Although common among community adolescents, self-injuring acts are mainly studied by psychiatrists and psychologists and rarely by social work researchers. The preponderance of medical research in the field has come ...Although common among community adolescents, self-injuring acts are mainly studied by psychiatrists and psychologists and rarely by social work researchers. The preponderance of medical research in the field has come to associate self-injuring acts with mental issues. This view has to a large extent been adopted among professionals as well as among laypeople. When examining adolescents’ unsolicited internet published narratives, this medicalization of self-injuring acts was found to have negative consequences for disclosure and help-seeking, and hence limit the adolescents’ possibilities to get adequate help and support. The main objective of this work is to study adolescents’ views on hampering factors for help-seeking for self-injuring acts and the role of medicalisation for their willingness for disclosure and help-seeking. Disclosure of self-injuring acts within the social network was described as met with demands to seek professional mental help. Seeking professional help was accompanied with fear of being perceived as crazy or diagnosed as mentally ill. Internet websites were described as value free and safe arenas giving opportunity to disclose self-injuring acts without fear of being stigmatized and labelled as mentally ill. An extended involvement of social work researchers and professionals, approaching self-injuring acts not primarily as a sign of mental problems, but as an adolescent way of trying to manage a complicated social context, could enhance finding adequate support systems. It is also necessary that the medical profession contributes to a demedicalization of self-injuring acts.展开更多
As we delve into the intricacies of human disease,millions of people continue to be diagnosed as having what are labelled as pre-conditions or sub-clinical entities and may receive treatments designed to prevent furth...As we delve into the intricacies of human disease,millions of people continue to be diagnosed as having what are labelled as pre-conditions or sub-clinical entities and may receive treatments designed to prevent further progression to clinical disease,but with debatable impact and consequences.Endocrinology is no different,with almost every organ system and associated diseases having subclinical entities.Although the expansion of these“grey”pre-conditions and their treatments come with a better understanding of pathophysiologic processes,they also entail financial costs and drug adverse-effects,and lack true prevention,thus refuting the very foundation of Medicine laid by Hippocrates“Primum non nocere”(Latin),i.e.,do no harm.Subclinical hypothyroidism,prediabetes,osteopenia,and minimal autonomous cortisol excess are some of the endocrine preclinical conditions which do not require active pharmacological management in the vast majority.In fact,progression to clinical disease is seen in only a small minority with reversal to normality in most.Giving drugs also does not lead to true prevention by changing the course of future disease.The goal of the medical fraternity thus as a whole should be to bring this large chunk of humanity out of the hospitals towards leading a healthy lifestyle and away from the label of a medical disease condition.展开更多
The process of medicalization of abortion in Poland began when pregnancy termination procedures were legalized in 1956.The context in which that was possible is important:it happened under the communist rule as part o...The process of medicalization of abortion in Poland began when pregnancy termination procedures were legalized in 1956.The context in which that was possible is important:it happened under the communist rule as part of the Soviet bloc.The main goal of communism was to promote scientific approach to medicine and to eliminate popular folk medicine.The communist rule was also characterized by state feminism,which involved mass employment of women in industry and other occupations.The positive side to the changes was the fact that health care was free of charge.However,the system excluded the care of village healers and abortionists who were replaced by obstetricians-gynecologists,usually men.According to the official propaganda,an abortion that was not performed by a medical professional was dangerous for a woman’s health and could cause her death.Indeed,abortion-related mortality decreased,but the rate of abortion itself did not fall;it gradually increased.This is typical for countries with no free market,including communist ones,where access to contraceptive pills is very limited with abortion being the primary method of birth control.After the fall of communism in Poland,access to abortion was severely restricted;nevertheless,contraceptive pills and morning-after pills are available on prescription from pharmacies.The total fertility rate decreased in comparison to the period of communism and its broad access to abortion.Therefore,I maintain that the process of medicalization of abortion has not ended despite the partial disenfranchisement of women.展开更多
Evaluating Adherence to Safety Standards for Physical Space Design, Equipment, and Patient and Staff Protection in Magnetic Resonance Imaging Centers:A Descriptive Cross-sectional Study Amirreza Sadeghinasab1, Jafar F...Evaluating Adherence to Safety Standards for Physical Space Design, Equipment, and Patient and Staff Protection in Magnetic Resonance Imaging Centers:A Descriptive Cross-sectional Study Amirreza Sadeghinasab1, Jafar Fatahiasl2, Mahmoud Mohammadi-Sadr1, Masoud Heydari Kahkesh3, and Marziyeh Tahmasbi2(1.Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran;2.Department of Radiologic Technology, School of Allied Medical Sciences, Ahvaz, Jundishapur University of Medical Sciences, Ahvaz, Iran;3.Department of Radiology and Radiotherapy, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran)Abstract:Magnetic resonance imaging(MRI) has revolutionized disease diagnosis and treatment.However, the technology poses safety risks, such as exposure to magnetic fields, RF pulses, and cryogens, necessitating strict adherence to safety protocols to protect patients and healthcare workers.展开更多
