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
For more than four hundred years,the modern medical system has primarily inherited the Western cultural tradition and taken shape gradually on the basis of innovating classical Western medicine.In the tradition of anc...For more than four hundred years,the modern medical system has primarily inherited the Western cultural tradition and taken shape gradually on the basis of innovating classical Western medicine.In the tradition of ancient Greek medicine,the Hippocratic school and its successor,the Galenic school,based their medical understanding on the humoral theory and theory of temperaments,holding that there exists an intimate connection between human bodily functions and the mind.展开更多
1 Introduction The history of medicine in modern China has frequently been framed through a lens of“Westernization”,in which traditional Chinese medicine(TCM)is portrayed as grad-ually yielding to modern Western med...1 Introduction The history of medicine in modern China has frequently been framed through a lens of“Westernization”,in which traditional Chinese medicine(TCM)is portrayed as grad-ually yielding to modern Western medicine.Such a binary framework,however,oversimplifies the intricate realities of medical encounters in China.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
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
From the perspective of historical geography,this study conducts an in-depth investigation into the heritage timeline,regional characteristics,and formative factors of Longsha medical school(龙砂医派Longsha medicine)....From the perspective of historical geography,this study conducts an in-depth investigation into the heritage timeline,regional characteristics,and formative factors of Longsha medical school(龙砂医派Longsha medicine).Tracing its origin to the Song Dynasty,Longsha medicine has been handed down through successive generations,with numerous renowned physicians such as Xu Shuwei(许叔微)and Lu Wengui(陆文圭)making remarkable contributions to its development.A combination of favorable geographical location,superior ecological environment,an open cultural atmosphere,abundant local medicinal resources,and the unique physical constitutions of the local population comprehensively shaped the school’s distinct features and fueled its evolution.The core characteristics of Longsha medicine can be summarized as:emphasizing on inheritance and academic exploration,focusing on traditional Chinese medicine(TCM)classical theories and clinical practice,and embodying profound cultural connotations.As a regional and comprehensive TCM academic school,it originated in the Huashi area(historically known as Longsha)in Jiangyin County,Wuxi City,Jiangsu Province,gradually expanded to the areas surrounding Taihu Lake(太湖),and exerted far-reaching influence nationwide.Serving as a core representative of the TCM academic field in the Yangtze River Delta,Longsha medicine boasts a long history,solid cultural foundation,a galaxy of eminent physicians,classic works,distinct academic traits,unique diagnostic and therapeutic techniques,and vigorous dynamic inheritance.Contemporary in-depth research on Longsha medicine not only facilitates the dynamic inheritance of the core TCM theories such as Wu Yun Liu Qi(五运六气the five movements and six qi theory)in Huang Di Nei Jing(《黄帝内经》The Yellow Emperor’s Inner Classic)but also verifies the effectiveness and scientific validity of TCM classical theories in guiding the prevention and treatment of modern diseases.Thus,the study helps to protect regional medical-historical sources,enrich the doctrinal landscape of TCM,sustain distinctive academic techniques,catalyzes contemporary innovation,cultivates characteristic TCM talent,and enhances clinical service effectiveness.展开更多
In the dynamic landscape of modern healthcare and precision medicine,the digital revolution is reshaping medical industries at an unprecedented pace,and traditional Chinese medicine(TCM)is no exception[1-4].The paper...In the dynamic landscape of modern healthcare and precision medicine,the digital revolution is reshaping medical industries at an unprecedented pace,and traditional Chinese medicine(TCM)is no exception[1-4].The paper“From digits towards digitization:the past,present,and future of traditional Chinese medicine”by Academician&TCM National Master Qi WANG(王琦).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
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
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
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
Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for me...Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards,rapid technological evolution,and insufficient localized ethical norms.Objective:To establish a Chinese expert consensus defining core AI competencies and a multi-modal assessment framework for medical students.Methods:A multidisciplinary(including medical education,clinical medicine,medical AI,public health,and medical ethics)expert group(n=32)developed an initial competency list based on the“Knowledge-Skills-Attitude”Medical Competency Model.Two Delphi rounds(100%response rate;consensus threshold:mean≥4.0,CV≤0.25)refined the framework.Core competencies were prioritized via Analytic Hierarchy Process(AHP).The final consensus document was established after multiple expert group meetings.Results:The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into profes-sional knowledge,clinical practice,research,and health management.It comprises a 21-item Competencies of AI Proficiency(CAIP)list across knowledge(eight indicators),skills(seven indicators),and attitude(six indicators)dimensions.Key com-petencies prioritized include understanding AI's role in multidisciplinary knowledge integration(CAIP3),identifying AI output biases(CAIP4),understanding health data governance(CAIP2),maintaining physician-led AI-assisted diagnosis(CAIP16),and identifying AI diagnostic biases(CAIP12).A multi-modal assessment framework is recommended,including paper-based/computerized tests for knowledge,situational judgment tests(SJTs)for attitudes,and objective structured clinical examinations(OSCEs)with a specific“AI Clinical Decision Conflict Scoring Scale”for skills.A multi-stage dynamic assessment system(“Pre-enrollment-Pre-clinical-Post-clinical”)is proposed for longitudinal tracking.Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years,constructing an integrated curriculum covering fundamental principles,advanced large model applications(e.g.,prompt engineering,agent development),and ethical considerations,supported by a"digital twin hospital platform."Conclusion:This consensus provides authoritative,China-specific guidance for defining and assessing medical students'AI literacy,adhering to national policies and regulations.It offers a core action framework for optimizing AI integration into medical education,fostering future healthcare professionals proficient in both AI technology and medical humanism,with a commitment to dynamic updating to adapt to evolving AI advancements.展开更多
Microrobotic systems are emerging as transformative technology for minimally invasive medicine,driven by innovations in actuation mechanisms,advanced fabrication paradigms,and multifunctional system integration.This c...Microrobotic systems are emerging as transformative technology for minimally invasive medicine,driven by innovations in actuation mechanisms,advanced fabrication paradigms,and multifunctional system integration.This comprehensive review analyzes the evolution of microrobotic technologies through three critical dimensions:(1)actuation modalities,including magnetic,optical,acoustic,chemical,and biological actuation,with a focus on the synergistic advantages of hybrid actuation strategies in complex internal physiological environments;(2)Fabrication methods cover technolo-gies such as photolithography,microinjection molding,self-assembly,and 3D printing,emphasizing innovative strategies involving multi-technology integration and collaborative manufacturing of bio/non-bio hybrid materials;(3)Internal phys-iological applications involve disease diagnosis,targeted drug delivery,minimally invasive surgery,tissue engineering,and cell manipulation,highlighting the broad prospects of microrobots in precision medicine.Despite remarkable progress,critical challenges remain,including low actuation efficiency,as seen in acoustic systems,limited biocompatibility,exem-plified by the toxicity of hydrogen peroxide in chemical actuation,delayed clinical translation,and other related challenges that must be addressed to advance the field.展开更多
Automatic and accurate medical image segmentation remains a fundamental task in computer-aided diagnosis and treatment planning.Recent advances in foundation models,such as the medical-focused Segment AnythingModel(Me...Automatic and accurate medical image segmentation remains a fundamental task in computer-aided diagnosis and treatment planning.Recent advances in foundation models,such as the medical-focused Segment AnythingModel(MedSAM),have demonstrated strong performance but face challenges inmanymedical applications due to anatomical complexity and a limited domain-specific prompt.Thiswork introduces amethodology that enhances segmentation robustness and precision by automatically generating multiple informative point prompts,rather than relying on single inputs.The proposed approach randomly samples sets of spatially distributed point prompts based on image features,enabling MedSAM to better capture fine-grained anatomical structures and boundaries.During inference,probability maps are aggregated to reduce local misclassifications without additional model training.Extensive experiments on various computed tomography(CT)and magnetic resonance imaging(MRI)datasets demonstrate improvements in Dice Similarity Coefficient(DSC)and Normalized Surface Dice(NSD)metrics compared to baseline SAM and Scribble Prompt models.A semi-automatic point sampling version based on the ground truth segmentations yielded enhanced results,achieving up to 92.1%DSC and 86.6%NSD,with significant gains in delineating complex organs such as the pancreas,colon,kidney,and brain tumours.The main novelty of our method consists of effectively combining the results of multiple point prompts into the medical segmentation pipeline so that single-point prompt methods are outperformed.Overall,the proposed model offers a straightforward yet effective approach to improve medical image segmentation performance while maintaining computational efficiency.展开更多
