Objective To broaden the current understanding of the usage willingness about artificial intelligence (AI) robots and relevant influence factors for elderly patients. Methods The elderly patients in the inpatient ward, outpatient department and physical examination of the Department of Geriatrics, West China Hospital of Sichuan University were selected by convenient sampling for investigation between February and April 2020, to explore the willingness of elderly patients to use AI robots and related influencing factors. Results A total of 446 elderly patients were included. There were 244 males and 202 females. The willingness to use AI robots was (14.40±3.62) points. There were statistically significant differences among the elderly patients with different ages, marital status, living conditions, educational level, current health status, current vision status, current hearing status, self-care ability and family support in their willingness to use AI robots (P<0.05). Multiple linear regression analysis showed that age, education level and family support were the influencing factors of use intention (P<0.05). Among the elderly patients, 60.76% had heard of AI robots, but only 28.03% knew the medical application of AI robots, and only 13.90% had used AI robot services. Most elderly patients (>60%) thought that some adverse factors may reduce their usage willingness, like “the price is too expensive” and “the use is complex, or I don’t know how to use”. Conclusions Elderly patients’ cognition of AI robots is still at a low level, and their willingness to use AI robots is mainly affected by age, education level and family support. It is suggested to consider the personalized needs of the elderly in terms of different ages, education levels and family support, and promote the cheap and user-friendly AI robots, so as to improve the use of AI robots by elderly patients.
Objective To identify the heart sounds of aortic stenosis by deep learning model based on DenseNet121 architecture, and to explore its application potential in clinical screening aortic stenosis. Methods We prospectively collected heart sounds and clinical data of patients with aortic stenosis in Tianjin Chest Hospital, from June 2021 to February 2022. The collected heart sound data were used to train, verify and test a deep learning model. We evaluated the performance of the model by drawing receiver operating characteristic curve and precision-recall curve. Results A total of 100 patients including 11 asymptomatic patients were included. There were 50 aortic stenosis patients with 30 males and 20 females at an average age of 68.18±10.63 years in an aortic stenosis group (stenosis group). And 50 patients without aortic valve disease were in a negative group, including 26 males and 24 females at an average age of 45.98±12.51 years. The model had an excellent ability to distinguish heart sound data collected from patients with aortic stenosis in clinical settings: accuracy at 91.67%, sensitivity at 90.00%, specificity at 92.50%, and area under receiver operating characteristic curve was 0.917. Conclusion The model of heart sound diagnosis of aortic stenosis based on deep learning has excellent application prospects in clinical screening, which can provide a new idea for the early identification of patients with aortic stenosis.
The monitoring of pregnant women is very important. It plays an important role in reducing fetal mortality, ensuring the safety of perinatal mother and fetus, preventing premature delivery and pregnancy accidents. At present, regular examination is the mainstream method for pregnant women's monitoring, but the means of examination out of hospital is scarce, and the equipment of hospital monitoring is expensive and the operation is complex. Using intelligent information technology (such as machine learning algorithm) can analyze the physiological signals of pregnant women, so as to realize the early detection and accident warning for mother and fetus, and achieve the purpose of high-quality monitoring out of hospital. However, at present, there are not enough public research reports related to the intelligent processing methods of out-of-hospital monitoring for pregnant women, so this paper takes the out-of-hospital monitoring for pregnant women as the research background, summarizes the public research reports of intelligent processing methods, analyzes the advantages and disadvantages of the existing research methods, points out the possible problems, and expounds the future development trend, which could provide reference for future related researches.
The robotic bronchoscopy system is a new technology for lung lesion location, biopsy and interventional therapy. Its safety and effectiveness have been clinically proven. Based on many advanced technologies carried by the robotic bronchoscopy system, it is more intelligent, convenient and stable when clinicians perform bronchoscopy operations. It has higher accuracy and diagnostic rates, and less complications than bronchoscopy with the assistance of magnetic navigation and ordinary bronchoscopy. This article gave a review of the progress of robotic bronchoscopy systems, and a prospect of the combination with artificial intelligence.
Artificial intelligence (AI) is reshaping evidence-based clinical decision-making. From the perspective of clinical decision-making, this paper explores the collaborative value of AI in life-cycle health management. While AI can enhance early disease screening efficiency (e.g., medical image analysis) and assist clinical decision-making through personalized health recommendations, its reliance on non-specialized data necessitates the development of dedicated AI systems grounded in high-quality, specialty-specific evidence. AI should serve as an auxiliary tool to evidence-based clinical decision-making, with physicians’ comprehensive judgment and humanistic care remaining central to medical decision-making. Clinicians must improve the reliability of decision making through refining prompt design and cross-validating AI outputs, while actively participate in AI tool optimization and ethical standard development. Future efforts should focus on creating specialty-specific AI tools based on high-quality evidence, establishing dynamic guideline update systems, and formulating medical ethical standards to position AI as a collaborative partner for physicians in implementing life-cycle health management.
