ObjectiveTo compare the consistency of artificial analysis and artificial intelligence analysis in the identification of fundus lesions in diabetic patients.MethodsA retrospective study. From May 2018 to May 2019, 1053 consecutive diabetic patients (2106 eyes) of the endocrinology department of the First Affiliated Hospital of Zhengzhou University were included in the study. Among them, 888 patients were males and 165 were females. They were 20-70 years old, with an average age of 53 years old. All patients were performed fundus imaging on diabetic Inspection by useing Japanese Kowa non-mydriatic fundus cameras. The artificial intelligence analysis of Shanggong's ophthalmology cloud network screening platform automatically detected diabetic retinopathy (DR) such as exudation, bleeding, and microaneurysms, and automatically classifies the image detection results according to the DR international staging standard. Manual analysis was performed by two attending physicians and reviewed by the chief physician to ensure the accuracy of manual analysis. When differences appeared between the analysis results of the two analysis methods, the manual analysis results shall be used as the standard. Consistency rate were calculated and compared. Consistency rate = (number of eyes with the same diagnosis result/total number of effective eyes collected) × 100%. Kappa consistency test was performed on the results of manual analysis and artificial intelligence analysis, 0.0≤κ＜0.2 was a very poor degree of consistency, 0.2≤κ＜0.4 meant poor consistency, 0.4≤κ＜0.6 meant medium consistency, and 0.6≤κ＜1.0 meant good consistency.ResultsAmong the 2106 eyes, 64 eyes were excluded that cannot be identified by artificial intelligence due to serious illness, 2042 eyes were finally included in the analysis. The results of artificial analysis and artificial intelligence analysis were completely consistent with 1835 eyes, accounting for 89.86%. There were differences in analysis of 207 eyes, accounting for 10.14%. The main differences between the two are as follows: (1) Artificial intelligence analysis points Bleeding, oozing, and manual analysis of 96 eyes (96/2042, 4.70%); (2) Artificial intelligence analysis of drusen, and manual analysis of 71 eyes (71/2042, 3.48%); (3) Artificial intelligence analyzes normal or vitreous degeneration, while manual analysis of punctate exudation or hemorrhage or microaneurysms in 40 eyes (40/2042, 1.95%). The diagnostic rates for non-DR were 23.2% and 20.2%, respectively. The diagnostic rates for non-DR were 76.8% and 79.8%, respectively. The accuracy of artificial intelligence interpretation is 87.8%. The results of the Kappa consistency test showed that the diagnostic results of manual analysis and artificial intelligence analysis were moderately consistent (κ=0.576, P＜0.01).ConclusionsManual analysis and artificial intelligence analysis showed moderate consistency in the diagnosis of fundus lesions in diabetic patients. The accuracy of artificial intelligence interpretation is 87.8%.
Acute kidney injury (AKI) is a common critical illness in clinical practice, with complex etiologies, acute onset, and rapid progression. It not only significantly increases the mortality rate of patients, but also may progress to chronic kidney disease. Currently, its incidence remains high, and improving early diagnosis rate and treatment efficacy is a major clinical challenge. Artificial intelligence (AI), with its powerful data processing and analysis capabilities, is developing rapidly in medical field, providing new ideas for disease diagnosis and treatment, and showing great potential in revolutionizing the early diagnosis, condition assessment, and treatment decision-making models in the AKI field. This article will review the application progress of AI in AKI prediction, condition assessment, and treatment decision-making, so as to provide references for clinicians and promote the further application and development of AI in the AKI field.
In recent years, the computer science represented by artificial intelligence and high-throughput sequencing technology represented by omics play a significant role in the medical field. This paper reviews the research progress of the application of artificial intelligence combined with omics data analysis in the diagnosis and treatment of non-small cell lung cancer (NSCLC), aiming to provide ideas for the development of a more effective artificial intelligence algorithm, and improve the diagnosis rate and prognosis of patients with early NSCLC through a non-invasive way.
Optical coherence tomography (OCT), as a high-resolution, non-invasive, in-vivo image method has been widely used in retinal field, especially in the examination of fundus diseases. Nowadays, the modality has been gradually popularized in most of the national basic-level hospitals. However, OCT is only employed as a diagnostic tool in most cases, ophthalmologists lack of awareness of further exploring the information behind the raw data. In the era of fast-developing artificial intelligence, on the basis of standardized information management, a more comprehensive OCT database should be established. Further original image processing, lesion analysis, and artificial intelligence development of OCT images will help improve the understanding level of vitreoretinal diseases among clinicians and assist ophthalmologists to make more appropriate clinical decisions.
