Overcoming Alzheimer's Disease Stigma by Leveraging Artificial Intelligence and Blockchain Technologies

Blockchain for Securing AI-Driven Healthcare Systems: A Systematic Review and Future Research Perspectives

The integration of AI in healthcare has significantly advanced diagnostics, patient monitoring, and personalized treatments. However, the reliance on vast datasets raises critical concerns about data privacy, security, and trustworthiness. Blockchain technology, with its decentralized and immutable nature, has emerged as a promising solution to these challenges. This systematic review aims to explore the role of blockchain in securing AI-driven healthcare systems, evaluating its potential to enhance data security, privacy, and interoperability while identifying key challenges and future research directions. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, conducting a comprehensive search across databases such as IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, Scopus, Web of Science, and PubMed. Inclusion criteria focused on peer-reviewed studies discussing blockchain and AI integration in healthcare, while exclusion criteria eliminated irrelevant or non-empirical studies. Data extraction captured study characteristics, AI algorithms, blockchain types, key findings, and limitations. Quality assessment was performed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist, evaluating methodological rigor, innovation, and reproducibility. The review included 15 studies highlighting blockchain's role in securing AI-driven healthcare systems. Key findings demonstrated blockchain's effectiveness in enabling decentralized data sharing (e.g., federated learning (FL)), enhancing data integrity, and improving diagnostic accuracy. However, challenges such as scalability, interoperability, and regulatory compliance were recurrent. The quality assessment revealed moderate-to-high methodological quality but underscored gaps in reproducibility and real-world validation. Blockchain technology holds transformative potential for securing AI-driven healthcare systems by addressing critical privacy and security concerns. However, the field remains nascent, with significant hurdles in scalability, technical standardization, and ethical governance. Future research should prioritize hybrid blockchain architectures, clinical trials, and interdisciplinary collaboration to bridge these gaps and realize the full potential of blockchain-AI integration in healthcare.

Privacy Barriers in Health Monitoring: Scoping Review

BACKGROUND

Health monitoring technologies help patients and older adults live better and stay longer in their own homes. However, there are many factors influencing their adoption of these technologies. Privacy is one of them.

OBJECTIVE

The aim of this study was to provide an overview of the privacy barriers in health monitoring from current research, analyze the factors that influence patients to adopt assisted living technologies, provide a social psychological explanation, and propose suggestions for mitigating these barriers in future research.

METHODS

A scoping review was conducted, and web-based literature databases were searched for published studies to explore the available research on privacy barriers in a health monitoring environment.

RESULTS

In total, 65 articles met the inclusion criteria and were selected and analyzed. Contradictory findings and results were found in some of the included articles. We analyzed the contradictory findings and provided possible explanations for current barriers, such as demographic differences, information asymmetry, researchers' conceptual confusion, inducible experiment design and its psychological impacts on participants, researchers' confirmation bias, and a lack of distinction among different user roles. We found that few exploratory studies have been conducted so far to collect privacy-related legal norms in a health monitoring environment. Four research questions related to privacy barriers were raised, and an attempt was made to provide answers.

CONCLUSIONS

This review highlights the problems of some research, summarizes patients' privacy concerns and legal concerns from the studies conducted, and lists the factors that should be considered when gathering and analyzing people's privacy attitudes.

Survival study on deep learning techniques for IoT enabled smart healthcare system

Purpose

The paper is to study a review of the employment of deep learning (DL) techniques inside the healthcare sector, together with the highlight of the strength and shortcomings of existing methods together with several research ultimatums. Our study lays the foundation for healthcare professionals and government with present-day inclinations in DL-based data analytics for smart healthcare.

Methods

A deep learning-based technique is designed to extract sensor displacement effects and predict abnormalities for activity recognition via Artificial Intelligence (AI). The presented technique minimizes the vanishing gradient issue of Recurrent Neural Networks (RNN), thereby reducing the time for detecting abnormalities with consideration of temporal and spatial factors. Proposed Moran Autocorrelation and Regression-based Elman Recurrent Neural Network (MAR-ERNN) introduced.

Results

Experimental results show the feasibility of the proposed method. The results show that the proposed method improves accuracy by 95% and reduces execution time by 18%.

Conclusion

MAR-ERNN performs well in the activity recognition of health status. Collectively, this IoT-enabled smart healthcare system is utilized by enhancing accuracy, and minimizing time and overhead reduction.

