Current and Future Application of Artificial Intelligence in Clinical Medicine

Cancer is a disorder with aggressive, low-median survival. Unfortunately, the healing time is long and expensive owing to high recurrence and mortality rates. It is essential to increase patient survival. Over the years, mathematical and computer engineering advancements have inspired numerous scientists to use quantitative methods to evaluate disease prognosis, such as multivariate statistical analysis, and the precision of these studies is considerably higher than that of observational predictions. However, as artificial intelligence (AI) has found widespread applications in clinical cancer research in recent years, especially machine learning and deep learning, cancer prediction output has reached new heights. The literature on the use of AI for cancer diagnosis and prognosis is discussed in this part. We discuss how AI supports the diagnosis of cancer, especially in terms of its unparalleled precision. We also illustrate forms in which these approaches progress the field. Opportunities and problems are addressed in the clinical application of AI.

The application of artificial intelligence has been in full swing in all walks of life, especially in the medical field, which has been favored and achieved remarkable results. This paper explores its application and development prospects by analyzing the fundamental principle of AI and its application in the medical-relevant industry and discusses its role in the medical field, which is more an aid than a substitute. Meanwhile, the application of AI in medical care is relatively lagging behind and has a lot of room for development compared with other fields. AI technology should be applied to the medical field in a _targeted manner to deal with bottlenecks and exert greater potential and value.

The COVID-19 outbreak is currently rampant worldwide. The situation in China has been the toughest since the very beginning of the outbreak. However, the pace of the spread has been controlled after employing various policies to eliminate the further painful loss. The advancement of high technology in AI, big data, and machine learning has benefited mankind significantly, especially in this global public health crisis. This paper aims to rethink China’s experiences in the application of AI from the concept of the “general-purpose technology” and attempts to link the technical aspect of AI to the future of Chinese society and culture regarding this technology.

The essence of the Artificial Intelligence (AI) decision system is that it helps us to make a better clinical plan and improve decision efficiency. Mature systems need data, core algorithms, and good interface support. Based on the cancer database of the Chinese Society of Clinical Oncology and the domestic core algorithm, we created the AI decision system with independent intellectual property rights using I ~ IVphases of clinical trials and research. To verify the accuracy of the system, we need assistance from doctors at different levels. The promotion of this system will further improve the standardized diagnosis and treatment of breast cancer and provide guidance for the establishment and application of other intelligent decision-making systems.

“Artificial intelligence and medical image” is an auxiliary tool for the computer to complete image classification, _target detection, image segmentation, and retrieval and assist doctors in diagnosing and treatment based on medical image through deep learning. This chapter includes the review of Artificial intelligence (AI) and its application in radiology, pathology, eye disease, deontology, dermatology, and ophthalmology, which we have benefited from the use of AI methods. Modern medicine is evidence-based medicine based on experiments. Doctors' diagnosis and treatment conclusions must be based on corresponding diagnostic data. Imaging is an important part of diagnosing, and 80% to 90% of data in the medical industry are derived from medical imaging. Therefore, clinicians have a strong demand for images, and they need to conduct a variety of quantitative analyses of medical images and comparison of historical images to complete a diagnosis. In contrast to this qualitative reasoning, AI is good at identifying complex patterns in the data and providing quantitative assessments in an automated manner. Integrating AI into clinical workflows as a tool to assist physicians allows for more accurate and repeatable radiological assessments.

This perspective aims to summarize the COVID-19 experience of the Chinese people, which included psychological assistance and open datasets. We hope that countries across the world can utilize the lessons learned and tools developed by China in response to the COVID-19 pandemic and share their fighting experience in academic publication freely so the world can solve this crisis. This perspective focuses on psychological assistance and open datasets in China's COVID-19 pandemic; they played an important role in fighting with COVID-19 and acquired major contributions to calm people in the restless environment. We hope other countries can absorb the quintessence from this experience and utilize their situation to prevent and protect citizens from being infected and get rid of sequela in the COVID-19 epidemic.

This chapter reports that Artificial Intelligence (AI) in clinical oncology includes deep learning for detecting the lung nodules and other algorithms for analyzing the nodules to acquire an early diagnosis of nodules and tumors. Therefore, the early diagnosis is of great significance in the treatment of lung cancer or precancerous disease. Also, the early detection of nodules can improve the treatment effects and reduce the chance of misdiagnosis.

Deep learning-based Artificial Intelligence (AI) with medical imaging cooperation has led to a significant increase in the detection rate of early lung cancer, but the identification of its benign and malignant nature remains difficult. This chapter describes the effect of AI on CT values, maximum surface area, volume, 3D longitudinal diameter, solid occupancy, multiplication time, and also ground glass pulmonary nodule. We believe that AI will be able to provide a variety of features for the analysis of the characteristics of the world's largest and most complex computer systems. It helps radiologists to determine the benign and malignant nature of pulmonary ground-glass nodules and improves the accuracy and quality of diagnosis. Besides, patient’s survival rate and quality of life can be improved as well.

Artificial intelligence technology is frontier technology as the interaction and cooperation between AI, and different disciplines can bring great opportunities and impetus to the development in social, scientific, and technological progress. This chapter included AI in imaging, pathology, and development in pathological diagnosis. With the continuous progress of society and science, AI technology has penetrated all aspects of human production and life, greatly promoting the liberation of productivity and the change in human lifestyle. Among them, the medical field is the one that has been significantly changed. The development and integration of the two disciplines have brought great changes to the development of healthcare systems across the globe. AI technology has been widely used in the field of medical treatment, including medical education, medical imaging, pathology, diagnostics, medical robotics, medical data management, molecular tumor research, etc. However, with time, the disadvantages of AI have been gradually exposed, and this historic cross-border cooperation also faces many challenges.