
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.
基金Natural Science Foundation of Hubei Province(No.2025AFC041).
文摘China implemented its Action Plan of Air Pollution Prevention and Control(APAPPC)in 2013 as a major step in national air-quality management.This study treated the APAPPC as a quasi-experiment,drawing on the Grossman and Cropper models,to examine how air pollution affected individual health capital and medical service demand.Using panel data from the China Health and Retirement Longitudinal Study for 2011,2013,2015,and 2018,the analysis applied a Heckman two-stage model and difference-in-differences estimation to identify the policy's effects on medical expenditure.The results showed that the APAPPC significantly reduced annual health spending,with stronger effects among women,older adults,and rural residents.The mechanism analysis indicated that the reduction in respiratory diseases played a key role.This study provides evidence that supports further airpollution control in China and offers useful insights for other developing countries.
基金supported by Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/540/46.
文摘Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment.
文摘BACKGROUND Drug utilization research has an important role in assisting the healthcare administration to know,compute,and refine the prescription whose principal objective is to enable the rational use of drugs.Research in developing nations relating to the cost of treatment is scarce when compared with developed countries.Thus,the drug utilization research studies from developing nations are most needed,and their number has been growing.AIM To evaluate patterns of utilization of antipsychotic drugs and direct medical cost analysis in patients newly diagnosed with schizophrenia.METHODS The present study was observational in type and based on a retrospective cohort to evaluate patterns of utilization of antipsychotic drugs using World Health Organization(WHO)core prescribing indicators and anatomical therapeutic chemical/defined daily dose indicators.We also calculated direct medical costs for a period of 6 months.RESULTS This study has found that atypical antipsychotics are the mainstay of treatment for schizophrenia in every age group and subcategories of schizophrenia.The evaluation based on WHO prescribing indicators showed a low average number of drugs per prescription and low prescribing frequency of antipsychotics from the National List of Essential Medicines 2015 and the WHO Essential Medicines List 2019.The total mean drug cost of our study was 1396 Indian rupees.The total mean cost due to the investigation in our study was 1017.34 Indian rupees.Therefore,the total mean direct medical cost incurred on patients in our study was 4337.28 Indian rupees.CONCLUSION The information from the present study can be used for reviewing and updating treatment policy at the institutional level.
文摘Background:Medical imaging advancements are constrained by fundamental trade-offs between acquisition speed,radiation dose,and image quality,forcing clinicians to work with noisy,incomplete data.Existing reconstruction methods either compromise on accuracy with iterative algorithms or suffer from limited generalizability with task-specific deep learning approaches.Methods:We present LDM-PIR,a lightweight physics-conditioned diffusion multi-model for medical image reconstruction that addresses key challenges in magnetic resonance imaging(MRI),CT,and low-photon imaging.Unlike traditional iterative methods,which are computationally expensive,or task-specific deep learning approaches lacking generalizability,integrates three innovations.A physics-conditioned diffusion framework that embeds acquisition operators(Fourier/Radon transforms)and noise models directly into the reconstruction process.A multi-model architecture that unifies denoising,inpainting,and super-resolution via shared weight conditioning.A lightweight design(2.1M parameters)enabling rapid inference(0.8s/image on GPU).Through self-supervised fine-tuning with measurement consistency losses adapts to new imaging modalities using fewer annotated samples.Results:Achieves state-of-the-art performance on fastMRI(peak signal-to-noise ratio(PSNR):34.04 for single-coil/31.50 for multi-coil)and Lung Image Database Consortium and Image Database Resource Initiative(28.83 PSNR under Poisson noise).Clinical evaluations demonstrate superior preservation of anatomical structures,with SSIM improvements of 8.8%for single-coil and 4.36%for multi-coil MRI over uDPIR.Conclusion:It offers a flexible,efficient,and scalable solution for medical image reconstruction,addressing the challenges of noise,undersampling,and modality generalization.The model’s lightweight design allows for rapid inference,while its self-supervised fine-tuning capability minimizes reliance on large annotated datasets,making it suitable for real-world clinical applications.