Fibre Extrusion Technology Ltd(FET)of Leeds,UK reported another successful exhibition at COMPAMED 2025 in Dusseldorf,following closely on the heels of ITMA ASIA in Singapore.This was the second time that FET had exhib...Fibre Extrusion Technology Ltd(FET)of Leeds,UK reported another successful exhibition at COMPAMED 2025 in Dusseldorf,following closely on the heels of ITMA ASIA in Singapore.This was the second time that FET had exhibited at this leading international trade fair for the medical technology supplier sector,a reflection of the company’s growing role in this sector.More than half of FET’s turnover is currently derived from the burgeoning medical market.COMPAMED is aimed at suppliers of a wide range of high-quality medical technology components,services and production equipment for the medical industry.FET’s expanding role in the medical sector is therefore an ideal fit for this trade show.展开更多
The use of AI in medicine began in the 1970s,with early efforts focused on developing expert systems for diagnosis.AI in oncology specifically began with the development of algorithms to analyze large volumes of medic...The use of AI in medicine began in the 1970s,with early efforts focused on developing expert systems for diagnosis.AI in oncology specifically began with the development of algorithms to analyze large volumes of medical data,primarily focusing on cancer diagnosis and early detection through medical imaging.1–3 The evolution of AI in oncology has transitioned from early rule-based systems to the rise and subsequent pivot of high-profile platforms,such as IBM Watson,leading to today's era of deep learning and generative AI for precision medicine.Table 1 below summarizes the selected milestones associated with Oncology Research and Practice.展开更多
Background:Drama therapy as a discipline in clinical and public health areas dates back to movements in the Netherlands,Great Britain and the United States of America in the 1960s.Today,drama therapy comprises a huge ...Background:Drama therapy as a discipline in clinical and public health areas dates back to movements in the Netherlands,Great Britain and the United States of America in the 1960s.Today,drama therapy comprises a huge variety of methods and is applied in numerous medical domains such as psychiatry,pediatrics and neurogeriatrics.Nonetheless,historical and philosophical considerations suggest that at all times dramatic arts have encompassed curative potential and helped to promote mental health.Regarding this perspective,the present article aims to explore the spirit of drama therapy in ancient Chinese and Greek cultures.Methods:Involving cross-cultural psychiatry and medical anthropology,comparative research centred around salutary momentums inhering in drama and dramatic practices in Ancient China and Greece.The entire research process consisted of three phases:(i)screening of ancient Chinese and Greek dramatic rituals and arts to select phenomena and genres with pathological,therapeutic and mental health relevance;(ii)medical ontological processing,in particular from a psychopathological,psychiatric,psychotherapeutic and mental health scientific point of view,to explore and elucidate their curative and preventative features;(iii)transdisciplinary considerations about the evolution of drama therapy,as well as their diverse modes of artistic and medical reasoning.Results:The research process resulted in the identification of five(functional)roots of drama therapy,as well as public health benefits of dramatic arts:(i)dramatic rituals,stage-trance settings and spiritual immersion,(ii)mise-en-scène of divine spheres and alternative worlds,as well as scenic imagination and creative fusion of reality and fantasy,desire and satisfaction,(iii)anthropologic ontology,search of meaning and self-actualisation‘beyond codes and conventions’,(iv)personality traits and differential psychological symbolism,aesthetic self-exploration and auxiliary egos,(v)introjection and role-identity,inventive ways to tackle life problems,‘working through’and catharsis.Conclusion:Interdisciplinary constructivist reasoning suggests that dramatic arts and drama therapy share similar health promoting potential and innate therapeutic power.This calls for further research(i)to explore the entire spectrum of curative factors inhering in dramatic entities,(ii)to explain how drama-based interventions may alleviate mental-health issues alongside culturally sensitive differential diagnostic guidelines,and(iii)to optimise beneficial effects through advanced drama therapeutic settings.The present study suggests that dramatic arts shall also be studied with regard to public health challenges,self-regulation and self-care,mental resilience,well-being and quality of life,and emphatically advocates intensified collaboration between drama theory and drama therapy.展开更多
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
The emergence of Medical Large Language Models has significantly transformed healthcare.Medical Large Language Models(Med-LLMs)serve as transformative tools that enhance clinical practice through applications in decis...The emergence of Medical Large Language Models has significantly transformed healthcare.Medical Large Language Models(Med-LLMs)serve as transformative tools that enhance clinical practice through applications in decision support,documentation,and diagnostics.This evaluation examines the performance of leading Med-LLMs,including GPT-4Med,Med-PaLM,MEDITRON,PubMedGPT,and MedAlpaca,across diverse medical datasets.It provides graphical comparisons of their effectiveness in distinct healthcare domains.The study introduces a domain-specific categorization system that aligns these models with optimal applications in clinical decision-making,documentation,drug discovery,research,patient interaction,and public health.The paper addresses deployment challenges of Medical-LLMs,emphasizing trustworthiness and explainability as essential requirements for healthcare AI.It presents current evaluation techniques that improve model transparency in high-stakes medical contexts and analyzes regulatory frameworks using benchmarking datasets such asMedQA,MedMCQA,PubMedQA,and MIMIC.By identifying ongoing challenges in biasmitigation,reliability,and ethical compliance,thiswork serves as a resource for selecting appropriate Med-LLMs and outlines future directions in the field.This analysis offers a roadmap for developing Med-LLMs that balance technological innovation with the trust and transparency required for clinical integration,a perspective often overlooked in existing literature.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
Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to ...Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to degraded tracking performance,particularly around high-acceleration segments and trajectory inflection points.This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking.Three models—autoregressive(AR),long short-term memory(LSTM),and temporal convolutional network(TCN)—were implemented and evaluated on both synthetic and real datasets.By aligning the prediction horizon with the end-to-end system delay,we demonstrate that prediction-based compensation significantly reduces tracking errors.Among the models,TCN achieved superior robustness and accuracy on complex motion patterns,particularly in multi-step prediction tasks,and exhibited better latency–horizon compatibility.The results suggest that TCN is a promising candidate for real-time latency compensation in teleoperated robotic systems involving dynamic soft-tissue interaction.展开更多
BACKGROUND Gastrointestinal endoscopy technology has significantly improved the diagnostic accuracy and the successful treatment of gastrointestinal diseases.However,a series of ethical issues have emerged,such as exp...BACKGROUND Gastrointestinal endoscopy technology has significantly improved the diagnostic accuracy and the successful treatment of gastrointestinal diseases.However,a series of ethical issues have emerged,such as expanding treatment indications,which affect the fair distribution of medical resources.There is limited research on ethical issues in the field of digestive endoscopy.AIM To investigate the level of ethical awareness among gastrointestinal endoscopy practitioners and analyze the ethical issues involved in gastrointestinal endoscopy technology.METHODS A questionnaire survey was performed to collect relevant data(gender,age,degree of education,professional title,personnel category,the level of understanding medical ethical principles,ethics training and its learning pathways)from gastrointestinal endoscopy practitioners at the Second Hospital of Dalian Medical University and Dalian Friendship Hospital,including licensed physicians and nurses(including trainees and graduate students).RESULTS The majority of gastrointestinal endoscopy practitioners have received training on ethics,but there is still considerable room for improvement in their ethical awareness.Different learning pathways may affect the mastery of ethical principles, and understanding of ethical principles is more easily achieved through hospital ethics institutions.CONCLUSIONTo address the ethical issues in gastrointestinal endoscopy technology, it is necessary to enhance the humanisticeducation of gastrointestinal endoscopy practitioners, incorporate ethical standards into the technology assessmentprocess, and establish a patient-centered diagnostic and treatment model to improve the ethical awareness of practitionersand achieve a balance between technology and ethics.展开更多
Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone t...Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone to errors and variability.Deep learning methods,particularly Vision Transformers(ViT),have shown promise for improving diagnostic accuracy by effectively extracting global features.However,ViT-based approaches face challenges related to computational complexity and limited generalizability.This research proposes the DualSet ViT-PSO-SVM framework,integrating aViTwith dual attentionmechanisms,Particle Swarm Optimization(PSO),and SupportVector Machines(SVM),aiming for efficient and robust lung cancer classification acrossmultiple medical image datasets.The study utilized three publicly available datasets:LIDC-IDRI,LUNA16,and TCIA,encompassing computed tomography(CT)scans and histopathological images.Data preprocessing included normalization,augmentation,and segmentation.Dual attention mechanisms enhanced ViT’s feature extraction capabilities.PSO optimized feature selection,and SVM performed classification.Model performance was evaluated on individual and combined datasets,benchmarked against CNN-based and standard ViT approaches.The DualSet ViT-PSO-SVM significantly outperformed existing methods,achieving superior accuracy rates of 97.85%(LIDC-IDRI),98.32%(LUNA16),and 96.75%(TCIA).Crossdataset evaluations demonstrated strong generalization capabilities and stability across similar imagingmodalities.The proposed framework effectively bridges advanced deep learning techniques with clinical applicability,offering a robust diagnostic tool for lung cancer detection,reducing complexity,and improving diagnostic reliability and interpretability.展开更多
