Abstract Internet of Things (IoT) technologies and healthcare present revolutionary chances to improve operational efficiency, patient outcomes, and tailored medication transformation. This paper thoroughly investigates IoT in healthcare using Latent Dirichlet Allocation (LDA) to spot important trends and research gaps in current work. To achieve this, researchers have comprehensively analyzed 11,586 published papers from 2006 to 2024 which are extracted from Scopus database. Researchers have identified 2, 5, and 10 key topics to define significant areas of the research. Over time, it compares research topics to show how important areas, including wearable technology, artificial intelligence-powered analytics, blockchain for safe data management, and edge computing, have evolved. The paper additionally examines important issues, including data privacy issues, lack of interoperability, and restricted inclusiveness for underprivileged communities. Emphasizing inclusivity, ethical compliance, and pragmatic implementation tactics catered to different healthcare environments, a strategy framework is suggested to help solve these difficulties. This paper helps IoT implementation in healthcare advance by giving actionable insights, particular discoveries, and future research directions, thereby opening the path for more fair, efficient, and sustainable healthcare systems.
Vanessa Sophia Cunha, Paul J. Diefenbach, Emil Polyak
This thesis explores the design and development of CLS Nexus, an AI-assisted clinical decision-support platform built for Child Life Specialists (CLS) in pediatric healthcare settings. The project addresses a documented gap in the field: despite a substantive evidence base for psychosocial intervention in pediatric care, no purpose-built digital framework exists to support specialists in organizing, discovering, and personalizing therapeutic activities at an institutional level. CLS Nexus is a WordPress-based proof-of-concept built with an endpoint-agnostic AI integration layer, using the Anthropic API with Claude Sonnet as the demonstration model, with the architecture designed to support institutional deployment without changes to the application layer. A particular focus was placed on positioning AI as a tool that extends specialist judgment rather than replacing it. The methodology employs a design-based research approach progressing through three iterative platform concepts, each of which produced design knowledge that informed the next, culminating in a fully functional proof-of-concept system. The platform encompasses two integrated AI systems: System 1, an automated content tagging pipeline that analyzes uploaded clinical materials across twenty-seven dimensions using a purpose-built pediatric psychology-informed taxonomy; and System 2, a structured patient intake advisor that scores candidate interventions against individual patient profiles using a zero-to-five star rating system with explicit flags across thirteen psychological categories. The platform's design, prompt engineering decisions, and clinical taxonomy structure are documented as academically significant artifacts throughout. Expert validation was conducted through a two-track asynchronous survey methodology, with healthcare professionals with clinical backgrounds evaluating the system's clinical credibility and taxonomy design, and digital media practitioners evaluating its information architecture, AI integration, and ethical positioning. The project contributes a concrete, ethically grounded example of how AI can be integrated into provider-facing clinical tools, demonstrating that meaningful personalization and clinical decision-support capability can be achieved through accessible platform infrastructure without displacing the specialist judgment that makes psychosocial care most effective.
Open access
Digital Mental Health Interventions
Electronic Health Records Systems
Artificial Intelligence in Healthcare and Education
Xavier Tadeo, Gyula Seres, Peter Wang, Yoann Sapanel · 19 authors
<sec> <title>UNSTRUCTURED</title> Incentivization in clinical trial participation can be challenging, with many studies failing to meet recruitment or retention goals despite traditional compensation strategies. Digital health evolves and, with it, new approaches can emerge to engage participants meaningfully. We propose unique digital images as a novel, symbolic incentive for clinical trials. Digital images combine qualities such as personalization, ownership, and digital visibility, which may drive engagement more effectively than monetary rewards alone. In our illustrative study, participants complete AI-personalized digital therapeutic training using CURATE.DTx, generating individualized learning trajectories. These are transformed into digital artworks and minted as non-fungible tokens (NFTs), given as a reward upon trial completion. This concept integrates gamification, personalization, and blockchain technology to support both intrinsic and extrinsic motivation. We explore the implications for decentralized healthcare, long-term behavior change, and participant recognition in the context of preventive medicine and longevity science. Our aim is to encourage research into the use of digital incentives to transform the participant experience and promote sustained engagement in health interventions. </sec>
Open access
Digital Mental Health Interventions
Artificial Intelligence in Healthcare and Education
Severin Bonnet, Jan-Gero Alexander Hannemann, Frank Teuteberg
Abstract In this paper, we report on the initial stage of a design science research (“DSR”) project aimed at establishing design principles for DeFAI (the intersection of DeFi and AI) generative AI-based chatbot assistants tailored to decentralized finance (“DeFi”). Addressing challenges such as user trust, data privacy and security, and regulatory compliance, we conducted a targeted literature review, expert interviews, as well as iterative prototype ideation and evaluation to derive three design principles: (1) Human-Centered Design, (2) Resilience and Interoperability, and (3) DeFi-Native User Experience Together, these principles operationalize general chatbot design guidance for DeFi contexts characterized by self-custody, irreversible transactions, and adversarial risk environments. We demonstrate these principles through DeFAIGuide, a mockup that illustrates how technical barriers in DeFi can be abstracted to enhance accessibility for novice users while also offering advanced features for expert users. Our study contributes actionable design knowledge to support the future development of DeFAI solutions that advance inclusivity, security, privacy, and self-sovereignty, paving the way for a more transparent and participatory financial future.
