Blockchain Papers

Suyogita Singh, Satya Bhushan Verma

In the early months of 2020, a fast-spreading outbreak was brought about by the new virus SARS-CoV-2. The uncontrolled spread, which led to a pandemic, illustrated the healthcare system’s slow response time to public health emergencies at that time. Blockchain technology was anticipated to be crucial in the effort to contain the COVID-19 pandemic. In that review, many potential blockchain applications were discovered; however, the majority of them were still in their infancy, and it couldn’t yet be predicted how they could contribute to the fight against COVID-19 through the use of platforms, access kinds, and consensus algorithms. Modern innovations such as blockchain and artificial intelligence (AI) were shown to be promising in limiting the spread of a virus. Blockchain could specifically aid in the battle against pandemics by supporting early epidemic identification, assuring the ordering of clinical information, and maintaining a trustworthy medical chain during disease tracing. AI also offered smart forms of diagnosing coronavirus therapies and supported the development of pharmaceuticals. Blockchain and AI software for epidemic and pandemic containment were analyzed in that research. First, a new conceptual strategy was proposed to tackle COVID-19 through an architecture that fused AI with blockchain. State-of-the-art research on the benefits of blockchain and AI in COVID-19 containment was then reviewed. Recent initiatives and use cases developed to tackle the coronavirus pandemic were also presented. A case study using federated intelligence for COVID-19 identification was also provided. Finally, attention was drawn to problems and prospective directions for further investigation into future coronavirus-like wide-ranging scenarios.

Meenakshi Aggarwal, Vikas Khullar, Nitin Goyal

Federated learning (FL) represents an advanced approach to tackling the issues linked with training machine learning (ML) models using distributed data while upholding privacy and security. It functions by enabling collaborative model training across a network of edge devices or servers, all without the need to transfer raw data. In place of sending data to a central server, which could potentially compromise privacy, federated learning empowers individual devices to conduct local training on their respective data. These updates are subsequently combined to develop an enhanced global model over multiple iteration. Additionally, as artificial intelligence (AI) becomes pervasive in novel application areas, concerns about the privacy of data and users are on the rise. This article offers an in-depth analysis of the advancements in FL, covering a wide array of topics including methodologies, applications, and challenges. By sidestepping the need to transfer raw data and instead focusing on sharing model updates or gradients, FL ensures the preservation of privacy and the efficient utilization of resources. Additionally, we investigate the diverse spectrum of application domains where FL holds significance. Instances encompass healthcare, finance, agriculture, education, Internet of Things (IoT), and industrial processes, all benefiting from the capacity of federated learning to harness data from decentralized sources without compromising data security. This article addresses complications such as model diversity, Non-IID (independent and identically distributed) data distribution, communication complexities, and security vulnerabilities. Furthermore, we discuss considerations related to regulatory compliance and ethics within the context of federated learning, particularly as data privacy regulations intensify.

Khulekani Sibanda, Patrick Ndayizigamiye, Hossana Twinomurinzi

Introduction: In the big data era, where corporations commodify health data, non-fungible tokens (NFTs) present a transformative avenue for patient empowerment and control. NFTs are unique digital assets on the blockchain, representing ownership of digital objects, including health data. By minting their data as NFTs, patients can track access, monetize its use, and build secure, private health information systems. However, research on NFTs in healthcare is in its infancy, warranting a comprehensive review. Methods: This study conducted a systematic literature review and thematic analysis of NFTs in healthcare to identify use cases, design models, and key challenges. Five multidisciplinary research databases (Scopus, Web of Science, Google Scholar, IEEE Explore, Elsevier Science Direct) were searched. The approach involved four stages: paper collection, inclusion/exclusion criteria application, screening, full-text reading, and quality assessment. A classification and coding framework was employed. Thematic analysis followed six steps: data familiarization, initial code generation, theme searching, theme review, theme definition/naming, and report production. Results: Analysis of 19 selected papers revealed three primary use cases: patient-centric data management, supply chain management for data provenance, and digital twin development. Notably, most solutions were prototypes or frameworks without real-world implementations. Four overarching themes emerged: data governance (ownership, tracking, privacy), data monetization (commercialization, incentivization, sharing), data protection, and data storage. The focus lies on user-controlled, private, and secure health data solutions. Additionally, data commodification is explored, with mechanisms proposed to incentivize data maintenance and sharing. NFTs are also suggested for tracking medical products in supply chains, ensuring data integrity and provenance. Ethereum and similar platforms dominate NFT minting, while compact NFT storage options are being explored for faster data access. Conclusion: NFTs offer significant potential for secure, traceable, decentralized healthcare data exchange systems. However, challenges exist, including dependence on blockchain, interoperability issues, and associated costs. The review identified research gaps, such as developing dual ownership models and data pricing strategies. Building an open standard for interoperability and adoption is crucial. The scalability, security, and privacy of NFT-backed healthcare applications require further investigation. Thus, this study proposes a research agenda for adopting NFTs in healthcare, focusing on governance, storage models, and perceptions.