In contemporary computer vision,convolutional neural networks(CNNs)and vision transformers(ViTs)represent the two primary architectural paradigms for image recognition.While both approaches have been widely adopted in...In contemporary computer vision,convolutional neural networks(CNNs)and vision transformers(ViTs)represent the two primary architectural paradigms for image recognition.While both approaches have been widely adopted in medical imaging applications,they operate based on fundamentally different computational principles.This report attempts to provide brief application notes on ViTs and CNNs,particularly focusing on scenarios that guide the selection of one architecture over the other in practical medical implementations.Generally,CNNs rely on convolutional kernels,localized receptive fields,and weight sharing,enabling efficient hierarchical feature extraction.These properties contribute to strong performance in detecting spatially constrained patterns such as textures,edges,and anatomical boundaries,while maintaining relatively low computational requirements.ViTs,on the other hand,decompose images into smaller segments referred to as tokens and employ self-attention mechanisms to model relationships across the entire image.This global modeling capability allows ViTs to capture long-range dependencies that may be difficult for convolution-based architectures to learn.However,ViTs typically achieve optimal performance when trained on extremely large datasets or when supported by extensive pretraining,as their reduced inductive bias requires greater data exposure to learn robust representations.This report briefly examines the architectural structure,underlying mathematical foundations,and relative performance characteristics of CNNs and ViTs,drawing upon recent findings from contemporary research.Emphasis is placed on understanding how differences in data availability,computational resources,and task requirements influence model effectiveness across medical imaging domains.Most importantly,the report serves as a concise application guide for practitioners seeking informed implementation decisions between these two influential deep learning frameworks.展开更多
Objective To address the dual challenges of long-tail distribution and feature sparsity in traditional Chinese medicine(TCM)syndrome differentiation within real clinical settings,we propose a data-efficient learning f...Objective To address the dual challenges of long-tail distribution and feature sparsity in traditional Chinese medicine(TCM)syndrome differentiation within real clinical settings,we propose a data-efficient learning framework enhanced by knowledge graphs.Methods We developed Agent-GNN,a three-stage decoupled learning framework,and validated it on the Traditional Chinese Medicine Syndrome Diagnosis(TCM-SD)dataset containing 54152 clinical records across 148 syndrome categories.First,we constructed a comprehensive medical knowledge graph encoding the complete TCM reasoning system.Second,we proposed a Functional Patient Profiling(FPP)method that utilizes large language models(LLMs)combined with Graph Retrieval-Augmented Generation(RAG)to extract structured symptom-etiology-pathogenesis subgraphs from medical records.Third,we employed heterogeneous graph neural networks to learn structured combination patterns explicitly.We compared our method against multiple baselines including BERT,ZY-BERT,ZY-BERT+Know,GAT,and GPT-4 Few-shot,using macro-F1 score as the primary evaluation metric.Additionally,ablation experiments were conducted to validate the contribution of each key component to model performance.Results Agent-GNN achieved an overall macro-F1 score of 72.4%,representing an 8.7 percentage points improvement over ZY-BERT+Know(63.7%),the strongest baseline among traditional methods.For long-tail syndromes with fewer than 10 samples,Agent-GNN reached a macro-F1 score of 58.6%,compared with 39.3%for ZY-BERT+Know and 41.2%for GPT-4 Few-shot,representing relative improvements of 49.2%and 42.2%,respectively.Ablation experiments confirmed that the explicit modeling of etiology-pathogenesis nodes contributed 12.4 percentage points to this enhanced long-tail syndrome performance.Conclusion This study proposes Agent-GNN,a knowledge graph-enhanced framework that effectively addresses the long-tail distribution challenge in TCM syndrome differentiation.By explicitly modeling manifestation-mechanism-essence patterns through structured knowledge graphs,our approach achieves superior performance in data-scarce scenarios while providing interpretable reasoning paths for TCM intelligent diagnosis.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
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
Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their dia...Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their diagnostic reliability.This review presents a structured and comprehensive analysis of advanced histogram equalization(HE)-based techniques for medical image enhancement.Our review methodology encompasses:(1)classical HE approaches and related limitations in medical domains;(2)adaptive schemes like Adaptive Histogram Equalization(AHE)and Contrast Limited Adaptive Histogrma Equalization(CLAHE)and their advance variants;(3)brightnesspreserving schemes like BBHE and MMBEBHE and related algorithms;(4)dynamic and recursive histogram equalization methods incorporating DHE and RMSHE;(5)fuzzy logic-based enhancement methodologies addressing uncertainty and noise in medical images;and(6)hybrid optimization methodologies through the application of metaheuristic algorithms(World Cup Optimization,Particle Swarm Optimization,Genetic Algorithms,along with histogram-based methodologies.)There is also a comparative discussion given based on contrast improvement,image brightness preservation,noise management,and computational efficiency.Such advancements have better capabilities of improving image quality,which is more important for improved diagnosis and image analysis.展开更多
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funded by National Social Science Fund of China,grant number 23XZX006.
