2024年 · 第04卷第01期

With the continuous improvement and development of modern network information technology and the continuous improvement of people’s demands for health care, the traditional health care model has evolved, giving birth to a new telemedicine health care model. Telemedicine refers to the comprehensive application of information technology for medical information transmission and long-distance communication between different places. It integrates medicine, computer technology, and communication technology for remote monitoring, diagnosis, consultation, case discussion, teaching, and surgery as well as a series of medical activities. With the continuous development of communication technology, telemedicine is also constantly changing. As a relatively novel technology, telemedicine is sought after by major hospitals. With the advancement of internet technology, digitization and informatization have been gradually applied in telemedicine, but due to various factors, telemedicine still has great limitations. This paper summarized the development status of telemedicine; discussed in detail the development of telemedicine at home and abroad; reviewed the application of telemedicine as well as the feasibility and limitations of its promotion and development; and put forward an outlook for the future development of telemedicine.

Millimeter waves are electromagnetic waves with wavelengths of 1–10 mm, which have characteristics of high frequency and short wavelength. They have gradually and widely been used in engineering and medical fields. We have identified studies related to millimeter waves in the biomedical field and summarized the biological effects of millimeter waves and their current status in medical applications. Finally, the shortcomings of existing studies and future developments were analyzed and discussed, with the aim of providing a reference for further research and development of millimeter waves in the medical field.

One concern about the application of medical artificial intelligence (AI) regards the "black box" feature which can only be viewed in terms of its inputs and outputs, with no way to understand the AI’s algorithm. This is problematic because patients, physicians, and even designers, do not understand why or how a treatment recommendation is produced by AI technologies. One view claims that the worry about black-box medicine is unreasonable because AI systems outperform human doctors in identifying the disease. Furthermore, under the medical AI-physician-patient model, the physician can undertake the responsibility of interpreting the medical AI’s diagnosis. In this study, we focus on the potential harm caused by the unexplainability feature of medical AI and try to show that such possible harm is underestimated. We will seek to contribute to the literature from three aspects. First, we appealed to a thought experiment to show that although the medical AI systems perform better on accuracy, the harm caused by medical AI’s misdiagnoses may be more serious than that caused by human doctors’ misdiagnoses in some cases. Second, in patient-centered medicine, physicians were obligated to provide adequate information to their patients in medical decision-making. However, the unexplainability feature of medical AI systems would limit the patient’s autonomy. Last, we tried to illustrate the psychological and financial burdens that may be caused by the unexplainablity feature of medical AI systems, which seems to be ignored by the previous ethical discussions.

Appropriate medical imaging is important for value-based care. We aim to evaluate the performance of generative pretrained transformer 4 (GPT-4), an innovative natural language processing model, providing appropriate medical imaging automatically in different clinical scenarios.

Institutional Review Boards (IRB) approval was not required due to the use of nonidentifiable data. Instead, we used 112 questions from the American College of Radiology (ACR) Radiology-TEACHES Program as prompts, which is an open-sourced question and answer program to guide appropriate medical imaging. We included 69 free-text case vignettes and 43 simplified cases. For the performance evaluation of GPT-4 and GPT-3.5, we considered the recommendations of ACR guidelines as the gold standard, and then three radiologists analyzed the consistency of the responses from the GPT models with those of the ACR. We set a five-score criterion for the evaluation of the consistency. A paired t-test was applied to assess the statistical significance of the findings.

For the performance of the GPT models in free-text case vignettes, the accuracy of GPT-4 was 92.9%, whereas the accuracy of GPT-3.5 was just 78.3%. GPT-4 can provide more appropriate suggestions to reduce the overutilization of medical imaging than GPT-3.5 (t = 3.429, P = 0.001). For the performance of the GPT models in simplified scenarios, the accuracy of GPT-4 and GPT-3.5 was 66.5% and 60.0%, respectively. The differences were not statistically significant (t = 1.858, P = 0.070). GPT-4 was characterized by longer reaction times (27.1 s in average) and extensive responses (137.1 words on average) than GPT-3.5.

As an advanced tool for improving value-based healthcare in clinics, GPT-4 may guide appropriate medical imaging accurately and efficiently.

Diabetes mellitus is a serious disease where the body of affected patients are failed to produce enough insulin that causes an abnormality of blood sugar. This disease happens for a number of reasons including modern lifestyle, lethargic attitude, unhealthy food consumption, family history, age, overweight, etc. The aim of this study was to propose a machine learning based prediction model that detected diabetes at the beginning.