文摘Objectives This study aimed to design and evaluate a detection system for the accidental dislodgement of head-and-neck medical supplies through hand position recognition and tracking in Intensive Care Unit(ICU)patients.Methods We conducted a single-center,prospective,parallel-group feasibility randomized controlled trial.We recruited 80 participants using convenience sampling from the ICU of a hospital in Ningbo City,Zhejiang Province,between March 2025 and June 2025,and they were randomly assigned to either the control group(routine care)or the intervention group(routine care plus image recognition-based detection system).The system continuously tracked patients’hand positions via bedside cameras and generated real-time alarms when hands entered predefined risk zones,notifying on-duty nurses to enable early intervention.System stability was assessed by continuous system uptime;system performance and clinical feasibility were evaluated by the frequencies of risk actions and accidental dislodgement of medical supplies(ADMS).Results All 80 participants completed the intervention,with 40 patients in each group.The baseline characteristics and median observation time of the two groups were balanced(intervention group:48 h/patient vs.control group:49 h/patient).Compared with the control group,the intervention group showed fewer ADMS(2/40 vs.9/40)and detected more risk actions per 100 h(36 vs.25);all system-detected events had corroborating images with complete concordance on manual review,and all nurse-recorded hand-contact events were accurately captured.Conclusions The study demonstrated that the image recognition-based detection system can function stably in clinical settings,providing accurate and continuous surveillance while supporting the early detection of risk actions.By reducing the observation burden and offering real-time cognitive support,the system complements routine nursing care and serves as an additional safety measure in ICU practice.With further optimization and larger multicenter validation,this approach could have the potential to make a significant contribution to the development of smart ICUs and the broader digital transformation of nursing care.
基金funded by China Law Society 2025 Annual Legal Research,Project grant number:CLS(2025)Y04.
文摘Background:Medical artificial intelligence(MAI)is a synthesis of medical science and artificial intelligence development,serving as a crucial field in the current advancement and application of AI.In the process of developing medical AI,there may arise not only legal risks such as infringement of privacy rights and health rights but also ethical risks stemming from violations of the principles of beneficence and non-maleficence.Methods:To effectively address the damages caused by MAI in the future,it is necessary to establish a hierarchical governance system with MAI.This paper examines the systematic collection of local practices in China and the induction and integration of legal remedies for the damage of MAI.Results:To effectively address the ethical and legal challenges of medical artificial intelligence,a hierarchical regulatory system should be established,which based on the impact of intervention measures on natural rights and differences in intervention timing.This paper finally obtains a legal hierarchical governance system corresponding to the ethical risks and legal risks of MAI in China.Conclusion:The Chinese government has formed a multi-agent governance system based on the impact of risks on rights and the timing of legal intervention,which provides a reference for other countries to follow up on the research on MAI risk management.
文摘In recent years,with the accelerating aging process of the population,China has entered an aging society,and the number of elderly patients with chronic diseases has been increasing.The traditional medical and elderly care service models can no longer fully meet their needs.The integrated medical and elderly care model has emerged as the times require.It organically combines medical resources with elderly care resources to provide comprehensive and continuous health management services for the elderly,becoming an important approach to solving the problems of chronic disease management among the elderly.In this regard,this paper first elaborates on the role of integrated medical and elderly care in the management of chronic diseases among the elderly,and then puts forward application strategies of integrated medical and elderly care in the management of chronic diseases among the elderly,in order to provide certain reference for relevant researchers.
文摘Mongolian medicine posits that disruptions to the natural balance of the three roots and seven elements within the human body may lead to ocular disorders,vision impairment,and ultimately myopia.China’s children and adolescents not only exhibit high myopia rates but also face increasingly prominent issues of younger onset and severe progression,which critically impact the nation’s future and require urgent attention.Myopia prevention constitutes a systematic project.Traditional Mongolian moxibustion therapy works by applying heat stimulation to specific acupoints to warm meridians,harmonize Qi-blood circulation,regulate elemental balance,thereby enhancing immunity for disease prevention.This holistic approach features non-invasive application with minimal side effects.However,current interventions in myopia management through this method still face challenges including inconsistent operational protocols and insufficiently systematic collaborative research.This paper reviews recent advancements in early intervention using Mongolian moxibustion therapy for myopia,providing insights to optimize myopia prevention strategies.
