Artificial Intelligence in Medical Imaging in China (Aug 3, 2024)_(9819984408)_(Springer).rar

Foreword
Preface
Contents
About the Editor
1: Basic Medical Imaging Artificial Intelligence Concepts and Terminologies
	1.1	 Significance of Terminology Standardization in Medical Imaging Artificial Intelligence
	1.2	 Common Terminologies in Medical Imaging AI
	1.3	 Current Situation and Future Outlook
	Appendix: Medical Imaging AI Terminology
		A
		B
		C
		D
		E
		F
		G
		H
		I
		J
		K
		L
		M
		N
		O
		P
		R
		S
		T
		U
		V
		W
2: Big Data in Artificial Intelligence Medical Imaging
	2.1	 Data Requirements for the Development of Artificial Intelligence in Medical Imaging
	2.2	 Data Quality and Standards
		2.2.1	 Overview of Related Standards in Health Care
		2.2.2	 General Requirement for Datasets of AIMD
		2.2.3	 Quality Requirements for Medical Image and Text Data
	2.3	 Data Annotation and Curation
		2.3.1	 General Requirements for Data Annotation
		2.3.2	 Case Study
			2.3.2.1	 Annotation of CT Pulmonary Nodules
			2.3.2.2	 CT and MRI Annotation of Liver Focal Lesions
			2.3.2.3	 The Annotation of Colorectal Cancer on CT and MR Images
			2.3.2.4	 Annotation of Central Nervous System Cancer on MR Images
3: Current Status of Medical Imaging Databases
	3.1	 Current Status and Challenges in the Construction of Medical Imaging Databases
		3.1.1	 The Importance of Medical Imaging Database Construction.
			3.1.1.1	 The Demands of National Strategies
			3.1.1.2	 The Demands of Clinical Development
			3.1.1.3	 The Demands of Clinical Teaching
			3.1.1.4	 The Demands of Research and Development
			3.1.1.5	 The Demands of Regulation
		3.1.2	 Current Status of Medical Imaging Database Construction in China
			3.1.2.1	 Large Amount of Nonstandardized Image Data
			3.1.2.2	 Lack of Large, Diverse, Standardized, Labelled Databases
			3.1.2.3	 Lack of Talent in Multidisciplinary Data Management
			3.1.2.4	 Serious Problems of Data Silos
			3.1.2.5	 Ethics and Regulations Related to Improving Medical Imaging Data
		3.1.3	 Challenges in Medical Imaging Database Construction in China
			3.1.3.1	 High-Technology Thresholds
			3.1.3.2	 Enormous Investment of Resources
			3.1.3.3	 Dynamic and Changing Requirements
			3.1.3.4	 Enduring but Rewarding Work with a Long Construction Cycle
		3.1.4	 Future and Expectations
	3.2	 Construction of Medical Imaging Databases
		3.2.1	 Key Elements of Database Construction
		3.2.2	 Information Model of a Database
		3.2.3	 Data Integration and Standardization
			3.2.3.1	 Integration of Data
			3.2.3.2	 Cleaning and Processing of Clinical Data
			3.2.3.3	 Standardization of Image Data
		3.2.4	 Data Privacy Protection
			3.2.4.1	 The Privacy Protection of Clinical Data
			3.2.4.2	 Privacy Protection of Imaging Data
			3.2.4.3	 Related Technology Trends
		3.2.5	 Methodology of Database Construction
			3.2.5.1	 Preparing for Medical Image Database Construction
				Organization and Top-Level Design
				Development of Image Database Construction Standards and Achieving Expert Consensus in Image Data Annotation
			3.2.5.2	 Construction of a Medical Image Database
				Acquisition and Cleaning of Medical Image Data
				Annotation of Medical Image Data
				Formation of a Medical Image Database
		3.2.6	 Quality Evaluation of a Database
	3.3	 Fourteen Medical Imaging Database Projects in China
		3.3.1	 Standardized DXA and QCT Reference Databases for Osteoporosis
		3.3.2	 Standardized CT Database of Chronic Obstructive Pulmonary Disease
		3.3.3	 Nuclear Medicine Multimodal Imaging Database for Ischaemic Heart Disease
