Ethical dimensions in telemedicine - balancing technology, responsible care, and patient protection.

Telemedicine, defined as the practice of delivering healthcare services remotely using information and communications technologies, raises a plethora of ethical considerations. As telemedicine evolves, its ethical dimensions play an increasingly pivotal role in balancing the benefits of advanced technologies, ensuring responsible healthcare practices within telemedicine environments, and safeguarding patient rights. Healthcare providers, patients, policymakers, and technology developers involved in telemedicine encounter numerous ethical challenges that need to be addressed. Key ethical topics include prioritizing the protection of patient rights and privacy, which entails ensuring equitable access to remote healthcare services and maintaining the doctor-patient relationship in virtual settings. Additional areas of focus encompass data security concerns and the quality of healthcare delivery, underscoring the importance of upholding ethical standards in the digital realm. A critical examination of these ethical dimensions highlights the necessity of establishing binding ethical guidelines and legal regulations. These measures could assist stakeholders in formulating effective strategies and methodologies to navigate the complex telemedicine landscape, ensuring adherence to the highest ethical standards and promoting patient welfare. A balanced approach to telemedicine ethics should integrate the benefits of telemedicine with proactive measures to address emerging ethical challenges and should be grounded in a well-prepared and respected ethical framework.

Machine unlearning in brain-inspired neural network paradigms.

Machine unlearning, which is crucial for data privacy and regulatory compliance, involves the selective removal of specific information from a machine learning model. This study focuses on implementing machine unlearning in Spiking Neuron Models (SNMs) that closely mimic biological neural network behaviors, aiming to enhance both flexibility and ethical compliance of AI models. We introduce a novel hybrid approach for machine unlearning in SNMs, which combines selective synaptic retraining, synaptic pruning, and adaptive neuron thresholding. This methodology is designed to effectively eliminate targeted information while preserving the overall integrity and performance of the neural network. Extensive experiments were conducted on various computer vision datasets to assess the impact of machine unlearning on critical performance metrics such as accuracy, precision, recall, and ROC AUC. Our findings indicate that the hybrid approach not only maintains but in some cases enhances the neural network's performance post-unlearning. The results confirm the practicality and efficiency of our approach, underscoring its applicability in real-world AI systems.

Non-fungible tokens (NFTs) in healthcare: a thematic analysis and research agenda.

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.

Orbital learning: a novel, actively orchestrated decentralised learning for healthcare.

A novel collaborative and continual learning across a network of decentralised healthcare units, avoiding identifiable data-sharing capacity, is proposed. Currently available methodologies, such as federated learning and swarm learning, have demonstrated decentralised learning. However, the majority of them face shortcomings that affect their performance and accuracy. These shortcomings include a non-uniform rate of data accumulation, non-uniform patient demographics, biased human labelling, and erroneous or malicious training data. A novel method to reduce such shortcomings is proposed in the present work through selective grouping and displacing of actors in a network of many entities for intra-group sharing of learning with inter-group accessibility. The proposed system, known as Orbital Learning, incorporates various features from split learning and ensemble learning for a robust and secure performance of supervised models. A digital embodiment of the information quality and flow within a decentralised network, this platform also acts as a digital twin of healthcare network. An example of ECG classification for arrhythmia with 6 clients is used to analyse its performance and is compared against federated learning. In this example, four separate experiments are conducted with varied configurations, such as varied age demographics and clients with data tampering. The results obtained show an average area under receiver operating characteristic curve (AUROC) of 0.819 (95% CI 0.784-0.853) for orbital learning whereas 0.714 (95% CI 0.692-0.736) for federated learning. This result shows an increase in overall performance and establishes that the proposed system can address the majority of the issues faced by existing decentralised learning methodologies. Further, a scalability demo conducted establishes the versatility and scalability of this platform in handling state-of-the-art large language models.

E-Health: Security, Privacy, and Ethics Requirements from a National Perspective in I. R. Iran.