Digital mental health interventions increasingly require robust security frameworks, authenticated content delivery, and personalized therapeutic experiences. This systematic literature review examines the convergence of Blockchain technology, non-fungible tokens (NFTs), and deepfake synthesis in mental health applications, addressing a critical gap in interdisciplinary research. Following PRISMA guidelines, we conducted comprehensive searches across Scopus database (2014-2024), supplemented by IEEE Xplore, Web of Science, and PubMed Central. Our methodology employed PICO framework-based queries, identifying 15 037 relevant studies across seven queries (Q) configurations (Q1-Q7), with 5093 studies meeting inclusion criteria after rigorous quality assessment. Results demonstrate Blockchain provides immutable data governance (3962 studies), NFTs enable secure therapeutic asset tokenization (771 studies), while deepfakes facilitate personalized avatar-based therapy (230 studies). Cross-technology integration remains limited: Blockchain-NFTs combinations (102 studies), Blockchain-Deepfake integrations (24 studies), NFTs-Deepfake applications (2 studies), with only 2 studies addressing all three technologies simultaneously. This review establishes the first comprehensive taxonomy of converged technologies in mental health, identifying critical research directions for scalable, secure, and ethically-compliant digital therapeutic platforms.
"With growing concerns about mental well-being, users want efficacious means to monitor emotions and get personalized assistance, with current solutions often sacrificing privacy or offering shallow revelations. ZenLoop overcomes the shortcomings by combining AI-based analysis of emotion with safe Web3 storage to provide both well-being support alongside privacy. This paper builds a conversational AI chatbot that offers coping mechanisms, a mood tracker to record emotion states, and an analysis dashboard to enable users to identify behavior patterns. Developed with React for frontend, Node.js for backend, and MongoDB for organized data, ZenLoop provides empathy-based responses leveraging NLP models trained on mental well-being dialogues. Journals are encrypted and stored in Web3-based storage, with immutable, decentralized protection. Trends in moods are depicted in interactive graphs, and AI-driven insights enable users to monitor emotion shifts. Tests show enhanced user engagement, improved self-perception, along with superior protection of data. The chatbot is effective in detecting levels of distress along with recommended interventions, promoting emotional resilience. By combining AI-driven tools for mental well-being with the security of blockchain, ZenLoop enables users to express emotion securely, monitor their mental well-being patterns, and get personalized advice at no cost of privacy. This work demonstrates the potential of privacy-based AI-based solutions to promote well-being at the emotional level, leading to the development of secure, user-centric applications for mental well-being.
Integrating blockchain into healthcare devices offers the potential for improved data control but faces significant usability and acceptance challenges. This study addresses this gap by evaluating CipherPal, an improved blockchain-enabled Smart Fidget Toy prototype, using a multi-framework approach to understand the interplay between technology, design, and user experience. We synthesized insights from three complementary frameworks: an expert review assessing adherence to Web3 Design Guidelines, a User Acceptance Toolkit assessment with professionals based on UTAUT2, and an extended three-day user testing study. The findings revealed that users valued CipherPal’s satisfying tactile interaction and perceived benefits for well-being, such as stress relief. However, significant usability barriers emerged, primarily related to challenging device–application connectivity and data synchronization. The multi-framework approach proved valuable in revealing these core tensions. While the device was conceptually accepted, the blockchain integration added significant interaction friction that overshadowed its potential benefits during the study. This research underscores the critical need for user-centered design in health-related blockchain applications, emphasizing that seamless usability and abstracting technical complexity are paramount for adoption.