		3.3.4	 Construction of a Chinese Diffuse Glioma Clinical Imaging Pathology Database
		3.3.5	 Construction of a Gastrointestinal Imaging Database
		3.3.6	 Establishment of a Domestically Based Multicentre, Multimodal, Multitask Breast Imaging Database
		3.3.7	 Outline of the Construction of a Standardized Image Database for Chronic Liver Disease and Primary Liver Cancer
		3.3.8	 Construction of a CT and MRI Standardized Database for Parotid Gland Tumours
			3.3.8.1	 Background
			3.3.8.2	 Project Approval Information
			3.3.8.3	 Information Included in the Database
			3.3.8.4	 The Goal and Vision of the Project
		3.3.9	 Construction of a Multimodal MR Image Database of Orbital Masses
		3.3.10	 Standardized of a Cardiovascular and Cerebrovascular Imaging Database
			3.3.10.1	 Clinical Background of the Project
			3.3.10.2	 Project Approval Information
			3.3.10.3	 The Information Included in the Database (Both Imaging and Clinical Data)
			3.3.10.4	 The Goal and Vision of the Project
		3.3.11	 A Chinese Image Database to Evaluate the Response of Therapeutic Practice in Lung Cancer Patients
		3.3.12	 Emergency Imaging Database
		3.3.13	 Standardized CTA Image Database of Aortic Dissection
		3.3.14	 Standardized Multimodal Imaging Database of Pulmonary Nodules
	3.4	 Introduction of Medical Imaging Databases in China and Overseas
		3.4.1	 International Databases
			3.4.1.1	 UK Biobank
			3.4.1.2	 Adolescent Brain Cognitive Development (ABCD)
			3.4.1.3	 Enhancing NeuroImaging Genetics Through Meta-Analysis (ENIGMA) Consortium
			3.4.1.4	 The Cancer Imaging Archive (TCIA)
			3.4.1.5	 Lung Image Database Consortium (LIDC)
		3.4.2	 Databases in China
			3.4.2.1	 Zhangjiang International Brain Bank
			3.4.2.2	 Chinese Imaging Genetics Database
			3.4.2.3	 China CTB3S Database
			3.4.2.4	 China MIND-CHINA Database
			3.4.2.5	 Chinese C-STRAT Database
		3.4.3	 Current Status and Future Perspective
	References
4: Radiomics and Multiomics Research
	4.1	 Radiomics Research Progress
		4.1.1	 Radiomics Algorithm
		4.1.2	 The Application of Radiomics in Disease Diagnosis and Treatment
			4.1.2.1	 The Progression of Radiomics in Gastric Cancer
			4.1.2.2	 The Progression of Radiomics in Nasopharyngeal Carcinoma
			4.1.2.3	 The Progression of Radiomics in Liver Cancer
			4.1.2.4	 The Application Progress of Radiomics in Lung Cancer
			4.1.2.5	 The Progression of Radiomics in Other Common Tumours
			4.1.2.6	 The Progress of Radiomics in Other Fields
	4.2	 Research Progress in Radiopathomics
		4.2.1	 Algorithms for Combined Analysis of Radiology and Pathology
			4.2.1.1	 Quantitative Analysis and Feature Extraction of Digital Pathology Images
			4.2.1.2	 Algorithms for Combined Analysis of Radiology and Pathology
		4.2.2	 Application of the Combined Analysis of Radiology and Pathology in Disease Diagnosis and Treatment
		4.2.3	 Current Status of Radiopathomics Research at Home and Abroad
	4.3	 Research Progress of Radiogenomics
		4.3.1	 Radiogenomics Research in Glioma
		4.3.2	 Radiogenomics Research in Lung Cancer
	4.4	 Current Challenges and Prospects
		4.4.1	 Artificial Intelligence Model Robustness
		4.4.2	 Medical Data Sharing
		4.4.3	 Medical Interpretability of AI Models
		4.4.4	 Commercialization of Medical AI Software
		4.4.5	 Advanced Analysis Methods and Applications
	References
5: Artificial Intelligence Algorithm Advances in Medical Imaging and Image Analysis
	5.1	 Traits of Medical Imaging
	5.2	 Medical Image Analysis Algorithms
		5.2.1	 Overview of Algorithm Trends
		5.2.2	 Annotation-Efficient Methods
			5.2.2.1	 Transfer Learning or Model Pretraining
			5.2.2.2	 Self-Supervised or Unsupervised Learning
