Quantum key distribution (QKD) is a viable technique for safeguarding image transmission against numerous attacks. To address the limitations of existing methods in securing electronic medical records, we have explored an encryption technique using a set of real-time medical images along with existing standard images using QKD protocols including BB84 (Bennett-Brassard 1984), CASCADE (Cascading Error Correction Algorithm for Data Exchange), and differential-phase-shift (DPS) under the fiber-based (OFC) and free-space (FSO) channels. It utilizes the quantum bit-plane representation of the Real Ket (QBRK) model and a logistic chaotic system with XOR operation. The performance of the developed framework is tested via visual illustration and 11 different quantitative assessments. The average entropy of the scrambled image for the medical source image is 7.9896, which is quite near to the optimum value of 8. The UACI (Unified Averaged Changed Intensity) and the NPCR (Number of Pixels Change Rate) metrics values lie between 33.7879 to 33.9711 and 99.6014 to 99.8000 for the FSO channel. The BB84 QKD protocol achieves a QBER (Quantum Bit Error Rate) of 0.0028 for an FSO channel with DCR (Dark Count Rate), which improves to 0.0012 when DCR is ignored for a 10 km link range. In addition, we analyzed and compared the effect of OFC on system output as the quantum channel. From several experiments, it is observed that the BB84 provides better results against CASCADE and DPS. The proposed work is validated using the Optisystem 15.1 software under FSO and for OFC.

Cyber-physical systems (CPSs) based on the Internet of Things (IoT) form the backbone of modern smart infrastructures, including smart cities, healthcare monitoring, industrial automation, and intelligent transportation. However, connecting many resource-limited IoT devices makes them more vulnerable to cyber threats, particularly quantum attacks. This review comprehensively examines quantum-secure communication (QSC) frameworks for IoT-enabled CPS, focusing on Quantum Key Distribution (QKD), post-quantum cryptographic (PQC) algorithms, and hybrid quantum–classical security models suitable for constrained devices. A PRISMA-guided search of the Scopus and Google Scholar database was conducted in January 2026 using three keyword groups related to hybrid security, artificial intelligence, and cyber-physical systems. Based on the evaluation, 6008 publications have been identified between 2001 and 2026. The first-round screening was performed for 4948 articles, after excluding duplicates. During the screening stage, 348 articles were selected for abstract scrutiny, 115 records were excluded due to no direct focus on CPS/IoT applications, 52 studies were excluded because these papers relied on traditional security models, 25 studies were excluded due to insufficient relevance to the review objectives, and 15 additional non-English studies were removed. Following the screening stage, 141 studies were selected for full-text eligibility. Out of those, 86 studies were removed due to a lack of specific evaluation metrics or not being published in a peer-reviewed venue. Furthermore, the publications are classified as QKD-based secure CPS and QSC for industrial IoT, AI-Assisted Secure Communication for CPS Networks, and hybrid PQC-QKD models for CPS/IoT devices. This article investigates recent advancements in secure data transmission, verified protocols, and AI-driven anomaly detection customized to CPS/IoT environments. In addition, operational hurdles, interaction with open innovations, real-time deployment, and secure edge-cloud integration are highlighted. By analyzing recent developments and identifying research gaps, this review provides a structured roadmap for designing secure, scalable, and quantum-safe IoT-based CPS frameworks capable of withstanding next-generation cyber threats. This systematic review was performed and reported according to the PRISMA 2020 guidelines.

Tuberculosis (TB) is a serious infectious disease generally caused by Mycobacterium tuberculosis (MTB). It is transmissible in nature that spreads through airborne droplets. It affects mainly the lungs and also to the other parts of the body such as kidneys, spine, brain, etc. The disease is more harmful and a life risk to people who are consuming tobacco or are affected by other diseases such as diabetes, HIV, etc. Chest pain, blood in the cough, discomfort when breathing, coughing from three weeks or longer are the general signs of TB. To identify TB, the number of tests, that is, skin, blood, sputum, chest x-ray, etc., are recommended by physicians. Generally, digital chest radiography, GeneXpert machine, and BacT/ALERT 3D devices are used for identification of TB. The smart devices play a crucial role in the medical applications by providing precise disease detection, treatment, patient monitoring, and healthcare management. Some of the advantages of the smart devices are they save time and give accurate results that helps both the experts and the patients. Taking into consideration the effects and causes of TB, this chapter focuses on the role of different smart devices for identification of TB along with its diagnosis. Considering the recent advancements of machine learning (ML) approaches for disease diagnosis, the chapter also provides the usage of ML approaches integrated with devices for effective diagnosis and therapeutics evaluation of TB. <!----> <!----> <!----> <!---->

Presents corrections to the paper, (Multi-Channel Multi-Protocol Quantum Key Distribution System for Secure Image Transmission in Healthcare).