文摘On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th National Congress of the Communist Party of China,China has vigorously promoted the integration and implementation of the Healthy China and Digital China strategies.The National Health Commission has prioritized the development of health and medical big data,issuing policies to promote standardized applica-tions and foster innovation in"Internet+Healthcare."Biomedical data has significantly contributed to preci-sion medicine,personalized health management,drug development,disease diagnosis,public health monitor-ing,and epidemic prediction capabilities.
文摘For the history of medical culture in the world,the exchange and transmission of medical knowledge has formed an important part of mutual learning among different cultures,which has also increasingly shown unique academic value in the study of knowledge history.Traditional Eastern medicine(such as Chinese medicine,Indian ayurvedic medicine,Persian medicine,Arabic medicine),and other medical systems in the ancient Western world(including Greek medicine and Roman medicine)have left precious literature/texts,cultural relics(for example,pills,preparations,medical instruments),folklore and legends,which truly record the process of learning,transplantation,fusion and succession after the encounter of different medical systems at least for the past two thousand years.
文摘This paper explores increased use of the concept of“medical bilingualism”since 2015 as scholars,especially of East Asian medical history and anthropology,have applied it to engagements between two medical systems.It reveals an ongoing evolution in the way that scholars understand what a medical system is and how medical systems are differentiated and compared with one another.The image of culturally homogeneous systems of meaning and practice that dominated mid-twentieth-century scholarship on medical systems(especially using the category of ethnomedicines)has been giving way to a more culturally heterogeneous and cosmopolitan picture of how medical practitioners evolve,integrate,and differentiate medical concepts and practices in the context of contemporary societies and the new forms of life they engender.This reformulated concept of medical bilingualism emphasizes the ways in which medical systems overlap yet remain distinct.First,the paper summarizes results of an experiment with AI searches on medical bilingualism,then narrates its historiography both pre-COVID-19 and during COVID-19,and finally concludes with some reflections on language ideology,multilingualism,and medical pluralism.
文摘Welcome to the 4th volume of Biomedical Engineering Communications the first issue of 2025!Biomedical engineering is a rapidly evolving field that combines engineering principles with medical and biological sciences to create innovative healthcare technologies.Biomedical engineering brings an interdisciplinary,problem-solving approach to bioengineering,biology and medicine.This interdisciplinary field is essential for developing advanced medical devices,diagnostic tools,and therapeutic solutions that enhance patient care and improve health outcomes.It allows them to develop technologies and systems that directly contribute to diagnosing,treating and preventing diseases.
文摘The medical education of the Song dynasty constitutes a pivotal aspect within the broader framework of ancient Chinese medical education. The advent of the imperial examination system coincided with the emergence of a medical examination system, which served as the cornerstone for the subsequent evolution of medical education. According to historical records, the Song government established dedicated medical departments, along with comprehensive systems encompassing medical professors, students, and examinations. By examining extant medical historical documents, such as Tai Yi Ju Zhu Ke Cheng Wen Ge(《太医局诸科程文格》 Examination Answers and Standards of the Imperial Medical Bureau), researchers and readers can obtain a comprehensive understanding of the medical system that prevailed in the Song dynasty. While the intricate details of medical education during this era are not explicitly documented in historical records, modern researchers have the opportunity to uncover the entire view of medical education, particularly the medical examination system, through rigorous analysis of these extant historical medical documents. Such studies offer valuable insights into the developmental trajectory of the ancient Chinese medical examination system and provide crucial references for contemporary medical education. By conducting in-depth literature research and analysis of Tai Yi Ju Zhu Ke Cheng Wen Ge, this study endeavors to reconstruct the authentic scenario of medical examinations in the Song dynasty, as presented in the document, for the benefit of modern readers and researchers.
文摘This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical content.The participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security weaknesses.This multi faceted exploration led to an indepth evaluation of the model’s performance and security.Notably,the correlation between accuracy,detection rate,and error rate was scrutinized.This analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance variability.The study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications.Applying blockchain to generative AI-created medical content addresses key personal information protection issues.By utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical images.This approach not only enhances security but also enables transparent and tamperproof record-keeping.Additionally,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient privacy.In conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient privacy.The proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content management.Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems,and visual diagnostic systems.