			5.2.2.3	 Semisupervised Learning or Multilabel Learning
		5.2.3	 Domain Adaptation
		5.2.4	 Generative Adversarial Networks
		5.2.5	 Advances in Network Architectures
			5.2.5.1	 Deep Network Architecture
			5.2.5.2	 U-Net for Image Segmentation
			5.2.5.3	 Neural Architecture Search
			5.2.5.4	 Attention-Based Transformer Architecture
			5.2.5.5	 Lightweight Models
		5.2.6	 Fusion of Deep Learning and Knowledge Modelling
			5.2.6.1	 Fusing Anatomical Knowledge and Learning
			5.2.6.2	 Fusing Imaging Knowledge and Learning
			5.2.6.3	 Fusing Domain Knowledge and Learning
			5.2.6.4	 Fusing Knowledge Graph and Learning
		5.2.7	 Deep Universal Representation Learning
		5.2.8	 Federated Learning
		5.2.9	 Uncertainty and Interpretability
	5.3	 Intelligent Algorithms in Medical Imaging
		5.3.1	 CT Imaging
			5.3.1.1	 Intelligent CT Scanning
			5.3.1.2	 Intelligent Low-Dose CT Imaging
			5.3.1.3	 Intelligent CT Artefact Reduction
			5.3.1.4	 Intelligent Spectral CT Imaging
		5.3.2	 MR Imaging
			5.3.2.1	 Modelling-Based MR Imaging
			5.3.2.2	 Deep Learning-Based MR Imaging
			5.3.2.3	 Model-Driven Deep Learning Approach
			5.3.2.4	 Other Directions of Intelligent MR Imaging
	5.4	 Future Outlook
	References
6: Application of Artificial Intelligence in Optimizing Medical Imaging Workflows
	6.1	 AI Improves the Process of Imaging Examination
		6.1.1	 Background Overview
		6.1.2	 Actual Cases
			6.1.2.1	 Intelligent Appointment Scheduling for Medical Imaging Examinations
			6.1.2.2	 Noncontact Medical Imaging Examination Empowered by AI
			6.1.2.3	 Improving Imaging Scan Protocols Through Deep Learning-Based MRI Image Reconstruction Technology
		6.1.3	 Domestic and International Comparison and Outlook
	6.2	 Automatic Outlining of Radiotherapy Target Areas and Involved Organs
		6.2.1	 Background Overview
		6.2.2	 Actual Cases
		6.2.3	 Domestic and International Comparison and Outlook
	6.3	 Image Quality Optimization
		6.3.1	 Background Overview
		6.3.2	 Actual Cases
			6.3.2.1	 Low-Dose CT Image Quality Improvement
			6.3.2.2	 Image Artefact Suppression
			6.3.2.3	 Image Quality Enhancement
		6.3.3	 Domestic and International Comparison and Outlook
	6.4	 Structured Reports
		6.4.1	 Background Overview
			6.4.1.1	 The Development Status of Structured Reports
			6.4.1.2	 Current Status of the Use of Structured Reports in China
			6.4.1.3	 Advantages and Disadvantages of Structured Reports
		6.4.2	 Actual Cases
			6.4.2.1	 Prestructuring and Poststructuring
			6.4.2.2	 Fully Structured and Semistructured
			6.4.2.3	 Disease-Specific Structured and Universal Structured
			6.4.2.4	 Intelligent Reporting
		6.4.3	 Domestic and International Comparison and Outlook
	6.5	 Medical Imaging Quality Control and Management
		6.5.1	 Background Overview
		6.5.2	 Actual Cases
		6.5.3	 Domestic and International Comparison and Outlook
	6.6	 Clinical Application of Contrast AI
		6.6.1	 Background Overview
		6.6.2	 Actual Cases
			6.6.2.1	 Intelligent Management of Contrast Agents
			6.6.2.2	 Contrast Agent Dose Management
		6.6.3	 Domestic and International Comparison and Outlook
	6.7	 Internet-Based Applications and Grassroots Services
		6.7.1	 Background Overview
		6.7.2	 Actual Cases
			6.7.2.1	 AI and Assisted Diagnosis
			6.7.2.2	 Internet Regional Quality Control
			6.7.2.3	 Training for Grassroots Doctors
			6.7.2.4	 Medical Big Data Analysis
		6.7.3	 Domestic and International Comparison and Outlook
	References
7: Application of Artificial Intelligence in Central Nervous System Imaging
	7.1	 Overview
	7.2	 Typical Case
		7.2.1	 AI and Stroke