Wail Zellagui, Abdessamad Imine, Yamina Tadjeddine

Recent advances in the field of large language models (LLMs), particularly the ChatGPT family, have given rise to a powerful and versatile machine interlocutor, packed with knowledge and challenging our understanding of learning. This interlocutor is a double-edged sword: it can be harnessed for a wide variety of beneficial tasks, but it can also be used to cause harm. This study explores the complicated interaction between ChatGPT and the growing problem of cryptocurrency fraud. Although ChatGPT is known for its adaptability and ethical considerations when used for harmful purposes, we highlight the deep connection that may exist between ChatGPT and fraudulent actions in the volatile cryptocurrency ecosystem. Based on our categorization of cryptocurrency frauds, we show how to influence outputs, bypass ethical terms, and achieve specific fraud goals by manipulating ChatGPT prompts. Furthermore, our findings emphasize the importance of realizing that ChatGPT could be a valuable instructor even for novice fraudsters, as well as understanding and safely deploying complex language models, particularly in the context of cryptocurrency frauds. Finally, our study underlines the importance of using LLMs responsibly and ethically in the digital currency sector, identifying potential risks and resolving ethical issues. It should be noted that our work is not intended to encourage and promote fraud, but rather to raise awareness of the risks of fraud associated with the use of ChatGPT.

Dr. Mrs. Yasmin Z. Shaikh

No abstract is available for this record.

Isabirye Edward Kezron

The rapid advancement and integration of Artificial Intelligence (AI) across critical sectors — including healthcare, finance, defense, and infrastructure — have exposed an often-overlooked risk: vulnerabilities within the AI supply chain. This research examines the security challenges and potential threats affecting AI model development and deployment, focusing on adversarial attacks, data poisoning, model theft, and compromised third-party components. By dissecting the AI supply chain into its core stages — data sourcing, model training, deployment, and maintenance — this study identifies key entry points for malicious actors. The paper proposes a multi-layered security framework combining blockchain-based data provenance, federated learning for decentralized model training, and zero-trust architecture to ensure secure deployment. Additionally, it explores how adversarial training, model watermarking, and real-time anomaly detection can mitigate risks without sacrificing model performance. Case studies of high-profile AI breaches are analyzed to demonstrate the consequences of unsecured pipelines, emphasizing the urgency of securing AI systems.

Giuseppe Rovere, Francesco Bosco, Angelo Miceli, Salvatore Ratano · 10 authors

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.

Stephan Rau, Alexander Rau, Johanna Nattenmüller, Anna Maria Fink · 7 authors

BACKGROUND: We investigated the potential of an imaging-aware GPT-4-based chatbot in providing diagnoses based on imaging descriptions of abdominal pathologies. METHODS: Utilizing zero-shot learning via the LlamaIndex framework, GPT-4 was enhanced using the 96 documents from the Radiographics Top 10 Reading List on gastrointestinal imaging, creating a gastrointestinal imaging-aware chatbot (GIA-CB). To assess its diagnostic capability, 50 cases on a variety of abdominal pathologies were created, comprising radiological findings in fluoroscopy, MRI, and CT. We compared the GIA-CB to the generic GPT-4 chatbot (g-CB) in providing the primary and 2 additional differential diagnoses, using interpretations from senior-level radiologists as ground truth. The trustworthiness of the GIA-CB was evaluated by investigating the source documents as provided by the knowledge-retrieval mechanism. Mann-Whitney U test was employed. RESULTS: The GIA-CB demonstrated a high capability to identify the most appropriate differential diagnosis in 39/50 cases (78%), significantly surpassing the g-CB in 27/50 cases (54%) (p = 0.006). Notably, the GIA-CB offered the primary differential in the top 3 differential diagnoses in 45/50 cases (90%) versus g-CB with 37/50 cases (74%) (p = 0.022) and always with appropriate explanations. The median response time was 29.8 s for GIA-CB and 15.7 s for g-CB, and the mean cost per case was $0.15 and $0.02, respectively. CONCLUSIONS: The GIA-CB not only provided an accurate diagnosis for gastrointestinal pathologies, but also direct access to source documents, providing insight into the decision-making process, a step towards trustworthy and explainable AI. Integrating context-specific data into AI models can support evidence-based clinical decision-making. RELEVANCE STATEMENT: A context-aware GPT-4 chatbot demonstrates high accuracy in providing differential diagnoses based on imaging descriptions, surpassing the generic GPT-4. It provided formulated rationale and source excerpts supporting the diagnoses, thus enhancing trustworthy decision-support. KEY POINTS: • Knowledge retrieval enhances differential diagnoses in a gastrointestinal imaging-aware chatbot (GIA-CB). • GIA-CB outperformed the generic counterpart, providing formulated rationale and source excerpts. • GIA-CB has the potential to pave the way for AI-assisted decision support systems.