Juan Camilo Pazos-Alfonso, Ana Luisa Mendoza-Barrera
This argumentative essay explores the psychological and clinical implications of using non-fungible tokens (NFTs) as symbolic tools in real-world psychological therapy. Adopting a constructive and pro-NFT perspective, the text argues that NFTs—unique digital assets recorded on a blockchain—offer novel opportunities to represent therapeutic milestones, identity processes, and meaningful personal experiences. The essay examines how NFTs can enhance patient motivation, promote self-reflection, support engagement, and reinforce a sense of autonomy throughout the therapeutic process. Drawing from concepts such as token economies, gamification, and narrative psychology, NFTs are proposed as symbolic reinforcers that validate personal growth, improve adherence to treatment, and help patients actively shape their therapeutic journey. The discussion highlights potential use cases, such as NFT-based achievements, digital identity representations, and collectible artifacts created during therapy. Technical and ethical considerations—such as accessibility, privacy, and informed consent—are addressed to ensure responsible implementation. Grounded in recent peer-reviewed research, the essay concludes that NFTs have the potential to enrich mental health interventions, especially in digital and immersive therapy environments. It recommends future empirical studies to assess the effectiveness of NFT-based systems in clinical practice. Ultimately, NFTs could serve as a bridge between emerging technologies and psychology, providing patients with symbolic tools to represent, commemorate, and take ownership of their therapeutic progress.
This current study explores the intention to adopt cryptocurrencies (IACR) within the Arab world. The study is founded on the diffusion of innovation theory and examines the relationship through the mediation of digital technostress and the moderation of ethical issues and government regulations. The study employed a quantitative approach and gathered cross-sectional data from 437 respondents through an online survey. Subsequently, structural equation modeling (SEM) was utilized to test hypotheses. The findings indicated that digital technostress acts as a mediating factor in the relationship between variables such as complexity, observability, compatibility, and the intention to adopt cryptocurrency, while no mediating effect was observed between relative advantage and trialability and the intention to adopt cryptocurrency through digital technostress. Furthermore, the study confirmed the moderating role of ethical issues in the relationship between digital technostress and the intention to adopt cryptocurrency. However, no moderating effects of government regulations on cryptocurrency adoption among Arab investors were identified. Findings highlight the importance of fostering supportive regulatory environments for cryptocurrency investment in the Arab world and affirm the applicability of the diffusion of innovation theory in the context of blockchain and cryptography. Empirical evidence emphasizes the need for further longitudinal investigations in global regions.
Federated learning (FL) is increasingly recognised for addressing security and privacy concerns in traditional cloud-centric machine learning (ML), particularly within personalised health monitoring such as wearable devices. By enabling global model training through localised policies, FL allows resource-constrained wearables to operate independently. However, conventional first-order FL approaches face several challenges in personalised model training due to the heterogeneous non-independent and identically distributed (non-iid) data by each individual's unique physiology and usage patterns. Recently, second-order FL approaches maintain the stability and consistency of non-iid datasets while improving personalised model training. This study proposes and develops a verifiable and auditable optimised second-order FL framework BFEL (blockchain enhanced federated edge learning) based on optimised FedCurv for personalised healthcare systems. FedCurv incorporates information about the importance of each parameter to each client's task (through fisher information matrix) which helps to preserve client-specific knowledge and reduce model drift during aggregation. Moreover, it minimizes communication rounds required to achieve a target precision convergence for each client device while effectively managing personalised training on non-iid and heterogeneous data. The incorporation of ethereum-based model aggregation ensures trust, verifiability, and auditability while public key encryption enhances privacy and security. Experimental results of federated CNNs and MLPs utilizing mnist, cifar-10, and PathMnist demonstrate framework's high efficiency, scalability, suitability for edge deployment on wearables, and significant reduction in communication cost.