		7.2.2	 AI and Neurodegenerative Diseases
		7.2.3	 AI and Brain Tumours
		7.2.4	 AI-Assisted Diagnosis Aid System for Multiple Brain Disorders
		7.2.5	 AI-Based Medical Image Synthesis in CNS Imaging
	7.3	 Artificial Intelligence in Central Nervous System Imaging: A Global Perspective
	References
8: Application of Artificial Intelligence in Head and Neck Imaging
	8.1	 Background Review
	8.2	 Actual Cases
		8.2.1	 Automated and Precise Segmentation of Head and Neck Lesions and Vital Structures
		8.2.2	 Diagnosis and Differential Diagnosis of Head and Neck Tumors
		8.2.3	 Stage and Grade of Head and Neck Tumors
		8.2.4	 Prognosis Prediction and Efficacy Evaluation of Head and Neck Tumors
		8.2.5	 Gene Expression and Molecular Biomarker Prediction in Head and Neck Tumors
		8.2.6	 Applications in Other Diseases of the Head and Neck
	8.3	 Contrast and Prospects at Home and Abroad
	References
9: Application of Artificial Intelligence in Thoracic Diseases
	9.1	 Background
	9.2	 Practical Case
		9.2.1	 AI for Thoracic Image Acquisition and Reconstruction
		9.2.2	 AI for Thoracic Lesion Detection and Diagnosis
		9.2.3	 Chest Disease Outcome Assessment and Prediction AI
		9.2.4	 AI for Structured Reporting for Thoracic Imaging
	9.3	 Domestic and International Status Comparison and Outlook
	References
10: Application of Artificial Intelligence in Cardiovascular Diseases
	10.1	 Overview
	10.2	 Application of Artificial Intelligence in Cardiovascular Imaging
		10.2.1	 Cardiovascular Imaging
		10.2.2	 Cardiac Tissue Structure Segmentation
		10.2.3	 Diagnosis and Flow Evaluation of Coronary Artery Stenosis in Coronary Heart Disease
		10.2.4	 Cardiovascular Disease Risk Prediction and Prognosis Assessment
	10.3	 Artificial Intelligence in Cardiovascular Imaging: A Global Perspective
	References
11: Application of Artificial Intelligence in Breast Imaging
	11.1	 Background Overview
	11.2	 Practical Example
		11.2.1	 Breast Tumour Detection
		11.2.2	 Breast Tumour Segmentation
		11.2.3	 Discrimination Between Benign and Malignant Breast Tumours
		11.2.4	 Breast Cancer Molecular Subtype Prediction
		11.2.5	 Breast Cancer Lymph Node Metastasis Risk Assessment
		11.2.6	 Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer
	11.3	 Comparison and Prospect at Home and Abroad
	11.4	 Challenges and Prospects
	References
12: Application of Artificial Intelligence in Abdominal Imaging
	12.1	 Background Overview
	12.2	 Practical Examples
		12.2.1	 Artificial Intelligence and Liver Cancer
		12.2.2	 Artificial Intelligence and Pancreatic Cancer
		12.2.3	 Artificial Intelligence and Biliary Tract Carcinoma
		12.2.4	 Artificial Intelligence and Gastric Cancer
		12.2.5	 Artificial Intelligence and Colorectal Cancer
		12.2.6	 Artificial Intelligence and Kidney Cancer
		12.2.7	 Artificial Intelligence and Bladder Cancer
		12.2.8	 Artificial Intelligence and Prostate Cancer
		12.2.9	 Artificial Intelligence and Endometrial Carcinoma
		12.2.10 Artificial Intelligence and Cervical Cancer
		12.2.11 Artificial Intelligence and Ovarian Cancer
	12.3	 ChatGPT in Radiology
	12.4	 Comparison and Prospects Worldwide
	References
13: Application of Artificial Intelligence in Musculoskeletal Imaging
	13.1	 Overview
	13.2	 Typical Cases
		13.2.1	 AI and Fractures
		13.2.2	 AI and Osteoporosis
		13.2.3	 Artificial Intelligence and Bone Age
		13.2.4	 AI and Sports Injury Diseases
		13.2.5	 AI and Bone and Joint Degenerative Diseases
		13.2.6	 AI and Musculoskeletal Tumours
		13.2.7	 The Large Model and Musculoskeletal System
	13.3	 Artificial Intelligence in Musculoskeletal Imaging: A Global Perspective