This paper explores the security, privacy, and ethical implications of e-health data in Iran's healthcare network. A framework is proposed to ensure security and privacy in electronic health information processing across various institutions. The framework addresses aspects such as software/hardware, communication networks, patient safety, privacy, confidentiality, online health service regulations, commercial and judicial exploitation, and education/research. The study categorizes these requirements into seven main categories to safeguard health-oriented service recipients' security and privacy.

Ethics and responsible AI deployment.

As Artificial Intelligence (AI) becomes more prevalent, protecting personal privacy is a critical ethical issue that must be addressed. This article explores the need for ethical AI systems that safeguard individual privacy while complying with ethical standards. By taking a multidisciplinary approach, the research examines innovative algorithmic techniques such as differential privacy, homomorphic encryption, federated learning, international regulatory frameworks, and ethical guidelines. The study concludes that these algorithms effectively enhance privacy protection while balancing the utility of AI with the need to protect personal data. The article emphasises the importance of a comprehensive approach that combines technological innovation with ethical and regulatory strategies to harness the power of AI in a way that respects and protects individual privacy.

Should We Better Stick to Pen and Paper? An Empirical Investigation on Functionality, Privacy and Data-Security of Physiotherapy Telehealth Applications.

BACKGROUND: Telehealth and mHealth apps become increasingly popular in health professions such as physiotherapy calling for increased awareness on functionality, privacy, and data security. OBJECTIVES: This work presents a functionality, privacy, and data-security evaluation of four telehealth services commonly used in physiotherapy. METHODS: We examined functionality and features, data protection, privacy implementations and data-security with a questionnaire and performed an in-depth investigation of the services. RESULTS: Privacy and security relevant findings such as use of outdated webservers, problems with certificate renewal as well as questionable GDPR compliance were reported. CONCLUSION: Due to the privacy and security relevant findings in this analysis it can be concluded that there is a need for improvement in design, development, operation as well as regulation of telehealth apps and services.

Choosing the right artificial intelligence solutions for your radiology department: key factors to consider.

The rapid evolution of artificial intelligence (AI), particularly in deep learning, has significantly impacted radiology, introducing an array of AI solutions for interpretative tasks. This paper provides radiology departments with a practical guide for selecting and integrating AI solutions, focusing on interpretative tasks that require the active involvement of radiologists. Our approach is not to list available applications or review scientific evidence, as this information is readily available in previous studies; instead, we concentrate on the essential factors radiology departments must consider when choosing AI solutions. These factors include clinical relevance, performance and validation, implementation and integration, clinical usability, costs and return on investment, and regulations, security, and privacy. We illustrate each factor with hypothetical scenarios to provide a clearer understanding and practical relevance. Through our experience and literature review, we provide insights and a practical roadmap for radiologists to navigate the complex landscape of AI in radiology. We aim to assist in making informed decisions that enhance diagnostic precision, improve patient outcomes, and streamline workflows, thus contributing to the advancement of radiological practices and patient care.

The Pulse of AI: Implementation of Artificial Intelligence in Healthcare and its Potential Hazards.

In this editorial, we explore the existing utilization of artificial intelligence (AI) within the healthcare industry, examining both its scope and potential harms if implemented and relied upon on a broader scale. Collaboration among corporations, government bodies, policymakers, and medical experts is essential to address potential concerns, ensuring smooth AI integration into healthcare systems.

Secure smart home architecture for ambient-assisted living using a multimedia Internet of Things based system in smart cities.