The Internet of Medical Things (IoMT) is transforming healthcare by seamlessly connecting medical devices, wearables, and sensors to enable personalized, real-time health monitoring and treatment for consumers. As IoMT continues to advance, ensuring the security and privacy of transmitted data has become a critical concern. Blockchain technology has emerged as a promising solution to enhance privacy and security, particularly in sensitive areas such as medical data within the Internet of Things. By integrating blockchain with IoT, secure transmission of medical data can be achieved, paving the way for improved healthcare services, enhanced consumer privacy, and accelerated medical advancements. In this paper, we propose EHRGuard: Enhancing Privacy and Security of Electronic Health Records through Blockchain Technology. EHRGuard is a novel system that leverages blockchain technology to address key challenges in the management of Electronic Health Records (EHRs), with a focus on improving privacy, security, and interoperability in healthcare data systems for consumers. The framework utilizes the Internet of Medical Things (IoMT) to collect real-time health data from consumers through sensors and integrates blockchain technology to ensure data anonymity, security, and integrity. By combining IoMT and blockchain, EHRGuard enables the seamless and secure gathering of real-time health data, ensuring that sensitive information is protected from unauthorized access and tampering. Experimental results demonstrate that the proposed system outperforms traditional healthcare systems in terms of service quality and consumer data monitoring. This innovative approach not only enhances the security and privacy of EHRs but also fosters trust and efficiency in healthcare systems, ultimately benefiting consumers and advancing medical research and treatment.
Mahdi Akbari Zarkesh, Ehsan Dastani, Bardia Safaei, Ali Movaghar
The pervasive adoption of Internet of Things (IoT) has significantly advanced healthcare digitization and modernization. Nevertheless, the sensitive nature of medical data presents security and privacy challenges. On the other hand, resource constraints of IoT devices often necessitates cloud services for data handling, introducing single points of failure, processing delays, and security vulnerabilities. Meanwhile, the blockchain technology offers potential solutions for enhancing security, decentralization, and data ownership. An ideal solution should ensure confidentiality, access control, and data integrity while being scalable, cost-effective, and integrable with the existing systems. However, current blockchain-based studies only address some of these requirements. Accordingly, this paper proposes EdgeLinker; a comprehensive solution incorporating Proof-of-Authority consensus, integrating smart contracts on the Ethereum blockchain for access control, and advanced cryptographic algorithms for secure data communication between IoT edge devices and the fog layer in healthcare fog applications. This novel framework has been implemented in a real-world fog testbed, using COTS fog devices. Based on a comprehensive set of evaluations, EdgeLinker demonstrates significant improvements in security and privacy with reasonable costs, making it an affordable and practical system for healthcare fog applications. Compared with the state-of-the-art, without significant changes in the write-time to the blockchain, EdgeLinker achieves a 35% improvement in data read time. Additionally, it is able to provide better throughput in both reading and writing transactions compared to the existing studies. EdgeLinker has been also examined in terms of energy, resource consumption and channel latency in both secure and non-secure modes, which has shown remarkable improvements.
Abstract The fusion of blockchain and artificial intelligence (AI) marks a paradigm shift in healthcare, addressing critical challenges in securing electronic health records (EHRs), ensuring data privacy, and facilitating secure data transmission. This study provides a comprehensive analysis of the adoption of blockchain and AI within healthcare, spotlighting their role in fortifying security and transparency leading the trajectory for a promising future in the realm of healthcare. Our study, employing the PRISMA model, scrutinized 402 relevant articles, employing a narrative analysis to explore the fusion of blockchain and AI in healthcare. The review includes the architecture of AI and blockchain, examines AI applications with and without blockchain integration, and elucidates the interdependency between AI and blockchain. The major findings include: (i) it protects data transfer, and digital records, and provides security; (ii) enhances EHR security and COVID-19 data transmission, thereby bolstering healthcare efficiency and reliability through precise assessment metrics; (iii) addresses challenges like data security, privacy, and decentralized computing, forming a robust tripod. The fusion of blockchain and AI revolutionize healthcare by securing EHRs, and enhancing privacy, and security. Private blockchain adoption reflects the sector’s commitment to data security, leading to improved efficiency and accessibility. This convergence promises enhanced disease identification, response, and overall healthcare efficacy, and addresses key sector challenges. Further exploration of advanced AI features integrated with blockchain promises to enhance outcomes, shaping the future of global healthcare delivery with guaranteed data security, privacy, and innovation.
Open access
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Shanshan Hu, Manuel Schmidt-Kraepelin, Scott Thiebes, Ali Sunyaev
Following the success of the Bitcoin blockchain, distributed ledger technology (DLT) has received extensive attention in health informatics research. Yet, the healthcare industry is highly complex with many different stakeholders, information systems, regulations, and challenges. Thus, DLT may be used in various settings and for different purposes. First surveys have started to synthesize our knowledge of the different use cases, in which healthcare may benefit from DLT implementations. However, an in-depth understanding of whether and how these use cases differ concerning their requirements of DLT characteristics (i.e., technical or administrative design features) is still lacking. In this work, we conducted a structured review of 185 studies on DLT-based applications in healthcare. The results reveal six pertinent use cases, each with its own combination of different purposes that DLT is used for. Furthermore, our study shows that each of these use cases has a unique set of requirements with regard to the most important DLT characteristics. In doing so, we seek to guide practitioners in the development of highly effective DLT-based applications in various healthcare settings and pave the way for future research to investigate the understudied areas of DLT-based applications in healthcare.