Blockchain in radiology research and clinical practice: current trends and future directions

Blockchain usage in healthcare, in radiology, in particular, is at its very early infancy. Only a few research applications have been tested, however, blockchain technology is widely known outside healthcare and widely adopted, especially in Finance, since 2009 at least. Learning by history, radiology is a potential ideal scenario to apply this technology. Blockchain could have the potential to increase radiological data value in both clinical and research settings for the patient digital record, radiological reports, privacy control, quantitative image analysis, cybersecurity, radiomics and artificial intelligence.Up-to-date experiences using blockchain in radiology are still limited, but radiologists should be aware of the emergence of this technology and follow its next developments. We present here the potentials of some applications of blockchain in radiology.

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the "Smart Healthcare" era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient's physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.

Applications of Blockchain in the Medical Field: Narrative Review

Background

As a distributed technology, blockchain has attracted increasing attention from stakeholders in the medical industry. Although previous studies have analyzed blockchain applications from the perspectives of technology, business, or patient care, few studies have focused on actual use-case scenarios of blockchain in health care. In particular, the outbreak of COVID-19 has led to some new ideas for the application of blockchain in medical practice.

Objective

This paper aims to provide a systematic review of the current and projected uses of blockchain technology in health care, as well as directions for future research. In addition to the framework structure of blockchain and application scenarios, its integration with other emerging technologies in health care is discussed.

Methods

We searched databases such as PubMed, EMBASE, Scopus, IEEE, and Springer using a combination of terms related to blockchain and health care. Potentially relevant papers were then compared to determine their relevance and reviewed independently for inclusion. Through a literature review, we summarize the key medical scenarios using blockchain technology.

Results

We found a total of 1647 relevant studies, 60 of which were unique studies that were included in this review. These studies report a variety of uses for blockchain and their emphasis differs. According to the different technical characteristics and application scenarios of blockchain, we summarize some medical scenarios closely related to blockchain from the perspective of technical classification. Moreover, potential challenges are mentioned, including the confidentiality of privacy, the efficiency of the system, security issues, and regulatory policy.

Conclusions

Blockchain technology can improve health care services in a decentralized, tamper-proof, transparent, and secure manner. With the development of this technology and its integration with other emerging technologies, blockchain has the potential to offer long-term benefits. Not only can it be a mechanism to secure electronic health records, but blockchain also provides a powerful tool that can empower users to control their own health data, enabling a foolproof health data history and establishing medical responsibility.

A Review of Artificial Intelligence, Big Data, and Blockchain Technology Applications in Medicine and Global Health

Artificial intelligence (AI) programs are applied to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, personalized medicine in healthcare, and outbreak predictions in global health, as in the case of the current COVID-19 pandemic. Machine learning (ML) is a field of AI that allows computers to learn and improve without being explicitly programmed. ML algorithms can also analyze large amounts of data called Big data through electronic health records for disease prevention and diagnosis. Wearable medical devices are used to continuously monitor an individual’s health status and store it in cloud computing. In the context of a newly published study, the potential benefits of sophisticated data analytics and machine learning are discussed in this review. We have conducted a literature search in all the popular databases such as Web of Science, Scopus, MEDLINE/PubMed and Google Scholar search engines. This paper describes the utilization of concepts underlying ML, big data, blockchain technology and their importance in medicine, healthcare, public health surveillance, case estimations in COVID-19 pandemic and other epidemics. The review also goes through the possible consequences and difficulties for medical practitioners and health technologists in designing futuristic models to improve the quality and well-being of human lives.

Association between Alzheimer's disease and risk of cancer: A retrospective cohort study in Shanghai, China

INTRODUCTION

We investigated the association between Alzheimer's disease (AD) and the risk of cancer in the Chinese population.

METHODS

In this retrospective cohort study, multivariate Cox proportional hazard regression analysis was used to determine the correlation between AD and the risk of various cancers, as shown by hazard ratios (HRs) with 95% confidence intervals (CIs).

RESULTS

Of 8097 AD patients, the HR for all subsequent cancers was 0.822 (95% CI, 0.728-0.928; P = .002). Among them, three specific cancers were associated with AD: lung cancer (HR, 0.656; 95% CI, 0.494- 0.871; P = .004), prostate and testicular cancer (HR, 0.414; 95% CI, 0.202-0.847; P = .016), and lymphoma (HR, 2.202; 95% CI, 1.005-4.826; P = .049).

CONCLUSION

Patients with AD might have a lower chance of developing several cancers, including lung cancer and prostate and testicular cancer. Meanwhile, a positive association between AD and a higher incident rate of lymphoma was observed.