	References
14: Application of Artificial Intelligence in Paediatric Imaging
	14.1	 Background Overview
	14.2	 Practical Cases
		14.2.1	 Clinical Application of AI in Improving Paediatric Imaging Techniques and Quality
		14.2.2	 Clinical Application of AI in Paediatric Neurological and Psychiatric Diseases
		14.2.3	 Clinical Application of AI in Paediatric Oncology
		14.2.4	 Clinical Application of AI in Paediatric Chest and Orthopaedics
	14.3	 Domestic and International Comparison and Prospects
	References
15: Application of Artificial Intelligence in Interventional Radiology
	15.1	 AI-Based Medical Image Processing Methods in Interventional Radiology
	15.2	 Radiation Dose Optimization of Interventional Radiology Using AI Technology
	15.3	 AI-Based Navigation Technology in Interventional Radiology
	15.4	 AI-Based Target Tracking Technology in Intervention Radiology
	15.5	 Application of AI in Predicting Therapeutic Effects After Interventional Therapy
	15.6	 Application of Foundation Models in Interventional Radiology
	15.7	 Artificial Intelligence in Interventional Radiology: A Global Perspective
	References
16: Application of Artificial Intelligence in Infectious Diseases
	16.1	 Background
	16.2	 Actual Cases
		16.2.1	 AI in Diagnosing TB
		16.2.2	 AI in Diagnosing HIV-Combined TB
		16.2.3	 AI in Diagnosing COVID-19
	16.3	 Large Language Model for Medical Imaging
	16.4	 Comparison and Prospect at Home and Abroad
	References
17: Applications of Artificial Intelligence in Nuclear Medicine
	17.1	 Nuclear Medicine Techniques
		17.1.1	 Low-Dose, Rapid, and High-Quality Imaging
		17.1.2	 CT-Free Attenuation and Scatter Correction
		17.1.3	 AI-Based Image Segmentation in the Field of Nuclear Medicine
	17.2	 Clinical Applications
		17.2.1	 Tumour Diagnosis and Differential Diagnosis
		17.2.2	 Analysis of Tumour Lesion Characteristics
		17.2.3	 Tumour Lesion Delineation
		17.2.4	 Evaluating Therapeutic Efficacy and Prognosis Prediction in Tumour Treatment
	17.3	 Comparison and Outlook of AI Technology in Nuclear Medicine in China and Other Countries
	References
18: Applications of Artificial Intelligence in Ultrasound Medicine
	18.1	 The Application of AI in Different Systems of Ultrasound Imaging
		18.1.1	 The Application of AI in Thyroid Ultrasound Imaging
			18.1.1.1	 Background
			18.1.1.2	 Applications
		18.1.2	 Applying AI in Breast Ultrasound Imaging
			18.1.2.1	 Background
			18.1.2.2	 Applications
		18.1.3	 Applying AI in Abdomen Ultrasound Imaging
			18.1.3.1	 Background
			18.1.3.2	 Applications
		18.1.4	 The Application of AI in Obstetrics and Gynaecology Ultrasound Imaging
			18.1.4.1	 Background
			18.1.4.2	 Applications
		18.1.5	 Applying AI in Blood Vessel Ultrasound Imaging
			18.1.5.1	 Background
			18.1.5.2	 Applications
		18.1.6	 Applying AI in Echocardiography
			18.1.6.1	 Background
			18.1.6.2	 Applications
		18.1.7	 Applying AI in Musculoskeletal Ultrasound Imaging
			18.1.7.1	 Background
			18.1.7.2	 Applications
		18.1.8	 Applying AI in Interventional Ultrasound
			18.1.8.1	 Background
			18.1.8.2	 Applications
		18.1.9	 Applying AI in Other Field of Ultrasound Imaging
	18.2	 AI Software Development in Ultrasound Medicine
	18.3	 Domestic and International Comparison and Outlook
	References
19: Application of Artificial Intelligence in Histopathology
	19.1	 Background Review
	19.2	 Real Cases
		19.2.1	 Pathological Diagnosis
		19.2.2	 Survival Analysis and Prognostic Evaluation
		19.2.3	 Gene Information Prediction
	19.3	 Rapid Establishment of a Pathological AI System for Auxiliary Diagnosis
	19.4	 Obstacles and Strategies for Establishing a Clinical Pathological AI System