Ahmad Musamih, Khaled Salah, Raja Jayaraman, Mohamed L. Seghier · 7 authors
Claustrophobia is a widespread mental health issue that affects a significant portion of the population. Traditional approaches, such as exposure therapies, have shown efficacy but are limited by their inability to provide immersive and controlled environments for effective therapy. These methods often struggle to replicate the intensity and authenticity of claustrophobic scenarios and face challenges in data security and patient engagement. In this paper, we propose a blockchain and Non-Fungible Token (NFT)-based metaverse to create a decentralized, transparent, immersive, and secure platform for claustrophobia exposure therapy. Our approach integrates blockchain technology for decentralized data management and NFTs for incentivizing patient participation, offering a unique enhancement to traditional therapy methods. We develop four blockchain smart contracts to manage registration, access control, NFT management, progress monitoring, and incentives distribution. Our system architecture, sequence diagrams, and algorithms are presented to demonstrate the functionality of our solution. We validate our approach through functionality testing, cost evaluation, and security analysis. Specifically, we measure the gas consumption of the smart contracts, analyze transaction costs, and assess system security against vulnerabilities. Additionally, we leverage existing literature to support the effectiveness of NFTs as incentives and the role of blockchain in enhancing data security and participation. The results show improvements in therapy engagement and data security, validating the practical implications of integrating blockchain and metaverse technologies in mental health therapy. We discuss the practical implications, challenges, and limitations of our solution, along with guidelines for generalization. The source code is publicly available on GitHub.
Xolminds is a web3 based self improvement platform that seeks to support people in reimagining their lives, recreating themselves and coping with mental health issues and challenges.
Mental health is an important aspect of well-being as it encompasses emotional, psychological and social well-being. The use of patient portals in mental health care has gained attention as a potential tool to improve access to care for individuals with mental illness. Patient portals may be vulnerable to unauthorized access if appropriate security measures are not put in place. This study leverages blockchain technology to create tamper-proof patient records. The proposed solution uses an on-chain database that stores hashes and the actual medical record of a patient as well as an off-chain solution that handles encryption of each user’s medical record using their respective keys in a trustless manner before they are uploaded on-chain. A secure smart contract hosted on the Ethereum and the Byzantine Fault Tolerance consensus algorithm was used to ensure patient privacy. The research employed the Comparative Analysis Research Methodology as the research methodology and the Kanban methodology as the software development methodology. The research project concludes that the proposed solution addresses the current security issues and data privacy concerns in patient data. The decentralized nature of blockchain ensures security, transparency and tamper-proof storage of information. Further research is needed for future advancements, like integrating blockchain-based patient portals with wearable devices and IoT.
David Mauricio, Paulo César Llanos-Colchado, Leandro Sebastián Cutipa-Salazar, Pedro Castañeda · 7 authors
In Peru, there is currently no integrated electronic health record (EHR) system that can be automatically shared between healthcare facilities. This leads to increased service costs due to duplicated examinations and records, as well as additional time required to manage patients’ clinical information. One alternative for ensuring the secure interoperability of EHRs while preserving data privacy is the use of blockchain technology. However, existing works consider a pre-established format for exchanging EHRs, which is not applicable when systems have different formats, as is the case in Peru. This work proposes an architecture and a web application for exchanging EHRs in heterogeneous systems. The proposed system includes the homologation of an EHR with rapid interoperability resources for medical attention using FHIR HL7, and vice versa, to achieve interoperability. Additionally, it utilizes blockchain technology to ensure data security and privacy. The web application was tested using a case simulation to demonstrate EHR interoperability between clinics in a clear, secure, and efficient manner. In addition, a survey was conducted with 30 patients regarding adoption, and another survey was conducted with 10 doctors from a public hospital in Peru regarding usability. The results demonstrate a very high level of adoption and usability for them all. Unlike other studies, the proposal does not necessitate alterations to existing EHR systems for interoperability. In other words, the proposal presents a feasible and cost-effective alternative to addressing the EHR interoperability issue in clinics and hospitals in Peru.
