Artificial Intelligence and Cyber Security in Clinical Digitalization: A Scientometrics Study of Global Trends

Abstract

Early detection of any disease can be treated with minimal effort. Unfortunately, many people fail to identify their illness in its early stages, leading to a higher death rate worldwide. Breast Cancer (BC), in particular, can be successfully treated if detected early, before it spreads to other parts of the body. However, misinterpretation by medical professionals can lead to incorrect diagnoses. Computer-aided diagnosis method provides automated assistance to practitioners, producing accurate results to analyze the severity of diseases. Generally, cancer plays a major role in securing the lives of the damaged. An efficient way to verify the Breast cancer is by generating a classification method using risk metrics. Simulations for Artificial Intelligence have been to enhance the efficiency of early-stage recognition. This research work investigates the combination of artificial intelligence (AI) algorithms for predicting healthcare applications with IoT systems designed to preserve private medical information. IoT devices, such as smartwatches, remote detectors, and smart medical devices, regularly collect massive volumes of patient data, which artificial intelligence (AI) algorithms use to forecast health outcomes, discover trends, and optimize treatment strategies. However, the real-time transmission of this information raises serious concerns regarding patient confidentiality and information security. This research looks into how IoT-based privacy-preserving solutions, such as edge computing, encryption, and decentralized data storage, are being combined with machine learning models to keep private medical data secure while allowing for accurate predictions.

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