In recent years,the rising incidence of gastrointestinal(GI)cancer has triggered an urgent need for effective early intervention strategies.Traditional endoscopic techniques often cause patient discomfort,and it is di...In recent years,the rising incidence of gastrointestinal(GI)cancer has triggered an urgent need for effective early intervention strategies.Traditional endoscopic techniques often cause patient discomfort,and it is difficult to navigate deep regions of complex organ structures.This work proposes a kind of bio-inspired magnetic soft robot(BMSR)to address these challenges.The design of the BMSRs is inspired by the rolling motion of the golden wheel spider.Two six-degree-of-freedom(6-DOF)robotic arms are used,where one arm is responsible for real-time manipulation of the BMSRs,and the other is dedicated to monitoring their status.Under the actuation of an external rotating magnetic field,the BMSRs can flexibly climb on inclined surfaces at any angle,involving the inverted surface.Through the powerful output force,the BMSRs can overcome the mobility barrier induced by different human organs,including mucus,folds,and height differences of up to 8 cm.Such an exceptional mobility enables the BMSRs to deliver drugs in the targeted complex GI environment.Moreover,in combination with an endoscope,it provides real-time visual feedback for precise navigation.In vitro animal experiments validate the feasibility of BMSRs,paving a way for their usage in minimally invasive GI treatment.This work advances the potential applications of magnetic soft robots in the biomedical field.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
While conventional FISH and IHC methods struggle to decode complex tissue heterogeneity and comprehensive molecular diagnosis due to low-throughput spatial information,spatial omics technologies enable high-throughput...While conventional FISH and IHC methods struggle to decode complex tissue heterogeneity and comprehensive molecular diagnosis due to low-throughput spatial information,spatial omics technologies enable high-throughput molecular mapping across tissue microenvironments.These technologies are emerging as transformative tools in molecular diagnostics and medical research.By integrating histopathological morphology with spatial multi-omics profiling(genome,transcriptome,epigenome,and proteome),spatial omics technologies open an avenue for understanding disease progression,therapeutic resistance mechanisms,and precise diagnosis.It particularly enhances tumor microenvironment analysis by mapping immune cell distributions and functional states,which may greatly facilitate tumor molecular subtyping,prognostic assessment,and prediction of the radiotherapy and chemotherapy efficacy.Despite the substantial advancements in spatial omics,the translation of spatial omics into clinical applications remains challenging due to robustness,efficacy,clinical validation,and cost constraints.In this review,we summarize the current progress and prospects of spatial omics technologies,particularly in medical research and diagnostic applications.展开更多
The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare I...The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.展开更多
Extreme heat events contribute to high mortality[1,2]and overwhelm emergency medical services through increased ambulance calls and overcrowded emergency departments.[3]Because morbidity and mortality are directly rel...Extreme heat events contribute to high mortality[1,2]and overwhelm emergency medical services through increased ambulance calls and overcrowded emergency departments.[3]Because morbidity and mortality are directly related to both the degree and duration of hyperthermia,timely recognition and management of heat exhaustion and heat stroke are critical for preventing death and reducing healthcare burdens.展开更多
Metaheuristic algorithms,renowned for strong global search capabilities,are effective tools for solving complex optimization problems and show substantial potential in e-Health applications.This review provides a syst...Metaheuristic algorithms,renowned for strong global search capabilities,are effective tools for solving complex optimization problems and show substantial potential in e-Health applications.This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health.We selected representative algorithms published between 2019 and 2024,and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts.CThe Harris Hawks Optimizer(HHO)demonstrated the highest early citation impact.The study also examined applications in disease prediction models,clinical decision support,and intelligent health monitoring.Notably,the Chaotic Salp Swarm Algorithm(CSSA)achieved 99.69% accuracy in detecting Novel Coronavirus Pneumonia.Future research should progress in three directions:improving theoretical reliability and performance predictability in medical contexts;designing more adaptive and deployable mechanisms for real-world systems;and integrating ethical,privacy,and technological considerations to enable precision medicine,digital twins,and intelligent medical devices.展开更多
文摘Although common among community adolescents, self-injuring acts are mainly studied by psychiatrists and psychologists and rarely by social work researchers. The preponderance of medical research in the field has come to associate self-injuring acts with mental issues. This view has to a large extent been adopted among professionals as well as among laypeople. When examining adolescents’ unsolicited internet published narratives, this medicalization of self-injuring acts was found to have negative consequences for disclosure and help-seeking, and hence limit the adolescents’ possibilities to get adequate help and support. The main objective of this work is to study adolescents’ views on hampering factors for help-seeking for self-injuring acts and the role of medicalisation for their willingness for disclosure and help-seeking. Disclosure of self-injuring acts within the social network was described as met with demands to seek professional mental help. Seeking professional help was accompanied with fear of being perceived as crazy or diagnosed as mentally ill. Internet websites were described as value free and safe arenas giving opportunity to disclose self-injuring acts without fear of being stigmatized and labelled as mentally ill. An extended involvement of social work researchers and professionals, approaching self-injuring acts not primarily as a sign of mental problems, but as an adolescent way of trying to manage a complicated social context, could enhance finding adequate support systems. It is also necessary that the medical profession contributes to a demedicalization of self-injuring acts.