Zhaoyan Liu, Min Shu, Wei Zhu

Due to spectacular gains during periods of rapid price increase and unpredictably large drops, Bitcoin has become a popular emergent asset class over the past few years. In this paper, we are interested in predicting the crashes of Bitcoin market. To tackle this task, we propose a framework for deep learning time series classification based on contrastive learning. The proposed framework is evaluated against six machine learning (ML) and deep learning (DL) baseline models, and outperforms them by 15.8% in balanced accuracy. Thus, we conclude that the contrastive learning strategy significantly enhance the model’s ability of extracting informative representations, and our proposed framework performs well in predicting Bitcoin crashes.

Jason P. Rothberg, Colin Keogh, Yury Rusinovich

Xavatar is a media, educational, and therapeutic platform specializing in immersive virtual reality (VR) and augmented reality (AR) content, as well as seamless interconnectivity across various devices such as mobiles, tablets, and computers. It is aimed at improving the lives of patients with chronic mobility and communication disorders, including dementia, Alzheimer's, autism, chronic immobility, isolation, and long-term hospitalization. The project represents a fusion of digital technologies including the Metaverse, artificial intelligence (AI), and Web3, all designed to enhance healthcare interactions and patient support. This opinion piece explores the transformative potential of Xavatar, highlighting its role in shaping future healthcare landscapes through innovative, empathetic, and engaging digital solutions.

Kartheek Chandu

Abstract: Criminal activities in India are on the rise, with many incidents going unreported. Despite the availability of an online portal for storing the First Information Reports (FIRs) handwritten FIRs always will remain common due to some of the traditional methods and practices. And probably in most cases, the complainants should personally visit the police stations in order to file a offense report. Crime and Criminal Tracking Network and Systems (CCTNS) was launched in 2010 for national wide e-governance, it will operates on a centralized system and it is particularly limited to some individual states. Therefore, there is a need for the decentralized solution in order to ensure failure of a single point and secure managing of criminal complaints from the unauthorized access.

Xuetao Pu, Rong Jiang, Zhiming Song, Zhihong Liang · 5 authors

The rapid development of the Hospital Information System has significantly enhanced the convenience of medical research and the management of medical information. However, the internal misuse and privacy leakage of medical big data are critical issues that need to be addressed in the process of medical research and information management. Access control serves as a method to prevent data misuse and privacy leakage. Nevertheless, traditional access control methods, limited by their single usage scenario and susceptibility to single point failures, fail to adapt to the polymorphic, real-time, and sensitive characteristics of medical big data scenarios. This paper proposes a smart contracts and risk-based access control model (SCR-BAC). This model integrates smart contracts with traditional risk-based access control and deploys risk-based access control policies in the form of smart contracts into the blockchain, thereby ensuring the protection of medical data. The model categorizes risk into historical and current risk, quantifies the historical risk based on the time decay factor and the doctor's historical behavior, and updates the doctor's composite risk value in real time. The access control policy, based on the comprehensive risk, is deployed into the blockchain in the form of a smart contract. The distributed nature of the blockchain is utilized to automatically enforce access control, thereby resolving the issue of single point failures. Simulation experiments demonstrate that the access control model proposed in this paper effectively curbs the access behavior of malicious doctors to a certain extent and imposes a limiting effect on the internal abuse and privacy leakage of medical big data.

