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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

» Generative artificial intelligence (AI), a rapidly evolving field, has the potential to revolutionize orthopedic care by enhancing diagnostic accuracy, treatment planning, and patient management through data-driven insights and personalized strategies.» Unlike traditional AI, generative AI has the potential to generate relevant information for orthopaedic surgeons when instructed through prompts, automating tasks such as literature reviews, streamlining workflows, predicting health outcomes, and improving patient interactions.» Prompt engineering is essential for crafting effective prompts for large language models (LLMs), ensuring accurate and reliable AI-generated outputs, and promoting ethical decision-making in clinical settings.» Orthopaedic surgeons can choose between various prompt types-including open-ended, focused, and choice-based prompts-to tailor AI responses for specific clinical tasks to enhance the precision and utility of generated information.» Understanding the limitations of LLMs, such as token limits, context windows, and hallucinations, is crucial for orthopaedic surgeons to effectively use generative AI while addressing ethical concerns related to bias, privacy, and accountability., Competing Interests: Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJSREV/B163)., (Copyright © 2024 by The Journal of Bone and Joint Surgery, Incorporated.)

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.)

Aim: Amidst COVID-19 pandemic, the health care delivery in India faces major challenges owing to the overwhelming hospitals, exhausted healthcare workers, and shortage of crucial medical supplies such as ventilators and oxygen. The study aims to propose a novel successful interventional home care model, the Virtual COVID In-Patient (VCIP) care for effective COVID management., Methods: The Covid-19 positive patients enrolled in VCIP were chosen for the study. A 24/7 active multidisciplinary WhatsApp group was created for each patient, for remote monitoring of temperature, blood pressure, blood glucose, respiratory and pulse rate along with the symptoms. Advice on sleep and exercises were given along with the medication via video-audio consultations. Lab facility was provided at the doorstep. Training on various devices, medications including steroids, delivering subcutaneous injections etc were given via video platforms., Results: Among the 220 patients who availed the VCIP facility, only two were hospitalized, yielding a 99.5 % success rate in preventing hospitalizations and patients enrolled have been immensely satisfied with their experience., Conclusions: With similar pandemics anticipated in near future, VCIP model may be considered for successful domiciliary treatment and overcoming the challenges., (Copyright © 2021 Diabetes India. All rights reserved.)

Background: The purpose of this study was to identify the incidence and types of complications after revision total hip arthroplasty (THA) within the first year, and determine the relative risk factors for these complications and of re-revision., Methods: The sample size of 5% Medicare claims data from 1998-2011 was studied. Primary THA patients who underwent subsequent revision were identified using ICD-9-CM codes. Outcomes and complications after revision THA were assessed. Multivariate Cox regression was used to evaluate the effect of patient demographic characteristics on the adjusted complication risk for revision THA patients., Results: Of the 64,260 primary THA patients identified between 1998 and 2011, 3555 patients (5.71%) underwent revision THA. Etiology of primary hip failure included mechanical complications such as loosening and wear (40.7%), dislocation (14.0%), and infection (11.3%). Complications after revision THA included infection and redo revision, 17.3% and 15.8% followed by venous thromboembolic disease (VTE) at 11.1%, dislocation at 5.43%, PE at 3.24%, and death at 2.11%. The rate of "new" infections after an aseptic revision was 8.13%. Patients in the 85+-year-old age group had a 100% greater adjusted risk of VTE (P < .001) and 406% higher adjusted risk of death (P < .001) than those in the 65-69 years-old age group. Patients with higher Charlson scores had higher adjusted risks of VTE (P < .001), infection (P < .001), death (P = .002), and re-revision THA (P = .011)., Conclusion: Advanced age is a clear risk factor for VTE and mortality, but not for dislocation, infection, or re-revision. Higher Charlson index was found to be a risk factor for every complication after revision except dislocation. Greater attention is required to address the high rate of infection and re-do revision after revision THA (17.3% and 15.8%)., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Primary total knee arthroplasty (TKA) for the treatment of knee arthritis has substantially increased over the past decade. Because of its success, the indications for primary TKA have expanded to include younger patients who are more active, elderly patients who have multiple comorbidities, and patients who have more complex issues, such as posttraumatic arthritis and severe deformity. TKA also has been used to salvage failed unicondylar arthroplasty and osteotomies about the knee. Exposure may be challenging and outcomes may not be as successful in patients with soft-tissue contractures, such as a stiff knee, who undergo TKA. Bone graft or augments may be required to correct deformity and attain proper knee alignment in patients who have a substantial varus or valgus deformity. TKA is somewhat challenging in patients who have deformity, bone loss, contracture, or multiple comorbidities, or have had prior surgery; therefore, it is necessary for surgeons to be aware of some general principles that may help minimize complications and improve outcomes.