Recent advances in smartphones and remote monitoring based on the Internet of Things (IoT) have enabled improved multidimensional intelligent services. The advent of IoT-based wearable and multimedia sensors has prevented millions of mishapsthrough seamless and systematic monitoring. An IoT-based monitoring system is composed of several sensor devices to measure vital signs, fall detection, energy consumption, and visual recognition. As the data collected by the sensors are transmitted to cloud storage through the Internet, data security is a major concern when transmitting data from remote locations. To improve data security and prediction accuracy, in this study, we proposed a smart and secure multimedia IoT monitoring system for smart homes backed up by smart grid supervisory control and data acquisition (SCADA). The proposed system employs state-of-the-art IoT microcontrollers and hardware devices and integrates them in a manner that significantly affects the accuracy and speed of the entire system. Furthermore, the information gathered from IoT is securely transferred through private channels and stored on the cloud, which can be accessed authentically and reliably using an information system built into an IoT application. The output was extensively compared in terms of power consumption and delivery ratio, which were based on the values collected with sequence numbers. The comparative analysis demonstrated that the proposed approach provides increased prediction accuracy and better security. Hence, the proposed powerefficient prototype model monitors the entire smart home environment in real time and serves as an early warning system for critical situations.

Mechanofluorochromic behaviors and data security protection properties of salicylaldimine-based difluoroboron complexes with different aryl substituents.

To further explore the relationship between aryl substituents and mechanofluorochromic (MFC) behaviors, four salicylaldimine-based difluoroboron complexes (ts-Ph BF2, ts-Ph-NA BF2, ts-2NA BF2, and ts-triphenylamine [TPA] BF2), including aromatic substituents with different steric hindrance effects, were designed and successfully synthesized. Four complexes with twisted molecular conformation displayed intramolecular charge transfer and aggregation-induced emission properties. Under external mechanical stimuli, the as-synthesized powders of ts-Ph BF2, ts-Ph-NA BF2, and ts-TPA BF2 exhibited redshift fluorescence emission behaviors, and ts-Ph BF2 and ts-TPA BF2 could be recovered to original shifts by fuming, but ts-Ph-NA BF2 displayed irreversible switching. ts-2NA BF2 had no change during the grinding and fuming processes. The results indicated that the MFC behaviors could be attributed to the phase transformation between the well-defined crystalline and disordered amorphous states by X-ray diffraction measurement. Further research illustrated that ts-TPA BF2 with the most significant MFC phenomenon could be applied in data security protection in ink-free rewritable paper.

Toward robust and privacy-enhanced facial recognition: A decentralized blockchain-based approach with GANs and deep learning.

In recent years, the extensive use of facial recognition technology has raised concerns about data privacy and security for various applications, such as improving security and streamlining attendance systems and smartphone access. In this study, a blockchain-based decentralized facial recognition system (DFRS) that has been designed to overcome the complexities of technology. The DFRS takes a trailblazing approach, focusing on finding a critical balance between the benefits of facial recognition and the protection of individuals' private rights in an era of increasing monitoring. First, the facial traits are segmented into separate clusters which are maintained by the specialized node that maintains the data privacy and security. After that, the data obfuscation is done by using generative adversarial networks. To ensure the security and authenticity of the data, the facial data is encoded and stored in the blockchain. The proposed system achieves significant results on the CelebA dataset, which shows the effectiveness of the proposed approach. The proposed model has demonstrated enhanced efficacy over existing methods, attaining 99.80% accuracy on the dataset. The study's results emphasize the system's efficacy, especially in biometrics and privacy-focused applications, demonstrating outstanding precision and efficiency during its implementation. This research provides a complete and novel solution for secure facial recognition and data security for privacy protection.

Ethical and Quality of Care-Related Challenges of Digital Health Twins in Older Care Settings: Protocol for a Scoping Review.