Jyotiraditya Gandhi, Krishnakumar Maurya, Viveksingh Panwar, Utpalkumar Patel · 6 authors

Abstract: In recent years, the integration of blockchain technology with healthcare systems has garnered considerable attention due to its potential to enhance security, privacy, and interoperability in managing patient medical records. This paper proposes a novel approach to patient medical record management by leveraging Ethereum blockchain and InterPlanetary File System (IPFS) for storage, within a government-controlled framework. The system ensures secure and immutable storage of patients' medical records, accessible only by verified medical professionals, thus facilitating informed diagnosis and treatment. Additionally, the platform provides mechanisms for patient recourse in case of inaccuracies, as well as potential integration with insurance agencies. Furthermore, the proposed system envisages a future extension to facilitate anonymized data sharing with the scientific community, thereby contributing to advancements in medical research. This paper provides a comprehensive academic description of the proposed approach, discussing its technical architecture, security measures, regulatory framework, and potential impact on healthcare delivery and research.

Guanyi Wang, Chen Chen, Ziyu Jiang, Gang Li · 6 authors

CryptoKitties, a trendy game on Ethereum that is an open-source public blockchain platform with a smart contract function, brought nonfungible tokens (NFTs) into the public eye in 2017. NFTs are popular because of their nonfungible properties and their unique and irreplaceable nature in the real world. The embryonic form of NFTs can be traced back to a P2P network protocol improved based on Bitcoin in 2012 that can realize decentralized digital asset transactions. NFTs have recently gained much attention and have shown an unprecedented explosive growth trend. Herein, the concept of digital asset NFTs is introduced into the medical and health field to conduct a subversive discussion on biobank operations. By converting biomedical data into NFTs, the collection and circulation of samples can be accelerated, and the transformation of resources can be promoted. In conclusion, the biobank can achieve sustainable development through "decentralization."

Krishan Kansal

Abstract: In the era of digital innovation, Artificial Intelligence (AI) has emerged as a pivotal catalyst, unlocking new avenues for the evolution of Web 3.0. Web 3.0 signifies the next phase of the internet, characterized by decentralized structures, peer-topeer networks, and cutting-edge technologies like blockchain and smart contracts. This research provides an in-depth exploration of the role played by AI in shaping Web 3.0, delving into both its opportunities and challenges. AI proves instrumental in processing and analysing vast datasets with increased efficiency, fostering intelligent decision-making and insightful outcomes. The paper extensively covers essential Web 3.0 concepts and technologies, encompassing the Semantic Web and ontologies, and underscores AI's transformative potential across diverse industries such as healthcare, finance, and education. An analysis of the challenges posed by AI in the Web 3.0 landscape, including issues of data privacy, bias, trust, and ethics, is presented. Furthermore, the research examines the broader societal implications of AI in Web 3.0. Conclusively, the paper outlines future directions and implications of AI within the Web 3.0 framework, proposing potential areas for subsequent research. By contributing to a comprehensive understanding of AI's impact on web development and its broader societal implications, this research aims to guide future endeavours in this dynamic field.

Tarandeep Kaur Bhatia, Prerana, Sudhanshu Singh, Navya Saluja · 5 authors

INTRODUCTION: An analysis of the convergence of blockchain and artificial intelligence (AI) technology demonstrates how these technologies can work together to revolutionize data management across a wide range of industries with their synergistic potential. OBJECTIVES: This paper discusses the integration of blockchain and artificial intelligence, the authors present an innovative framework that takes advantage of their strengths. As a result of blockchain's immutability and transparency, data can be securely stored and shared within this framework, making it ideal for sectors such as healthcare, finance, and supply chain. METHODS: To begin with, the paper discusses blockchain and artificial intelligence individually, emphasizing their respective advantages in decentralized data storage and intelligent decision-making. Blockchain-AI convergence is inevitable as both deal with data and value. RESULTS: As a result, the research paper highlights how blockchain and AI technologies can be transformed into transformative technologies. CONCLUSION: Using the synergistic framework presented in this paper, data management can be made more secure, transparent, and intelligent, with implications that go beyond traditional industries into emerging fields like the Internet of Things (IoT) and smart cities.

Naresh Kshetri, Rahul Mishra, Mir Mehedi Rahman, Tanja Steigner

In the ever-evolving healthcare sector, the widespread adoption of Internet of Things and wearable technologies facilitates remote patient monitoring. However, the existing client/server infrastructure poses significant security and privacy challenges, necessitating strict adherence to healthcare data regulations. To combat these issues, a decentralized approach is imperative, and blockchain technology emerges as a compelling solution for strengthening Internet of Things and medical systems security. This paper introduces HNMblock, a model that elevates the realms of epidemiological monitoring, medical system security, and wellness enhancement. By harnessing the transparency and immutability inherent in blockchain, HNMblock empowers real-time, tamper-proof tracking of epidemiological data, enabling swift responses to disease outbreaks. Furthermore, it fortifies the security of medical systems through advanced cryptographic techniques and smart contracts, with a paramount focus on safeguarding patient privacy. HNMblock also fosters personalized health care, encouraging patient involvement and data-informed decision-making. The integration of blockchain within the healthcare domain, as exemplified by HNMblock, holds the potential to revolutionize data management, epidemiological surveillance, and wellness, as meticulously explored in this research article.

