Objective To explore the use of ChatGPT (Chat Generative Pre-trained Transformer) in pediatric diagnosis, treatment and doctor-patient communication, evaluate the professionalism and accuracy of the medical advice provided, and assess its ability to provide psychological support. Methods The knowledge databases of ChatGPT 3.5 and 4.0 versions as of April 2023 were selected. A total of 30 diagnosis and treatment questions and 10 doctor-patient communication questions regarding the pediatric urinary system were submitted to ChatGPT versions 3.5 and 4.0, and the answers to ChatGPT were evaluated. Results The answers to the 40 questions answered by ChatGPT versions 3.5 and 4.0 all reached the qualified level. The answers to 30 diagnostic and treatment questions in ChatGPT 4.0 version were superior to those in ChatGPT 3.5 version (P=0.024). There was no statistically significant difference in the answers to the 10 doctor-patient communication questions answered by ChatGPT 3.5 and 4.0 versions (P=0.727). For prevention, single symptom, and disease diagnosis and treatment questions, ChatGPT’s answer scores were relatively high. For questions related to the diagnosis and treatment of complex medical conditions, ChatGPT’s answer scores were relatively low. Conclusion ChatGPT has certain value in assisting pediatric diagnosis, treatment and doctor-patient communication, but the medical advice provided by ChatGPT cannot completely replace the professional judgment and personal care of doctors.
In continuous renal replacement therapy (CRRT), the combination of medicine and engineering is propelling advancements in therapeutic technology. By enhancing the biocompatibility and specific adsorption capabilities of the blood adsorption materials, the therapeutic efficacy of CRRT is augmented, leading to a reduction in adverse reactions for patients. Moreover, the application of big data and artificial intelligence in CRRT is continually being developed. Utilizing intelligent devices, data analysis, and machine learning, the initiation, monitoring, and formulation of CRRT treatment plans are optimized, providing clinical patients with more efficient and secure therapeutic options, thereby further improving clinical outcomes.
In order to further regulate the application of 3D reconstruction in thoracic surgery, the Chinese Expert Consensus Group on the Application of Integrated 3D Reconstruction with Artificial Intelligence in Thoracic Surgery conducted discussions and developed this consensus. This consensus is based on the clinical experience and existing prospective or retrospective studies of 3D reconstruction technology in various scenarios of thoracic surgery and summarizes recommendations, and also appends a list of 3D reconstruction technology application scenarios that are currently controversial, not fully studied, or still in the exploratory stage, to provide direction and evidence for future clinical research and disease diagnosis and treatment, and to reach a consensus.
This article is based on the work practice of Deyang People’s Hospital in carrying out financial digital transformation under the background of artificial intelligence technology. It clarifies the concepts of financial digitization and artificial intelligence technology, summarizes the practical path of hospital financial digital transformation, and analyzes the specific applications and implementation effects of intelligent filling of expense reimbursement forms, intelligent review of documents, and intelligent management of medical insurance funds. These experiences have positive significance for optimizing financial business processes, improving data quality and utilization efficiency, and enhancing employee satisfaction. They can provide a reference for the digital transformation of financial management in public hospitals and the reconstruction of the value positioning of hospital financial management.
ObjectiveTo evaluate the application value of three-dimensional (3D) reconstruction in preoperative surgical diagnosis of new classification criteria for lung adenocarcinoma, which is helpful to develop a deep learning model of artificial intelligence in the auxiliary diagnosis and treatment of lung cancer.MethodsThe clinical data of 173 patients with ground-glass lung nodules with a diameter of ≤2 cm, who were admitted from October 2018 to June 2020 in our hospital were retrospectively analyzed. Among them, 55 were males and 118 were females with a median age of 61 (28-82) years. Pulmonary nodules in different parts of the same patient were treated as independent events, and a total of 181 subjects were included. According to the new classification criteria of pathological types, they were divided into pre-invasive lesions (atypical adenomatous hyperplasia and and adenocarcinoma in situ), minimally invasive adenocarcinoma and invasive adenocarcinoma. The relationship between 3D reconstruction parameters and different pathological subtypes of lung adenocarcinoma, and their diagnostic values were analyzed by multiplanar reconstruction and volume reconstruction techniques.ResultsIn different pathological types of lung adenocarcinoma, the diameter of lung nodules (P<0.001), average CT value (P<0.001), consolidation/tumor ratio (CTR, P<0.001), type of nodules (P<0.001), nodular morphology (P<0.001), pleural indenlation sign (P<0.001), air bronchogram sign (P=0.010), vascular access inside the nodule (P=0.005), TNM staging (P<0.001) were significantly different, while nodule growth sites were not (P=0.054). At the same time, it was also found that with the increased invasiveness of different pathological subtypes of lung adenocarcinoma, the proportion of dominant signs of each group gradually increased. Meanwhile, nodule diameter and the average CT value or CTR were independent risk factors for malignant degree of lung adenocarcinoma.ConclusionImaging signs of lung adenocarcinoma in 3D reconstruction, including nodule diameter, the average CT value, CTR, shape, type, vascular access conditions, air bronchogram sign, pleural indenlation sign, play an important role in the diagnosis of lung adenocarcinoma subtype and can provide guidance for personalized therapy to patients in clinics.