文摘As we delve into the intricacies of human disease,millions of people continue to be diagnosed as having what are labelled as pre-conditions or sub-clinical entities and may receive treatments designed to prevent further progression to clinical disease,but with debatable impact and consequences.Endocrinology is no different,with almost every organ system and associated diseases having subclinical entities.Although the expansion of these“grey”pre-conditions and their treatments come with a better understanding of pathophysiologic processes,they also entail financial costs and drug adverse-effects,and lack true prevention,thus refuting the very foundation of Medicine laid by Hippocrates“Primum non nocere”(Latin),i.e.,do no harm.Subclinical hypothyroidism,prediabetes,osteopenia,and minimal autonomous cortisol excess are some of the endocrine preclinical conditions which do not require active pharmacological management in the vast majority.In fact,progression to clinical disease is seen in only a small minority with reversal to normality in most.Giving drugs also does not lead to true prevention by changing the course of future disease.The goal of the medical fraternity thus as a whole should be to bring this large chunk of humanity out of the hospitals towards leading a healthy lifestyle and away from the label of a medical disease condition.
文摘The process of medicalization of abortion in Poland began when pregnancy termination procedures were legalized in 1956.The context in which that was possible is important:it happened under the communist rule as part of the Soviet bloc.The main goal of communism was to promote scientific approach to medicine and to eliminate popular folk medicine.The communist rule was also characterized by state feminism,which involved mass employment of women in industry and other occupations.The positive side to the changes was the fact that health care was free of charge.However,the system excluded the care of village healers and abortionists who were replaced by obstetricians-gynecologists,usually men.According to the official propaganda,an abortion that was not performed by a medical professional was dangerous for a woman’s health and could cause her death.Indeed,abortion-related mortality decreased,but the rate of abortion itself did not fall;it gradually increased.This is typical for countries with no free market,including communist ones,where access to contraceptive pills is very limited with abortion being the primary method of birth control.After the fall of communism in Poland,access to abortion was severely restricted;nevertheless,contraceptive pills and morning-after pills are available on prescription from pharmacies.The total fertility rate decreased in comparison to the period of communism and its broad access to abortion.Therefore,I maintain that the process of medicalization of abortion has not ended despite the partial disenfranchisement of women.
文摘Evaluating Adherence to Safety Standards for Physical Space Design, Equipment, and Patient and Staff Protection in Magnetic Resonance Imaging Centers:A Descriptive Cross-sectional Study Amirreza Sadeghinasab1, Jafar Fatahiasl2, Mahmoud Mohammadi-Sadr1, Masoud Heydari Kahkesh3, and Marziyeh Tahmasbi2(1.Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran;2.Department of Radiologic Technology, School of Allied Medical Sciences, Ahvaz, Jundishapur University of Medical Sciences, Ahvaz, Iran;3.Department of Radiology and Radiotherapy, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran)Abstract:Magnetic resonance imaging(MRI) has revolutionized disease diagnosis and treatment.However, the technology poses safety risks, such as exposure to magnetic fields, RF pulses, and cryogens, necessitating strict adherence to safety protocols to protect patients and healthcare workers.
