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ChatGPT is a large language models(LLMs)that uses deep learning techniques to produce human-like responses to natural language inputs. It belongs to the family of generative pre-training transformer(GPT)models currently publicly available developed by OpenAI in November 2022. ChatGPT is capable of capturing the nuances and intricacies of human language, generating appropriate and contextually relevant responses. It can assist medical professionals in various tasks, such as research, diagnosis, patient monitoring, and medical education, from identifying research programs to assisting in clinical and laboratory diagnosis, to know new developments in their fields and scientific writing. ChatGPT has also attracted increasing attention and widely used in ophthalmology. However, the use of ChatGPT and other artificial intelligence tools in such tasks comes now with several limitations, ethical and legal concerns, such as credibility, plagiarism, copyright infringement, and biases. Future research can focus on developing new methods to mitigate these limitations while harnessing the benefits of ChatGPT in medicine and related aspects.
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Since the advent of artificial intelligence(AI), it has been increasingly applied and rapidly developed in various fields. In the field of medicine, image features can be automatically extracted and the performance of feature learning and classification can be completed with the help of AI. In the field of ocular fundus disease, AI can give a diagnosis of age-related maculopathy by analyzing and identifying fundus photography and optical coherence tomography with an accuracy rate similar to that of ophthalmologists. In the future, AI may assist physicians in making a diagnosis of age-related macular degeneration, aid basic hospital in screening and curb its progression in the early stage of the disease. However, the technique has problems such as uncertain model recognition performance, opaque operation process, and excessive amount of clinical data required, which still cannot be widely used in the clinic. In recent years, a lot of research has been done in China in the application of deep learning with AI to assist diagnosis of ophthalmic diseases, and the results show that AI combined with imaging analysis of ophthalmic diseases has such characteristics as objectivity, efficiency and accuracy. In this article, studies on deep learning in the auxiliary diagnosis of age-related maculopathy are reviewed, and the progress on its application and the limitations that exist are analyzed, so as to provide more information on the use and extension of AI in this disease.
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Objective:This paper tried to have a dialogue with ChatGPT about ethics review, to understand the degree of intelligence of this application in the field of ethical management, and to analyze its possible impact on the future ethics review work.Methods:The research team sorted out 43 questions, then the research team member at abroad accomplish the dialogue with ChatGPT in both Chinese and English. Feedback answers were summarized and analyzed to explore their advantages and problems.Results:Most of the ChatGPT′s answers of this test were reasonable, with obvious advantages in response speed, and the rigor and friendliness were relatively good. However, there were also problems in consistency, comprehensiveness and expertise, the accuracy and computing power also still have a lot of space for improvement.Conclusions:It is too early for AI to replace professionals, but we should fully develop and utilize the advantages of AI to help professionals get rid of inefficient labor and play a better role.
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O objetivo deste estudo é analisar as condições de trabalho e os seus impactos na saúde dos trabalhadores no mercado de microtarefas de treinamento de dados para a produção de Inteligência Artificial (IA), em especial no que diz respeito a suas relações com a ideologia gerencialista. Os dados são provenientes de uma netnografia realizada entre os anos de 2020 e 2021, de análises dos websites das plataformas e de entrevistas realizadas com 15 trabalhadores. A partir da análise de quatro instâncias mediadoras (econômica, política, ideológica e psicológica), argumentamos que a ideologia gerencialista, consubstanciada a ideologia californiana, se caracteriza como um operador central na gestão do trabalho, que tem por finalidade garantir a adesão dos trabalhadores às plataformas e ocultar os conflitos do trabalho, direcionando-os para o nível individual e produzindo um cenário de individualização do sofrimento.
The objective of this study is to analyze working conditions and their impacts on worker's health in the Artificial Intelligence (AI) data annotation microtask market, especially to highlight their relationship with managerial ideology. The data comes from a netnography carried out between the years 2020 and 2021, from analysis on the platform's websites, and from interviews with 15 workers. Drawing from the analysis of four different mediation systems (economic, political, ideological, and psychological), we argue that the managerial ideology, overlaid with the Californian ideology, is characterized as a central element in the management of labor, which aims to guarantee the adherence of workers to platforms and hide the labor conflicts, directing them to the individual level and producing a scenario of individualization of suffering.
El objetivo de esta investigación es analizar las condiciones de trabajo y sus impactos en la salud de los tra-bajadores en el mercado de microtareas de anotación de datos para la producción de Inteligencia Artificial (IA), en particular en lo que concierne a su relación con la ideología managerial. Los datos provienen de una netnografía realizada entre los años 2020 y 2021, de análisis en los sitios web de las plataformas y de entrevistas con 15 trabajadores. A partir del análisis de cuatro instancias mediadoras (económica, política, ideológica y psicológica), argumentamos que la ideología gerencial, superpuesta en la ideología californi-ana, se caracteriza como un elemento central en la gestión del trabajo, que pretende garantizar la adhesión de los trabajadores a las plataformas y ocultar los conflictos del trabajo, dirigiéndolos al plano individual y produciendo un escenario de individualización del sufrimiento.
