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Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review.Sensors (Basel, Switzerland) Dec 2023The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and... (Review)
Review
The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones' dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.
PubMed: 38202987
DOI: 10.3390/s24010125 -
Patterns (New York, N.Y.) Sep 2022In this study, we analyzed health-advertising tactics of digital medicine companies (n = 5) to evaluate varying types of cross-site-tracking middleware (n = 32) used...
In this study, we analyzed health-advertising tactics of digital medicine companies (n = 5) to evaluate varying types of cross-site-tracking middleware (n = 32) used to extract health information from users. More specifically, we examine how browsing data can be exchanged between digital medicine companies and Facebook for advertising and lead generation and advertising purposes. Our analysis focused on companies offering services to patient advocates in the cancer community who frequently engage on social media. We co-produced this study with public cancer advocates leading or participating in breast cancer groups on Facebook. Following our analysis, we raise policy questions about what constitutes a health privacy breach based on existing federal laws such as the Health Breach Notification Rule and The HIPAA Privacy Rule. We discuss how these common marketing practices enable surveillance and targeting of medical ads to vulnerable patient populations without consent.
PubMed: 36124307
DOI: 10.1016/j.patter.2022.100561 -
BMC Public Health Aug 2013The uptake of HIV testing and counselling services remains low in risk groups around the world. Fear of stigmatisation, discrimination and breach of confidentiality... (Review)
Review
BACKGROUND
The uptake of HIV testing and counselling services remains low in risk groups around the world. Fear of stigmatisation, discrimination and breach of confidentiality results in low service usage among risk groups. HIV self-testing (HST) is a confidential HIV testing option that enables people to find out their status in the privacy of their homes. We evaluated the acceptability of HST and the benefits and challenges linked to the introduction of HST.
METHODS
A literature review was conducted on the acceptability of HST in projects in which HST was offered to study participants. Besides acceptability rates of HST, accuracy rates of self-testing, referral rates of HIV-positive individuals into medical care, disclosure rates and rates of first-time testers were assessed. In addition, the utilisation rate of a telephone hotline for counselling issues and clients` attitudes towards HST were extracted.
RESULTS
Eleven studies met the inclusion criteria (HST had been offered effectively to study participants and had been administered by participants themselves) and demonstrated universally high acceptability of HST among study populations. Studies included populations from resource poor settings (Kenya and Malawi) and from high-income countries (USA, Spain and Singapore). The majority of study participants were able to perform HST accurately with no or little support from trained staff. Participants appreciated the confidentiality and privacy but felt that the provision of adequate counselling services was inadequate.
CONCLUSIONS
The review demonstrates that HST is an acceptable testing alternative for risk groups and can be performed accurately by the majority of self-testers. Clients especially value the privacy and confidentiality of HST. Linkage to counselling as well as to treatment and care services remain major challenges.
Topics: Counseling; Female; HIV Infections; Humans; Male; Patient Acceptance of Health Care; Reagent Kits, Diagnostic; Self Care
PubMed: 23924387
DOI: 10.1186/1471-2458-13-735 -
NPJ Digital Medicine 2020The lack of interoperability in Britain's medical records systems precludes the realisation of benefits generated by increased spending elsewhere in healthcare. Growing... (Review)
Review
The lack of interoperability in Britain's medical records systems precludes the realisation of benefits generated by increased spending elsewhere in healthcare. Growing concerns regarding the security of online medical data following breaches, and regarding regulations governing data ownership, mandate strict parameters in the development of efficient methods to administrate medical records. Furthermore, consideration must be placed on the rise of connected devices, which vastly increase the amount of data that can be collected in order to improve a patient's long-term health outcomes. Increasing numbers of healthcare systems are developing Blockchain-based systems to manage medical data. A Blockchain is a decentralised, continuously growing online ledger of records, validated by members of the network. Traditionally used to manage cryptocurrency records, distributed ledger technology can be applied to various aspects of healthcare. In this manuscript, we focus on how Electronic Medical Records in particular can be managed by Blockchain, and how the introduction of this novel technology can create a more efficient and interoperable infrastructure to manage records that leads to improved healthcare outcomes, while maintaining patient data ownership and without compromising privacy or security of sensitive data.