BACKGROUND: Digital health twins (DHTs) have been evolving with their diverse applications in medicine, specifically in older care settings, with the increasing demands of older adults. DHTs have already contributed to improving the quality of dementia and trauma care, cardiac treatment, and health care services for older individuals. Despite its many benefits, the optimum implementation of DHTs has faced several challenges associated with ethical issues, quality of care, management and leadership, and design considerations in older care settings. Since the need for such care is continuously rising and there is evident potential for DHTs to meet those needs, this review aims to map key concepts to address the gaps in the research knowledge to improve DHT implementation. OBJECTIVE: The review aims to compile and synthesize the best available evidence regarding the problems encountered by older adults and care providers associated with the application of DHTs. The synthesis will collate the evidence of the issues associated with quality of care, the ethical implications of DHTs, and the strategies undertaken to overcome those challenges in older care settings. METHODS: The review will follow the Joanna Briggs Institute (JBI) methodology. The published studies will be searched through CINAHL, MEDLINE, JBI, and Web of Science, and the unpublished studies through Mednar, Trove, OCLC WorldCat, and Dissertations and Theses. Studies published in English from 2002 will be considered. This review will include studies of older individuals (aged 65 years or older) undergoing care delivery associated with DHTs and their respective care providers. The concept will include the application of the technology, and the context will involve studies based on the older care setting. A broad scope of evidence, including quantitative, qualitative, text and opinion studies, will be considered. A total of 2 independent reviewers will screen the titles and abstracts and then review the full text. Data will be extracted from the included studies using a data extraction tool developed for this study. RESULTS: The results will be presented in a PRISMA-ScR (Preferred Reporting Items for Systematic Review and Meta-Analysis extension for Scoping Reviews) flow diagram. A draft charting table will be developed as a data extraction tool. The results will be presented as a "map" of the data in a logical, diagrammatic, or tabular form in a descriptive format. CONCLUSIONS: The evidence synthesis is expected to uncover the shreds of evidence required to address the ethical and care quality-related challenges associated with applying DHTs. A synthesis of various strategies used to overcome identified challenges will provide more prospects for adopting them elsewhere and create a resource allocation model for older individuals. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/51153.

Survey of Medical Applications of Federated Learning.

OBJECTIVES: Medical artificial intelligence (AI) has recently attracted considerable attention. However, training medical AI models is challenging due to privacy-protection regulations. Among the proposed solutions, federated learning (FL) stands out. FL involves transmitting only model parameters without sharing the original data, making it particularly suitable for the medical field, where data privacy is paramount. This study reviews the application of FL in the medical domain. METHODS: We conducted a literature search using the keywords "federated learning" in combination with "medical," "healthcare," or "clinical" on Google Scholar and PubMed. After reviewing titles and abstracts, 58 papers were selected for analysis. These FL studies were categorized based on the types of data used, the target disease, the use of open datasets, the local model of FL, and the neural network model. We also examined issues related to heterogeneity and security. RESULTS: In the investigated FL studies, the most commonly used data type was image data, and the most studied target diseases were cancer and COVID-19. The majority of studies utilized open datasets. Furthermore, 72% of the FL articles addressed heterogeneity issues, while 50% discussed security concerns. CONCLUSIONS: FL in the medical domain appears to be in its early stages, with most research using open data and focusing on specific data types and diseases for performance verification purposes. Nonetheless, medical FL research is anticipated to be increasingly applied and to become a vital component of multi-institutional research.

Evaluating Health Care Professionals' Readiness for e-Health Adoption in the Context of the COVID-19 Pandemic: A Georgian Perspective.