Objective To systematically evaluate the diagnostic value of artificial intelligence assisted narrow-band imaging endoscopy diagnostic system for colorectal adenomatous polyps. Methods Pubmed, Embase, Web of Science, Cochrane Library, SinoMed, China National Knowledge Infrastructure, Chongqing VIP and Wanfang databases were searched. The diagnostic trials of the artificial intelligence assisted narrow-band imaging endoscopy diagnostic system for colorectal adenomatous polyps were comprehensively searched. The search time limit was from January 1, 2000 to October 31, 2022. The included studies were evaluated according to the Quality Assessment of Diagnostic Accuracy Studies-2, and the data were meta-analysed with RevMan 5.3, Meta-Disc 1.4 and Stata 13.0 statistical softwares. Results Finally, 11 articles were included, including 2178 patients. Meta-analysis results of the artificial intelligence assisted narrow-band imaging endoscopy diagnostic system for colorectal adenomatous polyps showed that the pooled sensitivity was 0.91, the pooled specificity was 0.88, the pooled positive likelihood ratio was 7.41, the pooled negative likelihood ratio was 0.10, the pooled diagnostic odds ratio was 76.45, and the area under the summary receiver operating characteristic curve was 0.957. Among them, 5 articles reported the diagnosis of small adenomatous polyps (diameter <5 mm) by the artificial intelligence assisted narrow-band imaging endoscopy diagnostic system. The results showed that the pooled sensitivity and the pooled specificity were 0.93 and 0.91, respectively, and the area under the summary receiver operating characteristic curve was 0.971. Five articles reported the accuracy of endoscopic diagnosis for adenomatous polyps of those with insufficient experience. The results showed that the pooled sensitivity and the pooled specificity were 0.84 and 0.76, respectively. The area under the summary receiver operating characteristic curve was 0.848. Compared with the artificial intelligence assisted narrow-band imaging endoscopy diagnostic system, the difference was statistically significant (Z=1.979, P=0.048). Conclusion The artificial intelligence assisted narrow-band imaging endoscopy diagnostic system has a high diagnostic accuracy, which can significantly improve the diagnostic accuracy for colorectal adenomatous polyps of those with insufficient endoscopic experience, and can effectively compensate for the adverse impact of their lack of endoscopic experience.
Pathological diagnosis is the gold standard for confirming breast cancer. Traditional manual pathological diagnosis methods for breast cancer are time-consuming, labor-intensive, highly subjective, and exhibit poor diagnostic consistency. In recent years, artificial intelligence (AI) technology has rapidly advanced and is progressively being applied clinically as a promising early diagnostic tool. However, many existing AI models lack interpretability, which limits the trustworthiness of their clinical application. Khater et al, by combining a high-precision machine learning model with an explainable AI model, achieved highly accurate breast tumor diagnosis and provided explanations for key biological and pathological features influencing the diagnostic results. This points the way for the future application and development of AI in medical diagnosis and treatment. The article interprets the main content of that study, and analyzes the advantages and limitations of AI in medical diagnosis and treatment, with the aim of promoting its better application in clinical practice.
