AI-Powered Blockchain Technology for Public Health: A Contemporary Review, Open Challenges, and Future Research Directions

Tracing the Blockchain Challenges in Healthcare: A Topic Modeling and Bibliometric Analysis

The application of blockchain technology to healthcare offers promise in providing solutions to some key challenges related to data sharing, privacy, security, and access control. However, several barriers prevent the widespread adoption of blockchain and prompted research efforts. This study aims to conduct a bibliometric analysis of 196 documents indexed in the Scopus database to examine their structure, impact, contributors, and journals. The bibliometric analysis provides information on the publication and citation structure, as well as the most productive authors, universities, countries, journals, and most cited studies. In addition, it identifies the most prevalent keywords and their co-occurrence patterns on blockchain challenges in healthcare. A topic modeling approach, using Latent Dirichlet Allocation (LDA), is also employed to reveal the latent topical structure of this literature. As a result of these findings, the research landscape in this area has been quantitatively analyzed, identifying six critical challenges regarding the use of blockchain in healthcare: data privacy/security, integration with smart devices, interoperability, scalability, governance, and cost.

A new technology for medical and surgical data organisation: the WSES-WJES Decentralised Knowledge Graph

BACKGROUND

The quality of Big Data analysis in medicine and surgery heavily depends on the methods used for clinical data collection, organization, and storage. The Knowledge Graph (KG) represents knowledge through a semantic model, enhancing connections between diverse and complex information. While it can improve the quality of health data collection, it has limitations that can be addressed by the Decentralized (blockchain-powered) Knowledge Graph (DKG). We report our experience in developing a DKG to organize data and knowledge in the field of emergency surgery.

METHODS AND RESULTS

The authors leveraged the cyb.ai protocol, a decentralized protocol within the Cosmos network, to develop the Emergency Surgery DKG. They populated the DKG with relevant information using publications from the World Society of Emergency Surgery (WSES) featured in the World Journal of Emergency Surgery (WJES). The result was the Decentralized Knowledge Graph (DKG) for the WSES-WJES bibliography.

CONCLUSIONS

Utilizing a DKG enables more effective structuring and organization of medical knowledge. This facilitates a deeper understanding of the interrelationships between various aspects of medicine and surgery, ultimately enhancing the diagnosis and treatment of different diseases. The system's design aims to be inclusive and user-friendly, providing access to high-quality surgical knowledge for healthcare providers worldwide, regardless of their technological capabilities or geographical location. As the DKG evolves, ongoing attention to user feedback, regulatory frameworks, and ethical considerations will be critical to its long-term success and global impact in the surgical field.

Biomedical Data Science, Artificial Intelligence, and Ethics: Navigating Challenges in the Face of Explosive Growth

Advances in biomedical data science and artificial intelligence (AI) are profoundly changing the landscape of healthcare. This article reviews the ethical issues that arise with the development of AI technologies, including threats to privacy, data security, consent, and justice, as they relate to donors of tissue and data. It also considers broader societal obligations, including the importance of assessing the unintended consequences of AI research in biomedicine. In addition, this article highlights the challenge of rapid AI development against the backdrop of disparate regulatory frameworks, calling for a global approach to address concerns around data misuse, unintended surveillance, and the equitable distribution of AI's benefits and burdens. Finally, a number of potential solutions to these ethical quandaries are offered. Namely, the merits of advocating for a collaborative, informed, and flexible regulatory approach that balances innovation with individual rights and public welfare, fostering a trustworthy AI-driven healthcare ecosystem, are discussed.

Adoption of blockchain as a step forward in orthopedic practice

Blockchain technology has gained popularity since the invention of Bitcoin in 2008. It offers a decentralized and secure system for managing and protecting data. In the healthcare sector, where data protection and patient privacy are crucial, blockchain has the potential to revolutionize various aspects, including patient data management, orthopedic registries, medical imaging, research data, and the integration of Internet of Things (IoT) devices. This manuscript explores the applications of blockchain in orthopedics and highlights its benefits. Furthermore, the combination of blockchain with artificial intelligence (AI), machine learning, and deep learning can enable more accurate diagnoses and treatment recommendations. AI algorithms can learn from large datasets stored on the blockchain, leading to advancements in automated clinical decision-making. Overall, blockchain technology has the potential to enhance data security, interoperability, and collaboration in orthopedics. While there are challenges to overcome, such as adoption barriers and data sharing willingness, the benefits offered by blockchain make it a promising innovation for the field.

Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review

Over the past five years, interest in the literature regarding the security of the Internet of Medical Things (IoMT) has increased. Due to the enhanced interconnectedness of IoMT devices, their susceptibility to cyber-attacks has proportionally escalated. Motivated by the promising potential of AI-related technologies to improve certain cybersecurity measures, we present a comprehensive review of this emerging field. In this review, we attempt to bridge the corresponding literature gap regarding modern cybersecurity technologies that deploy AI techniques to improve their performance and compensate for security and privacy vulnerabilities. In this direction, we have systematically gathered and classified the extensive research on this topic. Our findings highlight the fact that the integration of machine learning (ML) and deep learning (DL) techniques improves both the performance of cybersecurity measures and their speed, reliability, and effectiveness. This may be proven to be useful for improving the security and privacy of IoMT devices. Furthermore, by considering the numerous advantages of AI technologies as opposed to their core cybersecurity counterparts, including blockchain, anomaly detection, homomorphic encryption, differential privacy, federated learning, and so on, we provide a structured overview of the current scientific trends. We conclude with considerations for future research, emphasizing the promising potential of AI-driven cybersecurity in the IoMT landscape, especially in patient data protection and in data-driven healthcare.

Blockchain Revolutionizing in Emergency Medicine: A Scoping Review of Patient Journey through the ED

BACKGROUND

Blockchain technology has revolutionized the healthcare sector, including emergency medicine, by integrating AI, machine learning, and big data, thereby transforming traditional healthcare practices. The increasing utilization and accumulation of personal health data also raises concerns about security and privacy, particularly within emergency medical settings.

METHOD

Our review focused on articles published in databases such as Web of Science, PubMed, and Medline, discussing the revolutionary impact of blockchain technology within the context of the patient journey through the ED.

RESULTS

A total of 33 publications met our inclusion criteria. The findings emphasize that blockchain technology primarily finds its applications in data sharing and documentation. The pre-hospital and post-discharge applications stand out as distinctive features compared to other disciplines. Among various platforms, Ethereum and Hyperledger Fabric emerge as the most frequently utilized options, while Proof of Work (PoW) and Proof of Authority (PoA) stand out as the most commonly employed consensus algorithms in this emergency care domain. The ED journey map and two scenarios are presented, exemplifying the most distinctive applications of emergency medicine, and illustrating the potential of blockchain. Challenges such as interoperability, scalability, security, access control, and cost could potentially arise in emergency medical contexts, depending on the specific scenarios.

CONCLUSION

Our study examines the ongoing research on blockchain technology, highlighting its current influence and potential future advancements in optimizing emergency medical services. This approach empowers frontline medical professionals to validate their practices and recognize the transformative potential of blockchain in emergency medical care, ultimately benefiting both patients and healthcare providers.

Evolution of Hybrid Intelligence and Its Application in Evidence-Based Medicine: A Review

Modern medicine, both in clinical practice and research, has become more and more based on data, which is changing equally in type and quality with the advent and development of healthcare digitalization. The first part of the present paper aims to present the steps through which data, and subsequently clinical and research practice, have evolved from paper-based to digital, proposing a possible future of this digitalization in terms of potential applications and integration of digital tools in medical practice. Noting that digitalization is no more a possible future, but a concrete reality, there is a strong need for a new definition of evidence-based medicine, which must take into account the progressive integration of artificial intelligence (AI) in all decision-making processes. So, leaving behind the traditional research concept of human intelligence versus AI, poorly adaptable to real-world clinical practice, a Human and AI hybrid model, seen as a deep integration of AI and human thinking, is proposed as a new healthcare governance system. The second part of our review is focused on some of the major challenges the digitalization process has to face, particularly privacy issues, system complexity and opacity, and ethical concerns related to legal aspects and healthcare disparities. Analyzing these open issues, we aim to present some of the future directions that in our opinion should be pursued to implement AI in clinical practice.