Background: The COVID-19 pandemic has accelerated the adoption of Electronic health (e-Health), leveraging technologies such as telemedicine, electronic health records, artificial intelligence, and patient engagement platforms. This transformation underscores e-Health's role in providing efficient, patient-centered care. Our study explores health care professionals' readiness for these technologies, emphasizing the need for tailored education in this evolving landscape. Methods: In our study, conducted between February and March 2023, we administered a questionnaire-based survey to 500 staff members (82.4% female, 17.6% male) aged 25-70 from medical universities in Tbilisi, Georgia. The structured questionnaire covered topics such as computer literacy, telemedicine awareness, patient data security, and ethical considerations. We employed SPSS v21.0 for data analysis, encompassing descriptive statistics and thematic analysis of open-ended responses. Results: Our study included 500 participants categorized into five age groups. Notably, 31% considered themselves computer "experts," while 69% rated their skills as "intermediate" or "advanced." Furthermore, 85% used computers professionally, with 33% having practical computer training. Interestingly, 59% expressed interest in information technology training. Regarding e-Health, 15% believed it involves remote communication between health care professionals and patients, while 42% considered it "correct," and 37% "might be correct." Concerning its application in managing patients, opinions varied. In terms of e-Health's integration into Georgia's health care, responses ranged. Regarding patient data safety, participants exhibited diverse views. Finally, opinions on the necessity of informed consent for e-Health applications varied among participants. Conclusions: Our study explores health care professionals' readiness for e-Health adoption during the COVID-19 pandemic. It reveals varying computer literacy levels, a willingness to learn, differing views on e-Health applications, and mixed opinions on its integration into Georgian health care. These findings emphasize the need for clear e-Health terminology, education, tailored approaches, and a focus on data privacy and informed consent. Overall, e-Health's transformative role in modern health care is underscored.

Research on Oracle Technology Based on Multi-Threshold Aggregate Signature Algorithm and Enhanced Trustworthy Oracle Reputation Mechanism.

In the realm of IoT sensor data security, particularly in areas like agricultural product traceability, the challenges of ensuring product origin and quality are paramount. This research presents a novel blockchain oracle solution integrating an enhanced MTAS signature algorithm derived from the Schnorr signature algorithm. The key improvement lies in the automatic adaptation of flexible threshold values based on the current scenario, catering to diverse security and efficiency requirements. Utilizing the continuously increasing block height of the blockchain as a pivotal blinding parameter, our approach strengthens signature verifiability and security. By combining the block height with signature parameters, we devise a distinctive signing scheme reliant on a globally immutable timestamp. Additionally, this study introduces a reliable oracle reputation mechanism for monitoring and assessing oracle node performance, maintaining both local and global reputations. This mechanism leverages smart contracts to evaluate each oracle's historical service, penalizing or removing nodes engaged in inappropriate behaviors. Experimental results highlight the innovative contributions of our approach to enhancing on-chain efficiency and fortifying security during the on-chain process, offering promising advancements for secure and efficient IoT sensor data transmission.

Towards Precision Medicine in Spinal Surgery: Leveraging AI Technologies.

This critique explores the implications of integrating artificial intelligence (AI) technology, specifically OpenAI's advanced language model GPT-4 and its interface, ChatGPT, into the field of spinal surgery. It examines the potential effects of algorithmic bias, unique challenges in surgical domains, access and equity issues, cost implications, global disparities in technology adoption, and the concept of technological determinism. It posits that biases present in AI training data may impact the quality and equity of healthcare outcomes. Challenges related to the unique nature of surgical procedures, including real-time decision-making, are also addressed. Concerns over access, equity, and cost implications underscore the potential for exacerbated healthcare disparities. Global disparities in technology adoption highlight the importance of global collaboration, technology transfer, and capacity building. Finally, the critique challenges the notion of technological determinism, emphasizing the continued importance of human judgement and patient-care provider relationship in healthcare. The critique calls for a comprehensive evaluation of AI technology integration in healthcare to ensure equitable and quality care.

Researchers' Perspectives Regarding Ethical Issues of Biobank Research in the Arab Region.