The development of the fifth generation mobile networks (5G) technology has brought great breakthroughs and challenges to clinical medicine and medical education. In the context of “5G + medicine”, the development of telemedicine, emergency rescue, intelligent analysis and diagnosis has opened up new horizons for clinical medicine. Facing the constant impact of high technology, the focus of medical education should be on the cultivation of students’ integrated medical view, critical thinking, communication abilities and skills, and creativity. The “5G + education” model will be presented by means of virtual reality, artificial intelligence, cloud computing and other technologies, providing a new direction for the development of medical education. This article summarizes the key points and prospects of medical education under 5G technology in order to provide a reference for the field of medical education to adapt to the changes in the 5G era.
The rapid development of medical imaging methods based on artificial intelligence (AI) has led to the first release of the AI medical imaging research checklist (CLAIM) in 2020 to promote the completeness and consistency of AI medical imaging research reports. However, during the application process, it was found that some entries in CLAIM needed improvement. Therefore, the expert committee updated CLAIM and released the updated version of CLAIM 2024. This article introduces CLAIM 2024 for domestic scholars to follow up and refer to in a timely manner.
Objective To analyze the hot spot and future application trend of artificial intelligence technology in the field of intensive care medicine. Methods The CNKI, WanFang Data, VIP and Web of Science core collection databases were electronically searched to collect the related literature about the application of artificial intelligence in the field of critical medicine from January 1, 2013 to December 31, 2022. Bibliometrics was used to visually analyze the author, country, research institution, co-cited literature and key words. Results A total of 986 Chinese articles and 4 016 English articles were included. The number of articles published had increased year by year in the past decade, and the top three countries in English literature were China, the United States and Germany. The predictive model and machine learning were the most frequent key words in Chinese and English literature, respectively. Predicting disease progression, mortality and prognosis were the research focus of artificial intelligence in the field of critical medicine. ConclusionThe application of artificial intelligence in the field of critical medicine is on the rise, and the research hotspots are mainly related to monitoring, predicting disease progression, mortality, disease prognosis and the classification of disease phenotypes or subtypes.
ObjectiveTo explore the application of artificial intelligence in postoperative follow-up of day surgery patients, so as to establish an intelligent medical framework, promote the intelligent process of hospitals, and improve the management level of day surgery.MethodsThe artificial intelligence phonetic system was carried out by the Day Surgery Center, Renji Hospital, Shanghai Jiaotong University School of Medicine on June 1st, 2018. Through the system, the artificial intelligence voice system based on speech and semantic recognition technology was adopted to connect the data of the information center in the hospital to carry out postoperative follow-up of day surgery patients. We selected the 2 245 patients followed up by the artificial intelligence phonetic system from June 1st to November 30th 2018 (the AI follow-up group) and the 2 576 patients followed up by the traditional manual method from January 2nd to May 31st 2018 (the manual follow-up group), to compare the telephone connection rate, information collection rate, and call duration between them.ResultsThere was no statistically significant difference in telephone connection rate (85.70% vs. 86.68%) or information collection rate (98.86% vs. 98.48%) between the AI follow-up group and the manual follow-up group (P>0.05); but there was a statistically significant difference in call duration between the AI follow-up group and the manual follow-up group [(165.48±43.28) vs. (135.37±36.31) seconds, P<0.05], and the AI follow-up group had a longer call duration.ConclusionsThe application of artificial intelligence phonetic system in surgery has a good performance in call connection rate and information collection integrity. It plays an active role in improving efficiency, extending medical services and strengthening medical safety in the management of day surgery.
Systematic reviews (SRs) serve as a core methodology in evidence-based medicine (EBM), providing critical evidence for clinical practice and health decision-making. However, the manual screening of titles and abstracts in SRs is labor-intensive and time-consuming, becoming a major bottleneck in research efficiency. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), have introduced new opportunities and transformations in this field. This article provided an overview of the current status of intelligent screening for titles and abstracts in systematic reviews, with a focus on the application and effectiveness of LLMs. It aims to provide recommendations for users and developers, facilitating the better integration of automation algorithms into the SR process.
Currently, the medical imaging methods based on artificial intelligence are developing rapidly, and the related literature reports are increasing year by year. However, there is no special reporting standard, and the reporting of the results is not standardized. In order to improve the report quality of this kind of research and help readers and evaluators evaluate the quality of this kind of research more scientifically, a checklist for artificial intelligence in medical imaging (CLAIM) was put forward abroad. This paper introduces the content of CLAIM and explains its items.