Current and Future Application of Artificial Intelligence in Clinical Medicine presents updates on the application of machine learning and deep learning techniques in medical procedures. . Chapters in the volume have been written by outstanding contributors from cancer and computer science institutes with the goal of providing updated knowledge to the reader. Topics covered in the book include 1) Artificial Intelligence (AI) applications in cancer diagnosis and therapy, 2) Updates in AI applications in the medical industry, 3) the use of AI in studying the COVID-19 pandemic in China, 4) AI applications in clinical oncology (including AI-based mining for pulmonary nodules and the use of AI in understanding specific carcinomas), 5) AI in medical imaging. Each chapter presents information on related sub topics in a reader friendly format.
The combination of expert knowledge and multidisciplinary approaches highlighted in the book make it a valuable source of information for physicians and clinical researchers active in the field of cancer diagnosis and treatment (oncologists, oncologic surgeons, radiation oncologists, nuclear medicine physicians, and radiologists) and computer science scholars seeking to understand medical applications of artificial intelligence.
Author(s): Shigao Huang, Jie Yang
Publisher: Bentham Science Publishers
Year: 2021
Language: English
Pages: 153
City: Singapore
Cover
Title
Copyright
End User License Agreement
Contents
Preface
Acknowledgements
List of Contributors
Artificial Intelligence (AI) in Cancer Diagnosis and Prognosis
Parsa Mahmood Dar1,*, Amara Dar2 and Komal Hayat3
1. INTRODUCTION
2. MAJOR CANCER TYPE
2.1. Lung Cancer
2.2. Breast Cancer
2.3. Prostate Cancer
2.4. Colorectal Cancer
2.5. Development in Diagnostic Tools
3. ARTIFICIAL INTELLIGENCE (AI) IN PRECISION MEDICINE
4. CHALLENGES FOR AI IN CANCER TREATMENT
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Alternative or Auxiliary: Artificial Intelligence Accelerates the Development and Transformation of the Medical Care
Jie Yang1,2,*, Quanyi Hu1, Rui Tang3, Han Wang4,5, Kairong Duan1,6, Feng Wu5 and Simon Fong1,5
1. INTRODUCTION
2. ABOUT ARTIFICIAL INTELLIGENCE
3. APPLICATION STATUS AND DEVELOPMENT PROSPECTS IN THE MEDICAL INDUSTRY
3.1. Current Status of the Application of AI
3.1.1. Intelligent Services in the Ageing Society
3.1.2. Smart Ward
3.1.3. Hazard Warning Identification
3.1.4. Assistance in Disease Diagnosis
3.1.5. Assistance in Drug Development and Disease Treatment
3.1.6. Gene Sequencing
3.2. Development Prospects of AI
3.2.1. Cancer Management: The Combination of Tumor Organic Chips and AI
3.2.2. Clinical Decision Support: Intelligent Data Integration
4. THINKING AND PROSPECT
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Rethinking Artificial Intelligence in China’s COVID-19 Pandemic
Qichao Wang1,*
1. INTRODUCTION
2. THE COVID-19 AND AI APPLICATION IN CHINA
2.1. Big Data, Population Management, and Transportation
2.2. AI-based Medical System Against COVID in China
2.3. AI-Based Public Policy Against COVID-19 in China
2.4. AI Enterprises and Societal Research And Development in China
3. AI AS A GENERAL-PURPOSE TECHNOLOGY OF COVID-19 IN CHINA
4. CONCLUSION
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Artificial Intelligence System and its Application in Clinical Oncology
Shigao Huang1,*, Jie Yang2,3, Qun Song2, Kexing Liu2, Simon Fong2,4 and Qi Zhao1
1. INTRODUCTION
2. DEVELOPMENT OF AN AI SYSTEM
2.1. Establish a Knowledge Base
2.2. Building Knowledge Map
3. MAN-MACHINE COMMUNICATION INTERFACE
4. AI CLINICAL VALIDATION
4.1. Phase I Clinical Research
4.2. Phase II Clinical Research
4.3. Phase III Clinical Research