	19.5	 Comparison and Prospects of Research Worldwide
	References
20: Application of Artificial Intelligence in Ophthalmology
	20.1	 Application of AI in Anterior Segmental Diseases
		20.1.1	 Refractive Surgery
		20.1.2	 Corneal-Related Diseases
			20.1.2.1	 Keratoconus
			20.1.2.2	 Keratitis
		20.1.3	 Dry Eye Disease
		20.1.4	 Cataract
	20.2	 Application of AI in Fundus Diseases
		20.2.1	 Diabetic Retinopathy
		20.2.2	 Age-Related Macular Degeneration
		20.2.3	 Retinopathy of Prematurity
		20.2.4	 Retinal Vein Occlusion
		20.2.5	 Glaucoma
	20.3	 Eyes as a Window to Systemic Diseases
		20.3.1	 Cardiovascular Diseases
		20.3.2	 Anaemia
		20.3.3	 Renal Diseases
		20.3.4	 Hepatobiliary Diseases
	20.4	 Future Prospect
		20.4.1	 AI Applications for Ophthalmic Surgery
		20.4.2	 AI Application for Eye Personalization Recognition
	References
21: Research Report on the Current Application of Artificial Intelligence in Chinese Medical Imaging
	21.1	 Distribution of Research Samples
	21.2	 Current Status of Medical Imaging AI
	21.3	 Problems and Prospects
		21.3.1	 Issues with the Development of Medical Imaging AI
		21.3.2	 Domestic and International Comparison and Outlook
		21.3.3	 Future Prospects of Medical Imaging AI
	21.4	 Summary
	References
22: Current Situation and Prospects of Artificial Intelligence Research in Medical Imaging
	22.1	 Overview of Published Papers on Medical Imaging Artificial Intelligence
	22.2	 Overview of Projects Related to Medical Artificial Intelligence Funded by the National Natural Science Foundation of China
	22.3	 Overview of Medical Imaging Artificial Intelligence Patent Development
		22.3.1	 The International Situation
			22.3.1.1	 Global Application Trend: A Turning Point in 2015, with China Ranking First
			22.3.1.2	US Leads in Technical Originality, Prioritizing China as Target Market
			22.3.1.3	 High-Value Patents Dominated by Europe and the United States, China’s Position Gradually Improving
		22.3.2	 Overview of the Domestic Market
			22.3.2.1	 The Application Trend Converges Nationwide, with Guangdong Having a Slightly Stronger Layout Awareness
			22.3.2.2	 Proportion of High-Value Patents Slightly Increased in Shanghai, Shandong, and Sichuan
	22.4	 Challenges and Prospects in Medical Imaging AI Research
	References
23: Standardization of AI Products for Medical Imaging Processing
	23.1	 Overview of AIMD Standardization in China
		23.1.1	 Standardization Road Map
			23.1.1.1	 Fundamental Standards
			23.1.1.2	 Management Standards
			23.1.1.3	 Methodology Standards
			23.1.1.4	 Product Standards
		23.1.2	 Current Progress
	23.2	 Standardization Direction of AI Products for Medical Imaging
		23.2.1	 Decision Supporting Products
		23.2.2	 Process Optimization Products
			23.2.2.1	 Rapid Reconstruction of Magnetic Resonance Imaging
			23.2.2.2	 Application of Deep Learning Reconstruction in Low-Dose CT
		23.2.3	 Auxiliary Treatment and Intervention
			23.2.3.1	 Evaluation of Precision Radiotherapy Software
			23.2.3.2	 Evaluation of Preoperative Planning Software
		23.2.4	 Intelligent Traditional Chinese Medicine (ICTM)
	23.3	 Application of Standards and Future Trends
		23.3.1	 AIMD Testing Service
		23.3.2	 Future Trends
	References
24: Introduction to Clinical Trials and Case Reports for Medical Image AI Products
	24.1	 Clinical Trial Methods for Artificial Intelligence Products
		24.1.1	 Clinical Trial Methods for Chinese AI Products
		24.1.2	 Clinical Trials of Class II Medical Devices of Al Products
		24.1.3	 Clinical Trials of Class III Medical Devices with Al Products
