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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.)