Background: The recent expansion of genomic biobank research in the Arab region in the Middle East North Africa has raised complex ethical and regulatory issues. However, there is a lack of studies regarding the views of Arab researchers involved in such research. We aimed to assess the perceptions and attitudes of Arab researchers regarding these issues in biobank research. Methods: We developed a questionnaire to assess the perceptions and attitudes regarding genetic research of researchers from Egypt, Sudan, Morocco, and Jordan. The questionnaire requested demographic data, perceptions, and attitudes regarding the collection, storage, and use of biospecimens and data, the use of broad consent, data security, data sharing, and community engagement. We used multiple linear regressions to identify predictors of perceptions and attitudes. Results: We recruited 383 researchers. Researchers favored equally the use of broad and tiered consent (44.1% and 39.1%, respectively). Most respondents agreed with the importance of confidentiality protections to ensure data security (91.8%). However, lower percentages were seen regarding the importance of community engagement (64.5%), data sharing with national colleagues and international partners (60.9% and 41.1%, respectively), and biospecimen sharing with national colleagues and international partners (59.9% and 36.2%, respectively). Investigators were evenly split on whether the return of individual research results should depend on the availability or not of a medical intervention that can be offered to address the genetic anomaly (47.5% and 46.4%, respectively). Predictors of attitudes toward biospecimen research included serving on Research Ethics Committees, prior research ethics training, and affiliation with nonacademic institutions. Conclusions: We recommend further exploratory research with researchers regarding the importance of community engagement and to address their concerns about data sharing, with researchers within and outside their countries.

Geographical and practical challenges in the implementation of digital health passports for cross-border COVID-19 pandemic management: a narrative review and framework for solutions.

The rapid global spread of infectious diseases, epitomized by the recent COVID-19 pandemic, has highlighted the critical need for effective cross-border pandemic management strategies. Digital health passports (DHPs), which securely store and facilitate the sharing of critical health information, including vaccination records and test results, have emerged as a promising solution to enable safe travel and access to essential services and economic activities during pandemics. However, the implementation of DHPs faces several significant challenges, both related to geographical disparities and practical considerations, necessitating a comprehensive approach for successful global adoption. In this narrative review article, we identify and elaborate on the critical geographical and practical barriers that hinder global adoption and the effective utilization of DHPs. Geographical barriers are complex, encompassing disparities in vaccine access, regulatory inconsistencies, differences across countries in data security and users' privacy policies, challenges related to interoperability and standardization, and inadequacies in technological infrastructure and limited access to digital technologies. Practical challenges include the possibility of vaccine contraindications and breakthrough infections, uncertainties surrounding natural immunity, and limitations of standard tests in assessing infection risk. To address geographical disparities and enhance the functionality and interoperability of DHPs, we propose a framework that emphasizes international collaboration to achieve equitable access to vaccines and testing resources. Furthermore, we recommend international cooperation to establish unified vaccine regulatory frameworks, adopting globally accepted standards for data privacy and protection, implementing interoperability protocols, and taking steps to bridge the digital divide. Addressing practical challenges requires a meticulous approach to assessing individual risk and augmenting DHP implementation with rigorous health screenings and personal infection prevention measures. Collectively, these initiatives contribute to the development of robust and inclusive cross-border pandemic management strategies, ultimately promoting a safer and more interconnected global community in the face of current and future pandemics.

From Nanobots to Neural Networks: Multifaceted Revolution of Artificial Intelligence in Surgical Medicine and Therapeutics.

This comprehensive exploration unveils the transformative potential of Artificial Intelligence (AI) within medicine and surgery. Through a meticulous journey, we examine AI's current applications in healthcare, including medical diagnostics, surgical procedures, and advanced therapeutics. Delving into the theoretical foundations of AI, encompassing machine learning, deep learning, and Natural Language Processing (NLP), we illuminate the critical underpinnings supporting AI's integration into healthcare. Highlighting the symbiotic relationship between humans and machines, we emphasize how AI augments clinical capabilities without supplanting the irreplaceable human touch in healthcare delivery. Also, we'd like to briefly mention critical findings and takeaways they can expect to encounter in the article. A thoughtful analysis of the economic, societal, and ethical implications of AI's integration into healthcare underscores our commitment to addressing critical issues, such as data privacy, algorithmic transparency, and equitable access to AI-driven healthcare services. As we contemplate the future landscape, we project an exciting vista where more sophisticated AI algorithms and real-time surgical visualizations redefine the boundaries of medical achievement. While acknowledging the limitations of the present research, we shed light on AI's pivotal role in enhancing patient engagement, education, and data security within the burgeoning realm of AI-driven healthcare.