4.4. Phase IV Clinical Research
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Current Medical Imaging and Artificial Intelligence and its Future
Shigao Huang1, Jie Yang2,3, Lijian Tan3, Simon Fong2,4 and Qi Zhao1
1. INTRODUCTION
2. PROCESS OF AI IN MEDICAL IMAGING
2.1. Develop Standardized Use Cases
2.2. Establish a Data Sharing Method
2.3. Assess Clinical Practice and Infrastructure Needs
2.4. Ensure Technical Safety and Accuracy
3. APPLICATION OF AI + MEDICAL IMAGING IN VARIOUS FIELDS
3.1. Lung Screening
3.2. Screening for Radiculopathy
3.3. Target Outline
3.4. Three-dimensional Imaging of Viscera
3.5. Pathological Analysis
4. AI AND ITS APPLICATIONS IN EYE DISEASE
5. AI IN DENTISTRY
5.1. The Rise of Machine Learning
5.2. The Future of AI in Dentistry
6. EFFECTS OF AI ON TUMOR IMAGE WORKFLOW
7. THE EXPLORATION AND DEVELOPMENT OF AI IMAGE
7.1. Philips
7.2. Ali Health
7.3. Tencent Miying
7.4. Hainer Medical Trust
7.5. Deduce Technology
7.6. Yassen Technologies
7.7. Hui-Yi Hui Ying
7.8. Tuma Depth
7.9. Diyinjia
7.10. Heart Link Medical
7.11. DeepCare
7.12. Peptide Building Blocks
7.13. Smart Shadow Medical
7.14. Imagemesh Laboratory
8. THE NEXT FRONTIER
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Artificial Intelligence Played an Active Role in the COVID-19 Epidemic in China
Shigao Huang1,*, Jie Yang2,3,4, Xianxian Liu2, Simon Fong2,4 and Qi Zhao1
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Current Status and Future Outlook of Deep Learning Techniques For Nodule Detection
Shigao Huang1,*, Jie Yang2,3,4, Kun Lan2, Sunny Yaoyang Wu2, Simon Fong2,4 and Qi Zhao1
1. INTRODUCTION
2. OVERVIEW OF PULMONARY NODULES
3. OVERVIEW OF AI AND DEEP LEARNING
4. APPLICATION OF DEEP LEARNING IN LUNG NODULES
4.1. Rationale for the Detection of Pulmonary Nodules
4.2. Application of Deep Learning in the Detection and Diagnosis of Pulmonary Nodules
5. DATABASE
6. ISSUES AND OUTLOOK
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Artificial Intelligence-Based Mining of Benign and Malignant Characteristics of Pulmonary Ground-Glass Nodules
Xiaoxia Li1, Ting Gao2 and Shigao Huang3,*
1. DESCRIPTION OF AI
2. DEFINITION AND CLASSIFICATION OF GROUND-GLASS NODULES
3. ANALYSIS OF BENIGN AND MALIGNANT CHARACTERISTICS OF GROUND-GLASS NODULES
3.1. CT Value
3.2. Maximum Surface Area
3.3. Three-Dimensional Volume
3.4. Three-D Length to Diameter
3.5. Real Proportion
3.6. Doubling Time
3.7. Compactness and Sphericity Degree
4. OUTLOOK AND PROGRESS
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
ABBREVIATION
REFERENCES
Development of Artificial Intelligence in Imaging and Pathology
Gang Liu1 and Tao Qi2,*
1. INTRODUCTION
2. AI IMAGING
2.1. Overview of AI Imaging
2.2. Research Progress of AI Imaging
3. PATHOLOGY
3.1. Exploration of AI in Pathological Diagnosis
3.2. Grading of Renal Clear Cell Carcinoma
3. 3. Segmentation of Neoplastic Glandular Structure in Colorectal Cancer
3.4. Detection of MYCO Bacterium Tuberculosis in Special Staining
3.5. Determination of Proliferating Cells in Cervical Epithelial Lesions
4. THE EXPLORATION OF AI IN TUMOR PROGNOSTIC JUDGMENT
4.1. Prediction of Survival in Patients with Non-small Cell Lung Cancer and Breast Cancer
4.2. Predicting whether Patients with Stage T1 Colon Cancer need Additional Radical Surgery
4.3. To Evaluate Postoperative Distant Metastasis in Patients with Esophageal Squamous Cell Carcinoma
5. DEEP LEARNING IN THE MELANOCYTE TUMOR PATHOLOGICAL DIAGNOSIS
5.1. Deep Learning Development in Pathological Diagnosis
5.2. Diagnostic Melanocyte Benign and Malignant
5.3. Future Progress of AI Diagnosis
6. SUMMARY AND PROSPECT
CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
REFERENCES
Subject Index
Back Cover