Et al. Satish V. Kakade

Blockchain technology has been incorporated into the Healthcare Internet of Things (IoT) landscape as a revolutionary solution to tackle issues related to the sharing of medical records. This paper presents an innovative method that utilizes Temporal Blockchain for the purpose of Provenance Tracking. The introductory section provides context by delineating the significance of trust and transparency in medical data sharing within the healthcare IoT ecosystem. The study examines current blockchain solutions, delving into frameworks such as Hyperledger Fabric, Ethereum, Corda, and specialized approaches like temporal blockchain. The paper examines the difficulties associated with tracking the origin of data, concerns regarding privacy, problems related to scalability, and the need to comply with regulations. These challenges provide the context for the proposed methodology. The main emphasis is on Temporal Blockchain, integrating temporal elements to improve the tracking of origin and history. The evaluation parameters, such as security, provenance tracking, scalability, interoperability, privacy compliance, and performance, undergo a thorough assessment. The attained values demonstrate a strong emphasis on security at a high level, thorough tracking of origin and history, and strict adherence to privacy regulations. Nevertheless, the need for scalability and interoperability necessitates meticulous consideration. The study showcases the capacity of Temporal Blockchain to establish trust and enhance transparency in the sharing of medical records. The future scope focuses on tackling scalability challenges, improving interoperability, and making continuous optimization efforts. The proposed approach highlights notable accomplishments and emphasizes the continuous development and collaborative aspect of Blockchain-Based Medical Record Sharing in Healthcare IoT.

K. Murthy, S N Prasad

Neurological disorders are a significant health challenge globally, affecting millions of individuals and imposing a considerable economic burden on healthcare systems. Early and accurate diagnosis plays a crucial role in improving patient outcomes and managing these disorders effectively. This abstract presents a novel approach that combines blockchain technology with deep learning algorithms to enhance the detection of neurological disorders. The proposed system leverages the decentralized and transparent nature of blockchain to securely store and share medical data, enabling seamless collaboration among healthcare providers, researchers, and patients. This infrastructure ensures data integrity, privacy, and accessibility, addressing critical concerns in medical data management. Furthermore, the deep learning approach employs advanced neural network architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyze large-scale neurological data, including medical images, electroencephalograms (EEGs), and clinical records. By leveraging the power of deep learning, the system can automatically extract relevant features and patterns from complex neurological data, enabling accurate diagnosis and early detection of various disorders. The integration of blockchain and deep learning offers several advantages. Firstly, it facilitates secure and decentralized storage of medical data, ensuring patient privacy and data integrity. Secondly, it enables seamless data sharing and collaboration among multiple stakeholders, promoting knowledge exchange and enhancing research capabilities. Lastly, deep learning algorithms improve the accuracy and efficiency of neurological disorder detection, enabling timely interventions and personalized treatment plans. The proposed system holds great potential in revolutionizing the field of neurological disorder diagnosis and management. By leveraging the combined power of blockchain and deep learning, healthcare providers can enhance their diagnostic capabilities, leading to improved patient outcomes, reduced healthcare costs, and accelerated research advancements. However, further research and development are necessary to address technical challenges, scalability issues, and regulatory considerations to realize the full potential of this innovative approach.

Lakshay Kumar, Aayush Sharma, Harshal Sharma, Swati Sharma

No abstract is available for this record.