Subject(s)
Humans , Occupational Health , Task Performance and Analysis , Artificial Intelligence , Health , Workplace , Conflict, Psychological , Occupational StressABSTRACT
@#Cardiovascular diseases (CVDs) are major disease burdens with high mortality worldwide. Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates of patients with CVDs. The pathological mechanisms and multiple factors involved in CVDs are complex; thus, traditional data analysis is insufficient and inefficient to manage multidimensional data for the risk prediction of CVDs and heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis prediction. Meanwhile, traditional Chinese medicine (TCM) has been widely used for treating CVDs. TCM offers unique theoretical and practical applications in the diagnosis and treatment of CVDs. Big data have been generated to investigate the scientific basis of TCM diagnostic methods. TCM formulae contain multiple herbal items. Elucidating the complicated interactions between the active compounds and network modulations requires advanced data-analysis capability. Recent progress in artificial intelligence (AI) technology has allowed these challenges to be resolved, which significantly facilitates the development of integrative diagnostic and therapeutic strategies for CVDs and the understanding of the therapeutic principles of TCM formulae. Herein, we briefly introduce the basic concept and current progress of AI and machine learning (ML) technology, and summarize the applications of advanced AI and ML for the diagnosis and treatment of CVDs. Furthermore, we review the progress of AI and ML technology for investigating the scientific basis of TCM diagnosis and treatment for CVDs. We expect the application of AI and ML technology to promote synergy between western medicine and TCM, which can then boost the development of integrative medicine for the diagnosis and treatment of CVDs.
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@#Objective To explore the efficacy of artificial intelligence (AI) detection on pulmonary nodule compared with multidisciplinary team (MDT) in regional medical center. Methods We retrospectively analyzed the clinical data of 102 patients with lung nodules in the Xiamen Fifth Hospital from April to December 2020. There were 57 males and 45 females at age of 36-90 (48.8±11.6) years. The preoperative chest CT was imported into AI system to record the detected lung nodules. The detection rate of pulmonary nodules by AI system was calculated, and the sensitivity, specificity of AI in the different diagnosis of benign and malignant pulmonary was calculated and compared with manual film reading by MDT. Results A total of 322 nodules were detected by AI software system, and 305 nodules were manually detected by physicians (P<0.05). Among them, 113 pulmonary nodules were diagnosed by pathologist. Thirty-eight of 40 lung cancer nodules were AI high-risk nodules, the sensitivity was 95.0%, and 25 of 73 benign nodules were AI high-risk nodules, the specificity was 65.8%. Lung cancer nodules were correctly diagnosed by MDT, but benign nodules were still considered as lung cancer at the first diagnosis in 10 patients. Conclusion AI assisted diagnosis system has strong performance in the detection of pulmonary nodules, but it can not content itself with clinical needs in the differentiation of benign and malignant pulmonary nodules. The artificial intelligence system can be used as an auxiliary tool for MDT to detect pulmonary nodules in regional medical center.
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@#As an emerging technology, artificial intelligence (AI) uses human theory and technology for robots to study, develop, learn and identify human technologies. Thoracic surgeons should be aware of new opportunities that may affect their daily practice by the direct use of AI technology, or indirect use in the relevant medical fields (radiology, pathology, and respiratory medicine). The purpose of this paper is to review the application status and future development of AI associated with thoracic surgery, diagnosis of AI-related lung cancer, prognosis-assisted decision-making programs and robotic surgery. While AI technology has made rapid progress in many areas, the medical industry only accounts for a small part of AI use, and AI technology is gradually becoming widespread in the diagnosis, treatment, rehabilitation, and care of diseases. The future of AI is bright and full of innovative perspectives. The field of thoracic surgery has conducted valuable exploration and practice on AI, and will receive more and more influence and promotion from AI.
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The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis (ARN). The potential application of artificial intelligence (AI) algorithms in these areas of clinical research has not been reported previously. The present study aims to create a computational algorithm for the automated detection and evaluation of retinal necrosis from retinal fundus photographs. A total of 149 wide-angle fundus photographs from 40 eyes of 32 ARN patients were collected, and the U-Net method was used to construct the AI algorithm. Thereby, a novel algorithm based on deep machine learning in detection and evaluation of retinal necrosis was constructed for the first time. This algorithm had an area under the receiver operating curve of 0.92, with 86% sensitivity and 88% specificity in the detection of retinal necrosis. For the purpose of retinal necrosis evaluation, necrotic areas calculated by the AI algorithm were significantly positively correlated with viral load in aqueous humor samples (
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Objective: To explore the value support of medical records-structured specialized disease database established by using unstructured electronic medical record text information in clinical research. Methods: The information of patients who were admitted to a Grade A specialist hospital in Shanghai from Oct. 2007 to Sept. 2019 were collected. By using artificial intelligence (AI) engine and other information methods, the electronic medical record text information were structured into a structured database, and compared with the traditional structured database. Results: The information of 82 584 patients were collected, and the structured number of hospital records was 253 000. The specialized disease databases of lung cancer, esophageal cancer and mediastinal tumor were established. Compared with the traditional structured database, the specialized disease database expanded the scope of data retrieval and improved the efficiency of data retrieval. Conclusion: The construction of medical records-structured specialized disease database based on AI reduces the burden of clinician data retrieval, and provides valuable statistical data for clinical research.
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Early detection and accurate diagnosis are critical for the prognosis of lung cancer. Radiological imaging could reflect tumor heterogeneity in a non-invasive and comprehensive manner. Deep mining of high throughput imaging data is a big challenge for radiologists. Artificial intelligence (AI) methods excel at processing large quantities of high-dimensional information and analyzing data using algorithm. It can automatically recognize complex patterns in imaging data, provide quantitative assessments of radiographic characteristics, and is promising in tumor detection and diagnosis. Precision medicine could be made when AI was integrated into the clinical workflow as a tool to assist radiologists. Here we review the current progress and discuss the challenges and future directions of AI applications in lung tumor imaging diagnosis.