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Harini Sriraman, Saleena Badarudeen, Saransh Vats, Prakash Balasubramanian School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, IndiaCorrespondence: Prakash Balasubramanian, School of Computer Science Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India, Tel +91-044-39931228, Fax +91-044-39932555, Email Prakash.bala@vit.ac.inAbstract: Deep Learning (DL) drives academics to create models for cancer diagnosis using medical image processing because of its innate ability to recognize difficult-to-detect patterns in complex, noisy, and massive data. The use of deep learning algorithms for real-time cancer diagnosis is explored in depth in this work. Real-time medical diagnosis determines the illness or condition that accounts for a patient’s symptoms and outward physical manifestations within a predetermined time frame. With a waiting period of anywhere between 5 days and 30 days, there are currently several ways, including screening tests, biopsies, and other prospective methods, that can assist in discovering a problem, particularly cancer. This article conducts a thorough literature review to understand how DL affects the length of this waiting period. In addition, the accuracy and turnaround time of different imaging modalities is evaluated with DL-based cancer diagnosis. Convolutional neural networks are critical for real-time cancer diagnosis, with models achieving up to 99.3% accuracy. The effectiveness and cost of the infrastructure required for real-time image-based medical diagnostics are evaluated. According to the report, generalization problems, data variability, and explainable DL are some of the most significant barriers to using DL in clinical trials. Making DL applicable for cancer diagnosis will be made possible by explainable DL.Keywords: artificial intelligence, AI, machine learning, DL, CNN, healthcare, real-time diagnosis, classification, image processing, elastography, feedforward neural network

Prediction of the stock market is a challenging and time-consuming process. In recent times, various research analysts and organizations have used different tools and techniques to analyze and predict stock price movements. During the early days, investors mainly depend on technical indicators and fundamental parameters for short-term and long-term predictions, whereas nowadays many researchers started adopting artificial intelligence-based methodologies to predict stock price movements. In this article, an exhaustive literature study has been carried out to understand multiple techniques employed for prediction in the field of the financial market. As part of this study, more than hundreds of research articles focused on global indices and stock prices were collected and analyzed from multiple sources. Further, this study helps the researchers and investors to make a collective decision and choose the appropriate model for better profit and investment based on local and global market conditions., Competing Interests: The authors declare that they have no competing interests., (© 2024 Balasubramanian et al.)

Introduction: The diagnosis of urinary tract infections includes microbiologic culture of urine to determine the etiology of the infection. However, interpretation of the results can be confounded by various factors including the accuracy of a patient's history of current antibiotic usage. , Methodology: In this report, we tested urine specimens for the presence of antibiotics and compared our results to the accuracy of antibiotic data entry on the accompanying request forms. In addition, the consequences of culturing urine specimens with incomplete antibiotic history received in the laboratory were investigated., Results: During the study period, 14,680 urines were obtained and tested with a modified urine antibacterial substance assay (UABA). There were (97.32%) true-negative, 6 (0.04%) false-negative, 222 (1.51%) true-positive and 166 (1.13%) false-positive results. The sensitivity and specificity of this test was 97.37% and 98.85% respectively., Conclusion: This internal audit practice demonstrates the importance of accurately completed request forms and how this information impacts the clinical interpretation of urine culture results.

Background: Health literacy is the single best predictor of an individual's health status. It is important to customize health-related education material to the individual patient's level of reading skills. Readability of a given text is the objective measurement of the reading skills one should possess to understand the written material., Questions/purposes: In this article, some of the commonly used readability assessment tools are discussed and guidelines to improve the comprehension of patient education handouts are provided. Where are we now? Several healthcare organizations have recommended the readability of patient education materials be no higher than sixth- to eighth-grade level. However, most of the patient education materials currently available on major orthopaedic Web sites are written at a reading level that may be too advanced for comprehension by a substantial proportion of the population. WHERE DO WE NEED TO GO?: There are several readily available and validated tools for assessing the readability of written materials. While use of audiovisual aids such as video clips, line drawings, models, and charts can enhance the comprehension of a health-related topic, standard readability tools cannot construe such enhancements. HOW DO WE GET THERE?: Given the variability in the capacity to comprehend health-related materials among individuals seeking orthopaedic care, stratifying the contents of patient education materials at different levels of complexity will likely improve health literacy and enhance patient-centered communication.

One of the goals of the American Academy of Orthopaedic Surgeons (AAOS) is to disseminate patient education materials that suit the readability skills of the patient population. According to standard guidelines from healthcare organizations, the readability of patient education materials should be no higher than the sixth-grade level. We hypothesized the readability level of patient education materials available on the AAOS Web site would be higher than the recommended grade level, regardless when the material was available online. Readability scores of all articles from the AAOS Internet-based patient information Web site, "Your Orthopaedic Connection," were determined using the Flesch-Kincaid grade formula. The mean Flesch-Kincaid grade level of the 426 unique articles was 10.43. Only 10 (2%) of the articles had the recommended readability level of sixth grade or lower. The readability of the articles did not change with time. Our findings suggest the majority of the patient education materials available on the AAOS Web site had readability scores that may be too difficult for comprehension by a substantial portion of the patient population.