文摘Fibre Extrusion Technology Ltd(FET)of Leeds,UK reported another successful exhibition at COMPAMED 2025 in Dusseldorf,following closely on the heels of ITMA ASIA in Singapore.This was the second time that FET had exhibited at this leading international trade fair for the medical technology supplier sector,a reflection of the company’s growing role in this sector.More than half of FET’s turnover is currently derived from the burgeoning medical market.COMPAMED is aimed at suppliers of a wide range of high-quality medical technology components,services and production equipment for the medical industry.FET’s expanding role in the medical sector is therefore an ideal fit for this trade show.
文摘The use of AI in medicine began in the 1970s,with early efforts focused on developing expert systems for diagnosis.AI in oncology specifically began with the development of algorithms to analyze large volumes of medical data,primarily focusing on cancer diagnosis and early detection through medical imaging.1–3 The evolution of AI in oncology has transitioned from early rule-based systems to the rise and subsequent pivot of high-profile platforms,such as IBM Watson,leading to today's era of deep learning and generative AI for precision medicine.Table 1 below summarizes the selected milestones associated with Oncology Research and Practice.
文摘Background:Drama therapy as a discipline in clinical and public health areas dates back to movements in the Netherlands,Great Britain and the United States of America in the 1960s.Today,drama therapy comprises a huge variety of methods and is applied in numerous medical domains such as psychiatry,pediatrics and neurogeriatrics.Nonetheless,historical and philosophical considerations suggest that at all times dramatic arts have encompassed curative potential and helped to promote mental health.Regarding this perspective,the present article aims to explore the spirit of drama therapy in ancient Chinese and Greek cultures.Methods:Involving cross-cultural psychiatry and medical anthropology,comparative research centred around salutary momentums inhering in drama and dramatic practices in Ancient China and Greece.The entire research process consisted of three phases:(i)screening of ancient Chinese and Greek dramatic rituals and arts to select phenomena and genres with pathological,therapeutic and mental health relevance;(ii)medical ontological processing,in particular from a psychopathological,psychiatric,psychotherapeutic and mental health scientific point of view,to explore and elucidate their curative and preventative features;(iii)transdisciplinary considerations about the evolution of drama therapy,as well as their diverse modes of artistic and medical reasoning.Results:The research process resulted in the identification of five(functional)roots of drama therapy,as well as public health benefits of dramatic arts:(i)dramatic rituals,stage-trance settings and spiritual immersion,(ii)mise-en-scène of divine spheres and alternative worlds,as well as scenic imagination and creative fusion of reality and fantasy,desire and satisfaction,(iii)anthropologic ontology,search of meaning and self-actualisation‘beyond codes and conventions’,(iv)personality traits and differential psychological symbolism,aesthetic self-exploration and auxiliary egos,(v)introjection and role-identity,inventive ways to tackle life problems,‘working through’and catharsis.Conclusion:Interdisciplinary constructivist reasoning suggests that dramatic arts and drama therapy share similar health promoting potential and innate therapeutic power.This calls for further research(i)to explore the entire spectrum of curative factors inhering in dramatic entities,(ii)to explain how drama-based interventions may alleviate mental-health issues alongside culturally sensitive differential diagnostic guidelines,and(iii)to optimise beneficial effects through advanced drama therapeutic settings.The present study suggests that dramatic arts shall also be studied with regard to public health challenges,self-regulation and self-care,mental resilience,well-being and quality of life,and emphatically advocates intensified collaboration between drama theory and drama therapy.
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.
文摘The emergence of Medical Large Language Models has significantly transformed healthcare.Medical Large Language Models(Med-LLMs)serve as transformative tools that enhance clinical practice through applications in decision support,documentation,and diagnostics.This evaluation examines the performance of leading Med-LLMs,including GPT-4Med,Med-PaLM,MEDITRON,PubMedGPT,and MedAlpaca,across diverse medical datasets.It provides graphical comparisons of their effectiveness in distinct healthcare domains.The study introduces a domain-specific categorization system that aligns these models with optimal applications in clinical decision-making,documentation,drug discovery,research,patient interaction,and public health.The paper addresses deployment challenges of Medical-LLMs,emphasizing trustworthiness and explainability as essential requirements for healthcare AI.It presents current evaluation techniques that improve model transparency in high-stakes medical contexts and analyzes regulatory frameworks using benchmarking datasets such asMedQA,MedMCQA,PubMedQA,and MIMIC.By identifying ongoing challenges in biasmitigation,reliability,and ethical compliance,thiswork serves as a resource for selecting appropriate Med-LLMs and outlines future directions in the field.This analysis offers a roadmap for developing Med-LLMs that balance technological innovation with the trust and transparency required for clinical integration,a perspective often overlooked in existing literature.
