Background: Over the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being. Objective: This paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU³RATE, which takes multidimensional measures such as user star rating, user review and features declared by the developer to generate the rating of an app. However, currently, there is very little conceptual understanding on how user reviews affect app rating from a multi-dimensional perspective. This study applies AI-based text mining technique to develop more comprehensive understanding of user feedback based on several important factors, determining the mHealth app ratings. Method: Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user star rating, user text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users’ sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer’s statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. Results and conclusions: ACCU³RATE concentrates on heart related Apps found in the play store and App gallery. The findings indicate the efficacy of the proposed method as opposed to the current device scale. This study has implications for both App developers and consumers who are using mHealth Apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has also been noticed that the fuzzy based rating scale, as in ACCU³RATE, matches more closely to the rating performed by experts.

Integrating deep learning (DL) with the Internet of Medical Things (IoMT) is a paradigm shift in modern healthcare, offering enormous opportunities for patient care, diagnostics, and treatment. Implementing DL with IoMT has the potential to deliver better diagnosis, treatment, and patient management. However, the practical implementation has challenges, including data quality, privacy, interoperability, and limited computational resources. This survey article provides a conceptual IoMT framework for healthcare, synthesizes and identifies the state-of-the-art solutions that tackle the challenges of the current applications of DL, and analyzes existing limitations and potential future developments. Through an analysis of case studies and real-world implementations, this work provides insights into best practices and lessons learned, including the importance of robust data preprocessing, integration with legacy systems, and human-centric design. Finally, we outline future research directions, emphasizing the development of transparent, scalable, and privacy-preserving DL models to realize the full potential of IoMT in healthcare. This survey aims to serve as a foundational reference for researchers and practitioners seeking to navigate the challenges and harness the opportunities in this rapidly evolving field.

Importance of data security differs from sector to sector. However, data of sensitive nature are increasing day by day and thereby, it requires utmost attention towards the protection of data. Organizations from all over the world are getting involved to deploy the best cyber defense mechanism. Primary concerns for the organizations includes three common factors: people, processes, and technology. Smart national identity cards (smart cards) are immensely sensitive in nature. In Bangladesh, smart cards are being used for 22 services, which include banking, passports, driving licenses, etc. In addition, smart cards also hold bio-metric details for the cardholder such as retina scan, ten finger impressions, picture, and so on. Most of them are confidential data and prone to privacy breach. To address this issue, this paper propose a model for smart card management system using blockchain technology. In recent years, blockchain earned popularity among researchers to protect data from unlawful altercation. Adaptation of our proposed model will help the government of Bangladesh to secure citizens’ private information and bring transparency on their information management. Furthermore, it will provide access control capability to citizens in case of third-party information access requests.