ObjectiveTo better understand artificial intelligence (AI) and its application in management of liver cancer.MethodThe relevant literatures about AI in the diagnosis and treatment of liver cancer in recent years were reviewed.ResultsIn terms of diagnosis, the deep learning could precisely and quickly complete the imaging localization and segmentation of the liver, which was helpful for the diagnosis, while radiomics had a high value in assisting the diagnosis of liver cancer, predicting the postoperative recurrence and long-term survival of patients with liver cancer. In regard of treatment, although it was still difficult for AI to intervene in liver surgery, it had significant advantages in formulating individualized operation scheme for patients with liver cancer, which enabled precise hepatectomy and was helpful for prediction of intraoperative bleeding. AI fusion imaging could provide assistance in operation plan making and realize the precise placement of ablation needle. AI was able to predict the tumor response or even tumor progression after interventional therapy and radiotherapy. Pathological analysis was also facilitated by AI and was able to identify some details and feature textures that were difficult to manually distinguish. For transplantation, guidance of AI on the allocation of donor livers based on hazards models helped make better use of limited organ resources. AI could be applied in prognosis prediction in almost all treatment modalities.ConclusionsAI provides more efficient and precise diagnosis, treatment support and prognosis than conventional medical process in liver cancer, generally by constructing a fully functional model based on a series of data mining methods combined with statistical analysis.
With the widespread adoption of lung cancer screening, an increasing number of patients are being diagnosed with early-stage lung adenocarcinoma. For stage ⅠA lung adenocarcinoma, sublobar resection is the primary treatment approach. However, in patients with concomitant spread through air space (STAS), numerous studies advocate for lobectomy as the mainstay of treatment. Due to the limitations in preoperative prediction and intraoperative frozen section evaluation for assessing STAS, current research is largely restricted to using clinical and imaging features to predict STAS occurrence, with results that are inconsistent and unsatisfactory. Furthermore, most studies focus on individual clinical or imaging characteristics, and there is a lack of large-sample investigations. The rise of artificial intelligence in recent years has provided new insights into solving this problem, and existing studies have shown that artificial intelligence demonstrates better performance in STAS prediction compared to conventional methods. This article reviews the value of artificial intelligence in predicting STAS.
ObjectiveTo evaluate the value of imaging quantification parameters in artificial intelligence (AI) assisted diagnosis systems in clinical decision-making for lung nodules≤2 cm and the diagnostic efficacy of AI. MethodsLung nodule patients admitted to Affiliated Zhongshan Hospital of Dalian University from 2020 to 2023 were included. Imaging parameters of lung nodules were extracted using AI assisted diagnosis systems. Multifactor analysis was used to screen predictors for distinguishing benign and malignant nodules and high-risk predictors for recurrent invasive adenocarcinoma, and a diagnostic model was established and its performance evaluated. The diagnostic efficacy of the AI system was judged according to pathological results. ResultsA total of 594 patients with lung nodules were included, including 202 males and 392 females, with an average age of (58.75±11.55) years. Volume, average CT value, and 3D maximum diameter of non-solid nodules were independent predictors of malignant nodules, with thresholds of 287.4 mm3, ?491 HU, and 12.0 mm, respectively. The area under the curve (AUC) for diagnostic efficacy was ranked from high to low as combined model (0.802), volume (0.783), average CT value (0.749), and 3D maximum diameter (0.714). The average CT value and 3D long diameter of solid nodules were independent predictors of malignant nodules, with thresholds of ?81 HU and 17.5 mm, respectively, and AUC values of 0.874 and 0.686, respectively, with the combined prediction AUC of 0.957. The mass of cystic nodules was an independent predictor of malignancy when the mass>180.7 mg. Independent predictors of high recurrence risk of invasive adenocarcinoma in non-solid nodules were consolidation-tumor ratio (CTR), average CT value, 3D long diameter, and volume, with thresholds of 0.14, ?386 HU, 15.6 mm, and 1018.9 mm3, respectively, and diagnostic efficacy was ranked from high to low as combined model (0.788), 3D long diameter (0.735), volume (0.725), average CT value (0.720), and CTR (0.697). The accuracy of AI in predicting benign and malignant target nodules was 87.4%, with positive predictive value of 96.6% and negative predictive value of 58.9%. ConclusionIn clinical decision-making for lung nodules ≤2 cm, AI assisted diagnosis systems have high application value.