文摘For more than four hundred years,the modern medical system has primarily inherited the Western cultural tradition and taken shape gradually on the basis of innovating classical Western medicine.In the tradition of ancient Greek medicine,the Hippocratic school and its successor,the Galenic school,based their medical understanding on the humoral theory and theory of temperaments,holding that there exists an intimate connection between human bodily functions and the mind.
文摘1 Introduction The history of medicine in modern China has frequently been framed through a lens of“Westernization”,in which traditional Chinese medicine(TCM)is portrayed as grad-ually yielding to modern Western medicine.Such a binary framework,however,oversimplifies the intricate realities of medical encounters in China.
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.
基金the grants from the second round of Construction Project of National Traditional Chinese Medicine Academic Schools Inheritance Studio of the State Administration of Traditional Chinese Medicine(No.62,2019)the Seventh Batch of National Projects for Inheriting Academic Experience of Veteran Traditional Chinese Medicine Experts(No.[2022]76)Construction Project of Lu Shu’s National Veteran Traditional Chinese Medicine Expert Inheritance Studio under the National Administration of Traditional Chinese Medicine(No.[2022]75).
文摘From the perspective of historical geography,this study conducts an in-depth investigation into the heritage timeline,regional characteristics,and formative factors of Longsha medical school(龙砂医派Longsha medicine).Tracing its origin to the Song Dynasty,Longsha medicine has been handed down through successive generations,with numerous renowned physicians such as Xu Shuwei(许叔微)and Lu Wengui(陆文圭)making remarkable contributions to its development.A combination of favorable geographical location,superior ecological environment,an open cultural atmosphere,abundant local medicinal resources,and the unique physical constitutions of the local population comprehensively shaped the school’s distinct features and fueled its evolution.The core characteristics of Longsha medicine can be summarized as:emphasizing on inheritance and academic exploration,focusing on traditional Chinese medicine(TCM)classical theories and clinical practice,and embodying profound cultural connotations.As a regional and comprehensive TCM academic school,it originated in the Huashi area(historically known as Longsha)in Jiangyin County,Wuxi City,Jiangsu Province,gradually expanded to the areas surrounding Taihu Lake(太湖),and exerted far-reaching influence nationwide.Serving as a core representative of the TCM academic field in the Yangtze River Delta,Longsha medicine boasts a long history,solid cultural foundation,a galaxy of eminent physicians,classic works,distinct academic traits,unique diagnostic and therapeutic techniques,and vigorous dynamic inheritance.Contemporary in-depth research on Longsha medicine not only facilitates the dynamic inheritance of the core TCM theories such as Wu Yun Liu Qi(五运六气the five movements and six qi theory)in Huang Di Nei Jing(《黄帝内经》The Yellow Emperor’s Inner Classic)but also verifies the effectiveness and scientific validity of TCM classical theories in guiding the prevention and treatment of modern diseases.Thus,the study helps to protect regional medical-historical sources,enrich the doctrinal landscape of TCM,sustain distinctive academic techniques,catalyzes contemporary innovation,cultivates characteristic TCM talent,and enhances clinical service effectiveness.
文摘In the dynamic landscape of modern healthcare and precision medicine,the digital revolution is reshaping medical industries at an unprecedented pace,and traditional Chinese medicine(TCM)is no exception[1-4].The paper“From digits towards digitization:the past,present,and future of traditional Chinese medicine”by Academician&TCM National Master Qi WANG(王琦).
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.