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.
基金Support by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004]Guangzhou Huashang University[2024HSZD01,HS2023JYSZH01].
文摘Soft-tissue motion introduces significant challenges in robotic teleoperation,especially in medical scenarios where precise target tracking is critical.Latency across sensing,computation,and actuation chains leads to degraded tracking performance,particularly around high-acceleration segments and trajectory inflection points.This study investigates machine learning-based predictive compensation for latency mitigation in soft-tissue tracking.Three models—autoregressive(AR),long short-term memory(LSTM),and temporal convolutional network(TCN)—were implemented and evaluated on both synthetic and real datasets.By aligning the prediction horizon with the end-to-end system delay,we demonstrate that prediction-based compensation significantly reduces tracking errors.Among the models,TCN achieved superior robustness and accuracy on complex motion patterns,particularly in multi-step prediction tasks,and exhibited better latency–horizon compatibility.The results suggest that TCN is a promising candidate for real-time latency compensation in teleoperated robotic systems involving dynamic soft-tissue interaction.
文摘BACKGROUND Gastrointestinal endoscopy technology has significantly improved the diagnostic accuracy and the successful treatment of gastrointestinal diseases.However,a series of ethical issues have emerged,such as expanding treatment indications,which affect the fair distribution of medical resources.There is limited research on ethical issues in the field of digestive endoscopy.AIM To investigate the level of ethical awareness among gastrointestinal endoscopy practitioners and analyze the ethical issues involved in gastrointestinal endoscopy technology.METHODS A questionnaire survey was performed to collect relevant data(gender,age,degree of education,professional title,personnel category,the level of understanding medical ethical principles,ethics training and its learning pathways)from gastrointestinal endoscopy practitioners at the Second Hospital of Dalian Medical University and Dalian Friendship Hospital,including licensed physicians and nurses(including trainees and graduate students).RESULTS The majority of gastrointestinal endoscopy practitioners have received training on ethics,but there is still considerable room for improvement in their ethical awareness.Different learning pathways may affect the mastery of ethical principles, and understanding of ethical principles is more easily achieved through hospital ethics institutions.CONCLUSIONTo address the ethical issues in gastrointestinal endoscopy technology, it is necessary to enhance the humanisticeducation of gastrointestinal endoscopy practitioners, incorporate ethical standards into the technology assessmentprocess, and establish a patient-centered diagnostic and treatment model to improve the ethical awareness of practitionersand achieve a balance between technology and ethics.
文摘Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone to errors and variability.Deep learning methods,particularly Vision Transformers(ViT),have shown promise for improving diagnostic accuracy by effectively extracting global features.However,ViT-based approaches face challenges related to computational complexity and limited generalizability.This research proposes the DualSet ViT-PSO-SVM framework,integrating aViTwith dual attentionmechanisms,Particle Swarm Optimization(PSO),and SupportVector Machines(SVM),aiming for efficient and robust lung cancer classification acrossmultiple medical image datasets.The study utilized three publicly available datasets:LIDC-IDRI,LUNA16,and TCIA,encompassing computed tomography(CT)scans and histopathological images.Data preprocessing included normalization,augmentation,and segmentation.Dual attention mechanisms enhanced ViT’s feature extraction capabilities.PSO optimized feature selection,and SVM performed classification.Model performance was evaluated on individual and combined datasets,benchmarked against CNN-based and standard ViT approaches.The DualSet ViT-PSO-SVM significantly outperformed existing methods,achieving superior accuracy rates of 97.85%(LIDC-IDRI),98.32%(LUNA16),and 96.75%(TCIA).Crossdataset evaluations demonstrated strong generalization capabilities and stability across similar imagingmodalities.The proposed framework effectively bridges advanced deep learning techniques with clinical applicability,offering a robust diagnostic tool for lung cancer detection,reducing complexity,and improving diagnostic reliability and interpretability.