	24.2	 Introduction to Case Studies of Clinical Trials of Artificial Intelligence Products with NMPA Certificates
		24.2.1	 Case Study of Clinical Trials of AI-Assisted Analytical Software for Intracranial Haemorrhage
		24.2.2	 Clinical Trial Case for Intracranial Tumour AI-Assisted Analysis Software Product
		24.2.3	 Clinical Trial Case of AI-Assisted Analysis Software Product for Head and Neck Vascular Diseases
		24.2.4	 Clinical Trial Case of AI-Assisted Analysis Software Product for Coronary Artery
		24.2.5	 Clinical Trial Case of AI-Assisted Analysis Software Product for Rib Fracture
	24.3	 Challenges and Prospects
		24.3.1	 Comparison of Clinical Trials of AI Products at Home and Abroad
		24.3.2	 Challenges and Prospects of Clinical Trials for AI Products
	References
25: Clinical Evaluation of AI-Based Medical Devices
	25.1	 Global Clinical Evaluation Requirements for Deep-Learning-Assisted Decision-Making Medical Devices
		25.1.1	 Overview of Clinical Evaluation Requirements in Member Countries of the International Medical Device Regulators Forum
		25.1.2	 Clinical Evaluation of Typical Deep-Learning-Assisted Decision-Making Products in the United States
			25.1.2.1	 Image-Based Computer-Aided Detection Software
			25.1.2.2	 Image-Based Computer-Aided Diagnosis Software
			25.1.2.3	 Image-Based Computer-Aided Triage Software
			25.1.2.4	 Electrocardiogram (ECG) Data Monitoring and Diagnosis Software
			25.1.2.5	 Real-Time Image-Based Computer-Aided Detection Software
	25.2	 Clinical Evaluation Requirements for Deep-Learning-Assisted Decision-Making Products in China
		25.2.1	 Deep-Learning-Assisted Decision-Making Product Clinical Trial Design Considerations
			25.2.1.1	 Basic Types of Clinical Trial Design and Evaluation End Points
			25.2.1.2	 Study Subjects
			25.2.1.3	 “Gold Standard”
			25.2.1.4	 Sample Size and Other Considerations
		25.2.2	 Basic Situation of Clinical Evaluation of Typical Deep-Learning-Assisted Decision-Making Products
			25.2.2.1	 Diabetic Retinopathy Decision-Making Products
			25.2.2.2	 Pneumonia CT Image-Assisted Triage and Evaluation Products
			25.2.2.3	 CT Image-Assisted Triage Product for Intra-cranial Haemorrhage
			25.2.2.4	 Lung Nodule CT Image-Assisted Detection Products
			25.2.2.5	 Coronary CT Fraction Flow Reservation Products
	25.3	 Issues and Prospects
	25.4	 Application of Artificial Intelligence Technology in In Vitro Diagnostic Products and Clinical Evaluation Considerations
		25.4.1	 Peripheral Blood Cell Image-Assisted Recognition Software
		25.4.2	 Cervical Cytology Image Computer-Assisted Analysis Software
		25.4.3	 Pathological Image Computer-Assisted Analysis Software
		25.4.4	 Gene Sequence (Mutation) Analysis Software
		25.4.5	 Further Thoughts
			25.4.5.1	 Clinical Intended Use
			25.4.5.2	 Clinical Trial Evaluation End Points
			25.4.5.3	Reagents and Instruments Used in Conjunction
26: Research Advances in Supervision on AI-Based Medical Imaging Products
	26.1	 Research Advances in International Supervision
		26.1.1	 The United States
		26.1.2	 European Union, United Kingdom, Canada and Australia
		26.1.3	 Japan, South Korea and Singapore
		26.1.4	 International Medical Device Regulators Forum
		26.1.5	 The International Telecommunication Union and the World Health Organization
	26.2	 Domestic Research Advances in Supervision
		26.2.1	 Supervision of Ecological Construction
			26.2.1.1	 AI Medical Device Innovation Cooperation Platform
			26.2.1.2	 AI Medical Device Innovation Task Ranking
		26.2.2	 Research Advances in Supervision
			26.2.2.1	 Classification Definition
			26.2.2.2	 Product Naming