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Science and Technology Innovation 2030 Major Project,Grant/Award Number:2023ZD0508506。
文摘Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards,rapid technological evolution,and insufficient localized ethical norms.Objective:To establish a Chinese expert consensus defining core AI competencies and a multi-modal assessment framework for medical students.Methods:A multidisciplinary(including medical education,clinical medicine,medical AI,public health,and medical ethics)expert group(n=32)developed an initial competency list based on the“Knowledge-Skills-Attitude”Medical Competency Model.Two Delphi rounds(100%response rate;consensus threshold:mean≥4.0,CV≤0.25)refined the framework.Core competencies were prioritized via Analytic Hierarchy Process(AHP).The final consensus document was established after multiple expert group meetings.Results:The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into profes-sional knowledge,clinical practice,research,and health management.It comprises a 21-item Competencies of AI Proficiency(CAIP)list across knowledge(eight indicators),skills(seven indicators),and attitude(six indicators)dimensions.Key com-petencies prioritized include understanding AI's role in multidisciplinary knowledge integration(CAIP3),identifying AI output biases(CAIP4),understanding health data governance(CAIP2),maintaining physician-led AI-assisted diagnosis(CAIP16),and identifying AI diagnostic biases(CAIP12).A multi-modal assessment framework is recommended,including paper-based/computerized tests for knowledge,situational judgment tests(SJTs)for attitudes,and objective structured clinical examinations(OSCEs)with a specific“AI Clinical Decision Conflict Scoring Scale”for skills.A multi-stage dynamic assessment system(“Pre-enrollment-Pre-clinical-Post-clinical”)is proposed for longitudinal tracking.Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years,constructing an integrated curriculum covering fundamental principles,advanced large model applications(e.g.,prompt engineering,agent development),and ethical considerations,supported by a"digital twin hospital platform."Conclusion:This consensus provides authoritative,China-specific guidance for defining and assessing medical students'AI literacy,adhering to national policies and regulations.It offers a core action framework for optimizing AI integration into medical education,fostering future healthcare professionals proficient in both AI technology and medical humanism,with a commitment to dynamic updating to adapt to evolving AI advancements.
基金National Natural Science Foundation of China(Grant No.61903157)the Foundation of State Key Laboratory of Robotics(Grand No.2024-O08).
文摘Microrobotic systems are emerging as transformative technology for minimally invasive medicine,driven by innovations in actuation mechanisms,advanced fabrication paradigms,and multifunctional system integration.This comprehensive review analyzes the evolution of microrobotic technologies through three critical dimensions:(1)actuation modalities,including magnetic,optical,acoustic,chemical,and biological actuation,with a focus on the synergistic advantages of hybrid actuation strategies in complex internal physiological environments;(2)Fabrication methods cover technolo-gies such as photolithography,microinjection molding,self-assembly,and 3D printing,emphasizing innovative strategies involving multi-technology integration and collaborative manufacturing of bio/non-bio hybrid materials;(3)Internal phys-iological applications involve disease diagnosis,targeted drug delivery,minimally invasive surgery,tissue engineering,and cell manipulation,highlighting the broad prospects of microrobots in precision medicine.Despite remarkable progress,critical challenges remain,including low actuation efficiency,as seen in acoustic systems,limited biocompatibility,exem-plified by the toxicity of hydrogen peroxide in chemical actuation,delayed clinical translation,and other related challenges that must be addressed to advance the field.
基金supported by the Autonomous Government of Andalusia(Spain)under project UMA20-FEDERJA-108also by the Ministry of Science and Innovation of Spain,grant number PID2022-136764OA-I00+1 种基金It includes funds fromthe European Regional Development Fund(ERDF),It is also partially supported by the Fundación Unicaja(PUNI-003_2023)the Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND(ATECH-25-02).
文摘Automatic and accurate medical image segmentation remains a fundamental task in computer-aided diagnosis and treatment planning.Recent advances in foundation models,such as the medical-focused Segment AnythingModel(MedSAM),have demonstrated strong performance but face challenges inmanymedical applications due to anatomical complexity and a limited domain-specific prompt.Thiswork introduces amethodology that enhances segmentation robustness and precision by automatically generating multiple informative point prompts,rather than relying on single inputs.The proposed approach randomly samples sets of spatially distributed point prompts based on image features,enabling MedSAM to better capture fine-grained anatomical structures and boundaries.During inference,probability maps are aggregated to reduce local misclassifications without additional model training.Extensive experiments on various computed tomography(CT)and magnetic resonance imaging(MRI)datasets demonstrate improvements in Dice Similarity Coefficient(DSC)and Normalized Surface Dice(NSD)metrics compared to baseline SAM and Scribble Prompt models.A semi-automatic point sampling version based on the ground truth segmentations yielded enhanced results,achieving up to 92.1%DSC and 86.6%NSD,with significant gains in delineating complex organs such as the pancreas,colon,kidney,and brain tumours.The main novelty of our method consists of effectively combining the results of multiple point prompts into the medical segmentation pipeline so that single-point prompt methods are outperformed.Overall,the proposed model offers a straightforward yet effective approach to improve medical image segmentation performance while maintaining computational efficiency.