基金supported in part by the National Natural Science Foundation of China under grant 52175556the Macao Science and Technology Development Fund under grant 0004/2022/AKP,0102/2022/A2,and 0078/2023/RIB3+1 种基金the Research Committee of the University of Macao under grants MYRG2022-00068-FST and MYRG-CRG202200004-FST-ICIthe Guangdong Basic and Applied Basic Research Foundation under grant 2023A1515011178。
文摘In recent years,the rising incidence of gastrointestinal(GI)cancer has triggered an urgent need for effective early intervention strategies.Traditional endoscopic techniques often cause patient discomfort,and it is difficult to navigate deep regions of complex organ structures.This work proposes a kind of bio-inspired magnetic soft robot(BMSR)to address these challenges.The design of the BMSRs is inspired by the rolling motion of the golden wheel spider.Two six-degree-of-freedom(6-DOF)robotic arms are used,where one arm is responsible for real-time manipulation of the BMSRs,and the other is dedicated to monitoring their status.Under the actuation of an external rotating magnetic field,the BMSRs can flexibly climb on inclined surfaces at any angle,involving the inverted surface.Through the powerful output force,the BMSRs can overcome the mobility barrier induced by different human organs,including mucus,folds,and height differences of up to 8 cm.Such an exceptional mobility enables the BMSRs to deliver drugs in the targeted complex GI environment.Moreover,in combination with an endoscope,it provides real-time visual feedback for precise navigation.In vitro animal experiments validate the feasibility of BMSRs,paving a way for their usage in minimally invasive GI treatment.This work advances the potential applications of magnetic soft robots in the biomedical field.
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.
基金supported by the National Natural Science Foundation of China(32171022,32221005,and 32401246).
文摘While conventional FISH and IHC methods struggle to decode complex tissue heterogeneity and comprehensive molecular diagnosis due to low-throughput spatial information,spatial omics technologies enable high-throughput molecular mapping across tissue microenvironments.These technologies are emerging as transformative tools in molecular diagnostics and medical research.By integrating histopathological morphology with spatial multi-omics profiling(genome,transcriptome,epigenome,and proteome),spatial omics technologies open an avenue for understanding disease progression,therapeutic resistance mechanisms,and precise diagnosis.It particularly enhances tumor microenvironment analysis by mapping immune cell distributions and functional states,which may greatly facilitate tumor molecular subtyping,prognostic assessment,and prediction of the radiotherapy and chemotherapy efficacy.Despite the substantial advancements in spatial omics,the translation of spatial omics into clinical applications remains challenging due to robustness,efficacy,clinical validation,and cost constraints.In this review,we summarize the current progress and prospects of spatial omics technologies,particularly in medical research and diagnostic applications.
文摘The integration of machine learning(ML)technology with Internet of Things(IoT)systems produces essential changes in healthcare operations.Healthcare personnel can track patients around the clock thanks to healthcare IoT(H-IoT)technology,which also provides proactive statistical findings and precise medical diagnoses that enhance healthcare performance.This study examines how ML might support IoT-based health care systems,namely in the areas of prognostic systems,disease detection,patient tracking,and healthcare operations control.The study looks at the benefits and drawbacks of several machine learning techniques for H-IoT applications.It also examines the fundamental problems,such as data security and cyberthreats,as well as the high processing demands that these systems face.Alongside this,the essay discusses the advantages of all the technologies,including machine learning,deep learning,and the Internet of Things,as well as the significant difficulties and problems that arise when integrating the technology into healthcare forecasts.
文摘Extreme heat events contribute to high mortality[1,2]and overwhelm emergency medical services through increased ambulance calls and overcrowded emergency departments.[3]Because morbidity and mortality are directly related to both the degree and duration of hyperthermia,timely recognition and management of heat exhaustion and heat stroke are critical for preventing death and reducing healthcare burdens.
基金Supported by National Natural Science Foundation of China(Grant No.62506054)Natural Science Foundation of Chongqing,China(Grant Nos.CSTB2022NSCQ-MSX1571,CSTB2024NSCQ-MSX1118)+2 种基金the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant Nos.KJQN202400841,KJZD-M202500804)The National Natural Science Foundation of China(Grant No.61976030)Chongqing Technology and Business University High-level Talent Research Initiation Project(Grant No.2256004).
文摘Metaheuristic algorithms,renowned for strong global search capabilities,are effective tools for solving complex optimization problems and show substantial potential in e-Health applications.This review provides a systematic overview of recent advancements in metaheuristic algorithms and highlights their applications in e-Health.We selected representative algorithms published between 2019 and 2024,and quantified their influence using an entropy-weighted method based on journal impact factors and citation counts.CThe Harris Hawks Optimizer(HHO)demonstrated the highest early citation impact.The study also examined applications in disease prediction models,clinical decision support,and intelligent health monitoring.Notably,the Chaotic Salp Swarm Algorithm(CSSA)achieved 99.69% accuracy in detecting Novel Coronavirus Pneumonia.Future research should progress in three directions:improving theoretical reliability and performance predictability in medical contexts;designing more adaptive and deployable mechanisms for real-world systems;and integrating ethical,privacy,and technological considerations to enable precision medicine,digital twins,and intelligent medical devices.