In this work, we collected 520 patients records from the University of California, Irvine (UCI) machine learning repository of Sylhet Diabetes Hospital, Sylhet. Then, a similar questionnaire of that hospital was followed and assembled 558 patients records from all over Bangladesh through this questionnaire. However, we accumulated patient records of these two datasets. In the next step, these datasets were cleaned and applied thirty five state-of-arts classifiers such as logistic regression (LR), K nearest neighbors (KNN), support vector classifier (SVC), Nave Byes (NB), decision tree (DT), random forest (RF), stochastic gradient descent (SGD), Perceptron, AdaBoost, XGBoost, passive aggressive classifier (PAC), ridge classifier (RC), Nu-support vector classifier (Nu-SVC), linear support vector classifier (LSVC), calibrated classifier CV (CCCV), nearest centroid (NC), Gaussian process classifier (GPC), multinomial NB (MNB), complement NB, Bernoulli NB (BNB), categorical NB, Bagging, extra tree(ET), gradiant boosting classifier (GBC), Hist gradiant boosting classifier (HGBC), one vs rest classifier (OVsRC), multi-layer perceptron (MLP), label propagation (LP), label spreading (LS), stacking, ridge classifier CV (RCCV), logistic regression CV (LRCV), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and light gradient boosting machine (LGBM) to explore best stable predictive model. The performance of the classifiers has been measured using five metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic. Finally, these outcomes were interpreted using Shapley additive explanations methods and identified relevant features for happening diabetes.

In this work, different classifiers were shown their performance where ET outperformed any other classifiers with 97.11% accuracy for the Sylhet Diabetes Hospital dataset (SDHD) and MLP shows the best accuracy (96.42%) for the collected dataset. Subsequently, HGBC and LGBM provide the highest 94.90% accuracy for the combined datasets individually.

LGBM, stacking, HGBC, RF, ET, bagging, and GBC might represent more stable prediction results for each dataset.

We created and validated a computed tomography (CT)-based radiomic model using both clinical factors and the radiomic signature for assessing the strangulation risk of acute intestinal obstruction. This would assist surgeons in accurately predicting intestinal ischemia and strangulation in patients with intestinal obstruction.

We recruited 289 patients with acute intestinal obstruction admitted in the Affiliated Hospital of Qingdao University from January 2019 to February 2022. The patients were allocated to a training (n = 226) and validation cohort (n = 63). Radiomic features were collected from CT images, and the radiomic signature was extracted and used to calculate a radiomic score (Rad-score). A nomogram was constructed using the clinical features and the Rad-score, and the performance of the clinical, radiomics, and nomogram models was assessed in the two cohorts.

Six robust features were used to construct the radiomic signature. The nomogram incorporating hemoglobin levels, C-reactive protein levels, American Society of Anesthesiologists score, time of obstruction, CT image of mesenteric fluid (P < 0.05), and the signature demonstrated good predictive ability for intestinal ischemia in patients with acute intestinal obstruction, with areas under the curve of 0.892 (95% confidence interval, 0.837–0.947) and 0.781 (95% confidence interval, 0.619–0.944) for the training and validation sets, respectively. The decision curve analysis showed that this model outperformed the clinical and radiomic signature models.

The radiomic nomogram may effectively predict intestinal ischemia in patients with acute intestinal disease and may assist clinical decision-making.

Colorectal cancer (CRC) is the second leading cause of cancer fatalities and the third most common human disease. Identifying molecular subgroups of CRC and treating patients accordingly could result in better therapeutic success compared with treating all CRC patients similarly. Studies have highlighted the significance of CRC as a major cause of mortality worldwide and the potential benefits of identifying molecular subtypes to tailor treatment strategies and improve patient outcomes.

This study proposed an unsupervised learning approach using hierarchical clustering and feature selection to identify molecular subtypes and compares its performance with that of conventional methods. The proposed model contained gene expression data from CRC patients obtained from Kaggle and used dimension reduction techniques followed by Z-score-based outlier removal. Agglomerative hierarchy clustering was used to identify molecular subtypes, with a P-value-based approach for feature selection. The performance of the model was evaluated using various classifiers including multilayer perceptron (MLP).

The proposed methodology outperformed conventional methods, with the MLP classifier achieving the highest accuracy of 89% after feature selection. The model successfully identified molecular subtypes of CRC and differentiated between different subtypes based on their gene expression profiles.

This method could aid in developing tailored therapeutic strategies for CRC patients, although there is a need for further validation and evaluation of its clinical significance.