文摘As the healthcare system advances and expands in its services,the challenges of remaining efficient become more important.Emergency medical services(EMS)are vital cornerstones of communities.In many countries,EMS is available for every individual,regardless of their social or insurance status,via a toll-free telephone number.Understanding the risk factors for busy days in EMSs might be helpful for improving the allocation of resources,which is the key to better care for all patients in the prehospital setting.[1]An important factor influencing ambulance call volume could be the interplay of public behavior and weather.
文摘Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of care, a practice which has been found to reduce the time patients spend in hospitals, promote the quality of care and improve healthcare outcomes. Such tools include Medscape, VisualDx, Clinical Key, DynaMed, BMJ Best Practice and UpToDate. However, use of such tools has not yet been fully embraced in low-resource settings such as Uganda. Objective: This paper intends to collate data on the use and uptake of one such tool, UpToDate, which was provided at no cost to five medical schools in Uganda. Methods: Free access to UpToDate was granted through the IP addresses of five medical schools in Uganda in collaboration with Better Evidence at The Global Health Delivery Project at Harvard and Brigham and Women’s Hospital and Wolters Kluwer Health. Following the donation, medical librarians in the respective institutions conducted training sessions and created awareness of the tool. Usage data was aggregated, based on logins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows similar trends in increased usage over the period of August 2022 to August 2023 across the five medical schools. The most common topics viewed, mode of access (using either the computer or the mobile app), total usage by institution, ratio of uses to eligible users by institution and ratio of uses to students by institution are shared. Conclusion: The study revealed that the tool was used by various user categories across the institutions with similar steady improved usage over the year. These results can inform the librarians as they encourage their respective institutions to continue using the tool to support uptake of point-of-care tools in clinical practice.
文摘The integration of traditional Chinese medicine(TCM)into clinical education presents an opportunity to enhance medical training by providing students with a more holistic approach to patient care.This study explores the methods and challenges of integrating TCM theory into clinical internships for medical students at the First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine.A mixed-methods approach was employed,combining quantitative surveys and qualitative interviews with medical students,clinical instructors,and TCM practitioners.The results indicate that while students generally recognize the relevance and benefits of TCM in enhancing diagnostic skills and promoting a holistic understanding of health,several challenges remain.These include the lack of standardized TCM training,limited practical exposure to TCM diagnostic methods,and resistance from some clinical instructors.Despite these challenges,students expressed a strong desire for more structured TCM training and greater support from instructors.Based on the findings,the study recommends the standardization of TCM curricula,enhanced professional development for instructors,and increased collaboration between Western and TCM practitioners.The study concludes that the integration of TCM into medical education can significantly improve student clinical skills and patient care outcomes if appropriately structured and supported.
文摘Since the start of the 21st century,health issues caused by exposure to geological materials,such as heavy metal pollution and/or imbalances in essential elements,have consistently affected about one billion people worldwide.In response to this critical problem,the field of Medical Geology has developed through collaborative efforts from experts in various disciplines,including geologists,environmental scientists,biologists,biochemists,toxicologists,epidemiologists,and public health professionals.
基金funded by theDeanship of Research andGraduate Studies at King Khalid University through Large Research Project under grant number RGP2/417/46.
文摘A phase-aware cross-modal framework is presented that synthesizes UWF_FA from non-invasive UWF_RI for diabetic retinopathy(DR)stratification.A curated cohort of 1198 patients(2915 UWF_RI and 17,854 UWF_FA images)with strict registration quality supports training across three angiographic phases(initial,mid,final).The generator is based on a modified pix2pixHD with an added Gradient Variance Loss to better preserve microvasculature,and is evaluated using MAE,PSNR,SSIM,and MS-SSIM on held-out pairs.Quantitatively,the mid phase achieves the lowestMAE(98.76±42.67),while SSIM remains high across phases.Expert reviewshows substantial agreement(Cohen's κ=0.78–0.82)and Turing-stylemisclassification of 50%–70%of synthetic images as real,indicating strong perceptual realism.For downstream DR stratification,fusing multi-phase synthetic UWF_FA with UWF_RI in a Swin Transformer classifier yields significant gains over a UWF_RI-only baseline,with the full-phase setting(Set D)reaching AUC=0.910 and accuracy=0.829.These results support synthetic UWF_FA as a scalable,non-invasive complement to dye-based angiography that enhances screening accuracy while avoiding injection-related risks.