Rocco de Filippis, Abdullah Al Foysal

In an era marked by rapid technological advancement, the fusion of Artificial Intelligence (AI), Machine Learning (ML), and Distributed Ledger Technology (DLT), commonly referred to as blockchain, represents a pioneering frontier in healthcare and psychology.This paper explores the transformative potential of integrating these technologies to reimagine traditional practices and unlock novel approaches to patient care, diagnostics, therapy, and mental health management.Specifically, it investigates the unique and complementary roles that AI, ML, and DLT can play within healthcare and psychology, presenting a detailed roadmap for researchers, practitioners, and stakeholders.Through AI and ML's advanced analytics and predictive capabilities, and blockchain's secure, decentralized data management, this paper demonstrates how these technologies can collectively enhance diagnostic precision, personalize treatment plans, optimize resource allocation, and streamline administrative workflows.Central to this study is a proposed technical architecture, illustrating how AI, ML, and DLT can be integrated within healthcare workflows.This includes using blockchain for secure, verifiable patient data storage and off-chain AI/ML processing for real-time, data-driven insights.Additionally, this paper discusses practical methods, such as zero-knowledge proofs and federated learning, to maintain privacy and regulatory compliance in handling sensitive health data, especially in mental health contexts.Addressing the importance of ethical considerations, this paper highlights best practices in responsible innovation, emphasizing transparency, accountability, and fairness in the deployment of these technologies.Compliance with frameworks like GDPR and HIPAA is discussed as crucial for ensuring patient rights and establishing trust in data handling practices.Moreover, the paper underscores the need for interdisciplinary collaboration, identifying structured models for joint efforts between healthcare professionals, data scientists, and blockchain developers.Examples include cross-disciplinary training sessions, shared project management How to cite this paper:

Dominique Monlezun

Chapter 7 unites the different dimensions explored in each of the earlier chapters into a cohesive whole to understand the artificial intelligence (AI)-powered global public health ecosystem as humanity’s common home: its decentralized organic design (financing and integral sustainable development), framework (data architecture and political economics), inhabitants (culture and demographics), and foundation (ethics and human security balancing national security). It summarizes the key findings for these domains from the earlier chapters while highlighting emblematic AI case uses. It considers financing advances, including in universal health coverage, public–private partnerships, digital global health diplomacy, finance tracking, and value-based health. This chapter moves on to integral sustainable development advances, including in AI for the sustainable development goals, precision agriculture, climate change, affordable clean energy, equity, and generative AI (including ChatGPT). It then considers data architecture advances, including in the United Kingdom’s hybrid data architecture, India’s federated data architecture, swarm learning, gossip learning, blockchain, edge computing, application programming interfaces, augmented public health intelligence, quantum computing, zero-trust security, blockchain, and data solidarity. This chapter then considers political economic advances particularly from the perspective of Political Liberalism–bridging democracies and autocracies, including in data governance models (spanning Europe’s general data protection regulation and Japan’s agile governance), managed strategic competition, and World Health Organization coordination. Finally, this chapter considers AI ethics for the health ecosystem. Particular emphasis is given to how population aging, multicultural diversity, and human security requires more inclusive discussion of diverse perspectives, as through Personalist Social Contract ethics to generate and sustain substantive convergence on the unifying values of human dignity, rights, and sovereignty that then give rise to effective and equitable collective action. This chapter concludes by applying the abovesaid dimensions to concrete AI use cases for the global public health ecosystem that illustrates this integral approach, including ethics by design or embedded AI ethics (within existing ecosystem operations and structures), democratizing AI or personalizing AI (as with end-to-end AI platforms and edge computing expanding and interlinking free and affordable AI services for larger audiences), and ecosystem interoperability (uniting political economic interoperability, data interoperability, and moral interoperability to leverage global resources and insights for local communities leading their own projects).

K. Bagga, Akhilesh Kasturi, Sonaakshi Shri Kunrarasu, Beena B.M.

No abstract is available for this record.

Xiao-Yang Liu, Rongyi Zhu, Daochen Zha, Jiechao Gao · 7 authors

The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical science. However, concerns regarding data privacy have emerged, especially when multiple stakeholders aim to collaboratively enhance LLMs using sensitive data. In this scenario, federated learning becomes a natural choice, allowing decentralized fine-tuning without exposing raw data to central servers. Motivated by this, we investigate how data privacy can be ensured in LLM fine-tuning through practical federated learning approaches, enabling secure contributions from multiple parties to enhance LLMs. Yet, challenges arise: (1) despite avoiding raw data exposure, there is a risk of inferring sensitive information from model outputs, and (2) federated learning for LLMs incurs notable communication overhead. To address these challenges, this article introduces DP-LoRA, a novel federated learning algorithm tailored for LLMs. DP-LoRA preserves data privacy by employing a Gaussian mechanism that adds noise in weight updates, maintaining individual data privacy while facilitating collaborative model training. Moreover, DP-LoRA optimizes communication efficiency via low-rank adaptation, minimizing the transmission of updated weights during distributed training. The experimental results across medical, financial, and general datasets using various LLMs demonstrate that DP-LoRA effectively ensures strict privacy constraints while minimizing communication overhead.