Artificial intelligence (AI) is a new technical discipline that uses computer technology to research and develop the theory, method, technique, and application system for the simulation, extension, and expansion of human intelligence. With the assistance of new AI technology, the traditional medical environment has changed a lot. For example, a patient’s diagnosis based on radiological, pathological, endoscopic, ultrasonographic, and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload. The medical treatments during the perioperative period, including the preoperative preparation, surgical period, and postoperative recovery period, have been significantly enhanced with better surgical effects. In addition, AI technology has also played a crucial role in medical drug production, medical management, and medical education, taking them into a new direction. The purpose of this review is to introduce the application of AI in medicine and to provide an outlook of future trends.

Objectives: To explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method.Methods: A total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals.Results: The deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group.Conclusion: This deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency.

Objective: Explore a new deep learning (DL) object detection algorithm for clinical auxiliary diagnosis of lumbar spondylolisthesis and compare it with doctors’ evaluation to verify the effectiveness and feasibility of the DL algorithm in the diagnosis of lumbar spondylolisthesis.Methods: Lumbar lateral radiographs of 1,596 patients with lumbar spondylolisthesis from three medical institutions were collected, and senior orthopedic surgeons and radiologists jointly diagnosed and marked them to establish a database. These radiographs were randomly divided into a training set (n = 1,117), a validation set (n = 240), and a test set (n = 239) in a ratio of 0.7 : 0.15: 0.15. We trained two DL models for automatic detection of spondylolisthesis and evaluated their diagnostic performance by PR curves, areas under the curve, precision, recall, F1-score. Then we chose the model with better performance and compared its results with professionals’ evaluation.Results: A total of 1,780 annotations were marked for training (1,242), validation (263), and test (275). The Faster Region-based Convolutional Neural Network (R-CNN) showed better precision (0.935), recall (0.935), and F1-score (0.935) in the detection of spondylolisthesis, which outperformed the doctor group with precision (0.927), recall (0.892), f1-score (0.910). In addition, with the assistance of the DL model, the precision of the doctor group increased by 4.8%, the recall by 8.2%, the F1-score by 6.4%, and the average diagnosis time per plain X-ray was shortened by 7.139 s.Conclusion: The DL detection algorithm is an effective method for clinical diagnosis of lumbar spondylolisthesis. It can be used as an assistant expert to improve the accuracy of lumbar spondylolisthesis diagnosis and reduce the clinical workloads.

Ischemic stroke is caused by cerebral ischemia upon the blockage of an artery, which results in a high disability rate. Little is known regarding the mechanism of astrocyte function in cerebral ischemia. We aimed to determine the effects of running on the transformation of astrocytes, and subsequent synapse formation. A study of middle cerebral artery occlusion (MCAO) after running in vivo showed that running can promote the transformation of astrocytes toward the neuroprotective phenotype. Our findings of oxygen-glucose deprived astrocytes in vitro after running revealed that these astrocytes transformed into the neuroprotective phenotype, and that the expression of STAT3 and Gpc6 was increased. We confirmed that mechanistically, running can target Gpc6 through the STAT3 pathway and then regulate the number of synapses. We concluded that running promotes synapse proliferation by polarizing astrocytes toward the neuroprotective phenotype and ultimately leads to nerve regeneration.

The biosynthesis of metal nanoparticles by plants, bacteria, and cells has been receiving considerable attention in recent years. The traditional synthesis of metal nanoparticles always needed high temperatures, high pressure, and toxic agents. However, the biosynthesis process (including bioreduction and biomineralization) is simpler, safe, economical, and green. The process of biosynthesis can insulate toxic agents, streamline flux, increase the transition efficiency of interactants, and improve the product yield. The biosynthesized metal nanoparticles share similar characteristics with traditional ones, serving as photosensors to achieve light-to-heat/energy transduction, or a drug delivery system. The biosynthetic metal nanoparticles thus could be widely applied in the medical field for disease diagnosis and treatment. It contributed a novel modality for the facile and green synthesis of metal nanoparticles. Increasing studies have been exploring the mechanism for the biosynthesis of metal nanoparticles, devoted to a controllable biosynthesis process. Combined with our previous studies on the biosynthesis of gold nanoparticles with green tea, tumor cells, and cell components, we reviewed the green methods of bioreduction and biomineralization of metal nanoparticles including the internal mechanism, aimed to make a comprehensive introduction to the biosynthesis of metal nanoparticles and relevant biomedical applications, and inspired further research.