			26.2.2.3	 Technical Review
			26.2.2.4	 System Verification
		26.2.3	 Typical Product Overview
	26.3	 Issues and Prospects
		26.3.1	 Supervision and Regulations of AI Medical Devices Need to Be Improved
		26.3.2	 The Safety and Effectiveness Evaluation System of AI Medical Devices Needs to Be Strengthened
27: Current Status of Artificial Intelligence (AI) Industrialization in Medical Imaging
	27.1	 An Overview of the Current Status of Industrial Development
		27.1.1	 An Overview of the Development of AI in Medical Imaging in China
		27.1.2	 National Policies Continue to Support It
		27.1.3	 Significant Increase in Medical Device Registration Approval
		27.1.4	 Steady Growth in Market Size
		27.1.5	 Distribution of AI Medical Imaging Industry
			27.1.5.1	 Radiology Department
			27.1.5.2	 Thoracic Surgery
			27.1.5.3	 Department of Cardiology
			27.1.5.4	 Department of Endocrinology
			27.1.5.5	 Information Section
			27.1.5.6	 Business Model
			27.1.5.7	 Commercialization and Financing
	27.2	 Introduction to AI Medical Imaging Enterprises
	27.3	 Developing Tendency and Prospect
28: Current Situation and Prospects of Education in Medical Imaging Artificial Intelligence
	28.1	 Research on the Current Situation of AI in Medical Imaging Education
		28.1.1	 The Current Situation of AI Courses Offered in Training Junior College Students
		28.1.2	 The Current Situation of AI Courses Offered in Undergraduate Training
		28.1.3	 The Current Situation of AI Courses Offered in the Process of Cultivating Master’s and Doctoral Students
		28.1.4	 The Current Situation of AI Courses Offered in the Standardized Resident Physician Training Process
		28.1.5	 Summary and Analysis
	28.2	 Artificial Intelligence Medical Imaging Education Promotes Cross-Talent Training
		28.2.1	 AI Applications for Medical Imaging Higher Education
		28.2.2	 AI Applications in Medical Imaging Continuing Education
			28.2.2.1	 AI Integration in a Multiformat Teaching Approach for Continuing Medical Education
			28.2.2.2	 Management Mechanism for Integrating AI Education in Continuing Medical Education
			28.2.2.3	 Continuing Medical Education Articulation with Undergraduate Education and Postgraduate Standardized Training
	28.3	 Artificial Intelligence Innovates the Teaching Mode of Medical Imaging Higher Education
	28.4	 Application Prospects of Artificial Intelligence in Medical Imaging Education
	References
29: Ethics and Safety in Medical Imaging and Artificial Intelligence
	29.1	 Ethical Issues in the Field of Medical Imaging AI
		29.1.1	 Basic Ethical Principles of Medical Imaging AI
			29.1.1.1	 Transparency and Interpretability
			29.1.1.2	 Privacy Protection
			29.1.1.3	 Doctors Subjectivity
			29.1.1.4	 Responsibility Traceability
			29.1.1.5	 Fair Benefits
		29.1.2	 Progress in Medical Imaging AI Ethics Worldwide
			29.1.2.1	 Specification Aspects
			29.1.2.2	 Data Aspects
			29.1.2.3	 Algorithms
	29.2	 Security Issues in the Field of Medical Imaging and AI
		29.2.1	 Progress in Data Security Both at Home and Abroad
			29.2.1.1	 Data Security Legislation
			29.2.1.2	 Construction of the Data Security Protection System
			29.2.1.3	 Strengthening Data Protection Technology and Means
		29.2.2	 Algorithmic Security Makes Progress Worldwide
			29.2.2.1	 Algorithm Vulnerabilities
			29.2.2.2	 Black Box of the Algorithm
			29.2.2.3	 Algorithmic Bias
		29.2.3	 Progress in Human-Computer Interaction Security at Home and Abroad
	29.3	 Summary and Outlook
		29.3.1	 Ethical Issues and Perspectives
		29.3.2	 Security Issues and Prospects
		29.3.3	 Data Protection and Outlook
	References