文摘In contemporary computer vision,convolutional neural networks(CNNs)and vision transformers(ViTs)represent the two primary architectural paradigms for image recognition.While both approaches have been widely adopted in medical imaging applications,they operate based on fundamentally different computational principles.This report attempts to provide brief application notes on ViTs and CNNs,particularly focusing on scenarios that guide the selection of one architecture over the other in practical medical implementations.Generally,CNNs rely on convolutional kernels,localized receptive fields,and weight sharing,enabling efficient hierarchical feature extraction.These properties contribute to strong performance in detecting spatially constrained patterns such as textures,edges,and anatomical boundaries,while maintaining relatively low computational requirements.ViTs,on the other hand,decompose images into smaller segments referred to as tokens and employ self-attention mechanisms to model relationships across the entire image.This global modeling capability allows ViTs to capture long-range dependencies that may be difficult for convolution-based architectures to learn.However,ViTs typically achieve optimal performance when trained on extremely large datasets or when supported by extensive pretraining,as their reduced inductive bias requires greater data exposure to learn robust representations.This report briefly examines the architectural structure,underlying mathematical foundations,and relative performance characteristics of CNNs and ViTs,drawing upon recent findings from contemporary research.Emphasis is placed on understanding how differences in data availability,computational resources,and task requirements influence model effectiveness across medical imaging domains.Most importantly,the report serves as a concise application guide for practitioners seeking informed implementation decisions between these two influential deep learning frameworks.
基金Sichuan TCM Culture Coordinated Development Research Center Project(2023XT131)National Key Science and Technology Project of China(2023ZD0509405)National Natural Science Foundation of China(82174236).
文摘Objective To address the dual challenges of long-tail distribution and feature sparsity in traditional Chinese medicine(TCM)syndrome differentiation within real clinical settings,we propose a data-efficient learning framework enhanced by knowledge graphs.Methods We developed Agent-GNN,a three-stage decoupled learning framework,and validated it on the Traditional Chinese Medicine Syndrome Diagnosis(TCM-SD)dataset containing 54152 clinical records across 148 syndrome categories.First,we constructed a comprehensive medical knowledge graph encoding the complete TCM reasoning system.Second,we proposed a Functional Patient Profiling(FPP)method that utilizes large language models(LLMs)combined with Graph Retrieval-Augmented Generation(RAG)to extract structured symptom-etiology-pathogenesis subgraphs from medical records.Third,we employed heterogeneous graph neural networks to learn structured combination patterns explicitly.We compared our method against multiple baselines including BERT,ZY-BERT,ZY-BERT+Know,GAT,and GPT-4 Few-shot,using macro-F1 score as the primary evaluation metric.Additionally,ablation experiments were conducted to validate the contribution of each key component to model performance.Results Agent-GNN achieved an overall macro-F1 score of 72.4%,representing an 8.7 percentage points improvement over ZY-BERT+Know(63.7%),the strongest baseline among traditional methods.For long-tail syndromes with fewer than 10 samples,Agent-GNN reached a macro-F1 score of 58.6%,compared with 39.3%for ZY-BERT+Know and 41.2%for GPT-4 Few-shot,representing relative improvements of 49.2%and 42.2%,respectively.Ablation experiments confirmed that the explicit modeling of etiology-pathogenesis nodes contributed 12.4 percentage points to this enhanced long-tail syndrome performance.Conclusion This study proposes Agent-GNN,a knowledge graph-enhanced framework that effectively addresses the long-tail distribution challenge in TCM syndrome differentiation.By explicitly modeling manifestation-mechanism-essence patterns through structured knowledge graphs,our approach achieves superior performance in data-scarce scenarios while providing interpretable reasoning paths for TCM intelligent diagnosis.
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.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant No.(IFPDP-261-22).
文摘Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their diagnostic reliability.This review presents a structured and comprehensive analysis of advanced histogram equalization(HE)-based techniques for medical image enhancement.Our review methodology encompasses:(1)classical HE approaches and related limitations in medical domains;(2)adaptive schemes like Adaptive Histogram Equalization(AHE)and Contrast Limited Adaptive Histogrma Equalization(CLAHE)and their advance variants;(3)brightnesspreserving schemes like BBHE and MMBEBHE and related algorithms;(4)dynamic and recursive histogram equalization methods incorporating DHE and RMSHE;(5)fuzzy logic-based enhancement methodologies addressing uncertainty and noise in medical images;and(6)hybrid optimization methodologies through the application of metaheuristic algorithms(World Cup Optimization,Particle Swarm Optimization,Genetic Algorithms,along with histogram-based methodologies.)There is also a comparative discussion given based on contrast improvement,image brightness preservation,noise management,and computational efficiency.Such advancements have better capabilities of improving image quality,which is more important for improved diagnosis and image analysis.