ObjectiveTo study the application of artificial intelligence based on neural network in breast cancer screening and diagnosis, and to summarize its current situation and clinical application value.MethodThe combined studies of neural network and artificial intelligence in the directions of breast mammography, breast ultrasound, breast magnetic resonance, and breast pathology diagnosis in CNKI and PubMed database were reviewed.ResultsPublic databases of mammography, such as Digital Database for Screening Mammography (DDSM), provided raw materials for the research of neural network in the field of mammography. Mammography was the most widely used data for screening and diagnosis of breast diseases by neural network. In the field of mammography and color doppler ultrasound, neural network could segment, measure, and analyze the characteristics, judge the benign or malignant, and issue a structured report. The application of neural network in the field of breast ultrasound focused on the diagnosis and treatment of benign and malignant breast diseases. Samsung Madison Group taken the lead in grafting research results into ultrasound instruments. Breast MRI had a lot of high-throughput information, which had became the breakthrough point for the joint study of artificial neural network and imaging omics. Pathological images had more data information to be measured, and quantitative analysis of data was the advantage of neural network. The combination of the two kinds of methods could significantly improve the diagnosis time of pathologists.ConclusionsTo study the application of artificial intelligence in breast cancer screening and diagnosis is to analyze the application of neural network in breast imaging and pathology. At present, artificial intelligence screening can be used as a physician assistant and an objective diagnostic reference assistant, to improve the diagnosis of breast disease. With the development of medical image histology and neural network, the application of artificial intelligence in medical field can be extended to surgical method design, efficacy evaluation, prognosis analysis, and so on.
Continuous renal replacement therapy (CRRT) is one of the important therapeutic techniques for critically ill patients. In recent years, the field of artificial intelligence has developed rapidly and has been widely applied in manufacturing, automotive, and even daily life. The development and application of artificial intelligence in the medical field are also advancing rapidly, and artificial intelligence radiographic imaging result judgment, pathological result judgment, patient prognosis prediction are gradually being used in clinical practice. The development of artificial intelligence in the field of CRRT has also made rapid progress. Therefore, this article will elaborate on the current application status of artificial intelligence in CRRT, as well as its future prospects in CRRT, so as to provide a reference for understanding the application of artificial intelligence in CRRT.
Electrocardiogram (ECG) is a noninvasive, inexpensive, and convenient test for diagnosing cardiovascular diseases and assessing the risk of cardiovascular events. Although there are clear standardized operations and procedures for ECG examination, the interpretation of ECG by even trained physicians can be biased due to differences in diagnostic experience. In recent years, artificial intelligence has become a powerful tool to automatically analyze medical data by building deep neural network models, and has been widely used in the field of medical image diagnosis such as CT, MRI, ultrasound and ECG. This article mainly introduces the application progress of deep neural network models in ECG diagnosis and prediction of cardiovascular diseases, and discusses its limitations and application prospects.
Generative artificial intelligence (AI) technology plays a significant role in enhancing data application capabilities, improving disease diagnosis and treatment plans, and advancing health management, drug development, genetic analysis, and precision medicine. However, due to the diagnostic complexity, treatment diversity, and high technical demands of orthopedic diseases, the application of generative AI in orthopedics is still in its early exploration stage. This paper, based on the experience of applying generative AI, summarizes the concept, working principles, progress of application in orthopedics, as well as the existing shortcomings and optimization strategies, aiming to provide valuable insights for the application of generative AI in orthopedics clinical practice.
Cardiovascular disease (CVD) has caused a huge burden of disease worldwide, and accurate diagnosis and assessment of CVD has a clear significance for improving the prognosis of patients. The development of artificial intelligence (AI) and its rapid application in the medical field have enabled new approaches for the analysis and fitting of various CVD data. At present, in addition to structured medical records, the CVD field also includes a large number of non-linear data brought by imaging and electrophysiological examinations. How to use AI to process such multi-source data has been explored by a large number of studies. Therefore, this review discusses the existing ways of processing various multi-source heterogeneous data with existing artificial intelligence technologies by summarizing various existing studies, and analyzes their possible advantages and disadvantages, in order to provide a basis for the future application of AI in CVD.