The idea of networked personal medical devices is a component of contemporary Smart Health Systems (SHS). These gadgets offer remote observing and the exchange of wellbeing information, which enormously further develop the patient's personal satisfaction while at the same time reducing treatment expenses for both the patient and the medical care suppliers (telemedicine). For individuals' wellbeing, a cutting edge individual wellbeing record framework is fundamental. Here, there are still difficulties with information mix from different EHRs, information interoperability, and guaranteeing that admittance to information is totally under the power of the patient. Some security issues are caused by the Network. To settle these issues, we propose a novel profound learning-based framework that circuits state of the art decentralized innovations like IPFS and blockchain with wellbeing information interoperability principles and advances like FHIR's APIs. In this review, we show that correspondence between private clinical gadgets is as a matter of fact powerless to different cyber attacks. We show how an outer assailant could involve man-in-the-center, replay, bogus information infusion, and refusal of-administration assaults to block delicate wellbeing information stream by capturing the correspondence of the individual clinical gadget. We likewise suggest an Interruption Recognition Framework (IDS), GAN, to additional screen traffic on private clinical gear and spot attacks against them. Our extensive investigation shows that GAN, with an F1-score of 98 % and an accuracy of 98.7 %, can successfully and accurately recognise numerous assaults on personal medical equipment.
Ajay Kumar, Rajiv R. P. Singh, Indranath Chatterjee, Nikita Sharma · 5 authors
Abstract Financially incentivizing health-related behaviors can improve health record outcomes and reduce healthcare costs. Blockchain and IoT technologies can be used to develop safe and transparent incentive schemes in healthcare. IoT devices, such as body sensor networks and wearable sensors, etc. connect the physical and digital world making it easier to collect useful health-related data for further analysis. There are, however, many security and privacy issues with the use of IoT. Some of these IoT security issues can be alleviated using Blockchain technology. Incorporating neuroadaptive technology can result in more personalized and effective therapies using machine learning algorithms and real-time feedback. The research investigates the possibilities of neuroadaptive incentivization in healthcare using Blockchain and IoT on patient health records. The core idea is to incentivize patients to keep their health parameters within standard range thereby reducing the load on healthcare system. In summary, we have presented a proof of concept for neuroadaptive incentivization in healthcare using Blockchain and IoT and discuss various applications and implementation challenges.
Most algorithms deployed in healthcare do not consider gender and sex despite the effect they have on individuals' health differences. Missing these dimensions in healthcare information systems is a point of concern, as neglecting these aspects will inevitably perpetuate existing biases, produce far from optimal results, and may generate diagnosis errors. An often-overlooked community with distinct care values and needs are LGBT+ older adults, which has traditionally been under-surveyed in healthcare and technology design. This paper investigates the implications of missing gender and sex considerations in distributed ledger technologies for LGBT+ older adults. By using the value sensitive design methodology, our contribution shows that many value meanings dear to marginalized communities are not considered in the design of the blockchain, such as LGBT+ older adults' interpretations of trust, privacy, and security. By highlighting the LGBT+ older population values, our contribution alerts us to the potential discriminatory implications of these technologies, which do not consider the gender and sex differences of marginalized, silent populations. Focusing on one community throughout - LGBT+ older adults - we emphasize the need for a holistic, value sensitive design approach for the development of ledger technologies for healthcare, including the values of everyone within the healthcare ecosystem.
The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.
Tomas Bueno Momčilović, Matthias Buchinger, Dian Balta
In its 14 years, distributed ledger technology has attracted increasing attention, investments, enthusiasm, and user base. However, ongoing doubts about its usefulness and recent losses of trust in prominent cryptocurrencies have fueled deeply skeptical assessments. Multiple groups attempted to disentangle the technology from the associated hype and controversy by building workflows for rapid prototyping and informed decision-making, but their mostly isolated work leaves users only with fewer unclarities. To bridge the gaps between these contributions, we develop a holistic analytical framework and open-source web tool for making evidence-based decisions. Consisting of three stages - evaluation, elicitation, and design - the framework relies on input from the users' domain knowledge, maps their choices, and provides an output of needed technology bundles. We apply it to an example clinical use case to clarify the directions of our contribution charts for prototyping, hopefully driving the conversation towards ways to enhance further tools and approaches.