PubMed: 31934645
DOI: 10.1038/s41746-019-0211-0 -
Current Opinion in Psychology Dec 2020Mental healthcare providers increasingly use technology for psychotherapy services. This progress enables professionals to communicate, store information, and rely on... (Review)
Review
Mental healthcare providers increasingly use technology for psychotherapy services. This progress enables professionals to communicate, store information, and rely on digital software and hardware. Emails, text messaging, telepsychology/telemental health therapy, electronic medical records, cloud-based storage, apps/applications, and assessments are now available within the provision of services. Of those mentioned, some are directly utilized for psychotherapy while others indirectly aid providers. Whereas professionals previously wrote notes locally, technology has empowered providers to work more efficiently with third-party services and solutions. However, the implementation of these advancements in mental healthcare involves consequences to digital privacy and might increase clients' risk of unintended breaches of confidentiality. This manuscript reviews common technologies, considers the vulnerabilities therein, and proposes suggestions to strengthen privacy.
Topics: Confidentiality; Electronic Health Records; Humans; Mental Health Services; Privacy; Technology
PubMed: 32361651
DOI: 10.1016/j.copsyc.2020.03.012 -
Annals of the New York Academy of... Jan 2017Accessing and integrating human genomic data with phenotypes are important for biomedical research. Making genomic data accessible for research purposes, however, must... (Review)
Review
Accessing and integrating human genomic data with phenotypes are important for biomedical research. Making genomic data accessible for research purposes, however, must be handled carefully to avoid leakage of sensitive individual information to unauthorized parties and improper use of data. In this article, we focus on data sharing within the scope of data accessibility for research. Current common practices to gain biomedical data access are strictly rule based, without a clear and quantitative measurement of the risk of privacy breaches. In addition, several types of studies require privacy-preserving linkage of genotype and phenotype information across different locations (e.g., genotypes stored in a sequencing facility and phenotypes stored in an electronic health record) to accelerate discoveries. The computer science community has developed a spectrum of techniques for data privacy and confidentiality protection, many of which have yet to be tested on real-world problems. In this article, we discuss clinical, technical, and ethical aspects of genome data privacy and confidentiality in the United States, as well as potential solutions for privacy-preserving genotype-phenotype linkage in biomedical research.
Topics: Computational Biology; Computer Security; Data Mining; Genetic Privacy; Genomics; Humans; Informed Consent; Medical Record Linkage; Risk Management; United States
PubMed: 27681358
DOI: 10.1111/nyas.13259 -
International Journal of Environmental... Aug 2023Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the... (Review)
Review
Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the requirement of data sharing. In healthcare systems, FL allows Medical Internet of Things (MIoT) devices and electronic health records (EHRs) to be trained locally without sending patients data to the central server. This allows healthcare decisions and diagnoses based on datasets from all participants, as well as streamlining other healthcare processes. In terms of user data privacy, this technology allows collaborative training without the need of sharing the local data with the central server. However, there are privacy challenges in FL arising from the fact that the model updates are shared between the client and the server which can be used for re-generating the client's data, breaching privacy requirements of applications in domains like healthcare. In this paper, we have conducted a review of the literature to analyse the existing privacy and security enhancement methods proposed for FL in healthcare systems. It has been identified that the research in the domain focuses on seven techniques: Differential Privacy, Homomorphic Encryption, Blockchain, Hierarchical Approaches, Peer to Peer Sharing, Intelligence on the Edge Device, and Mixed, Hybrid and Miscellaneous Approaches. The strengths, limitations, and trade-offs of each technique were discussed, and the possible future for these seven privacy enhancement techniques for healthcare FL systems was identified.
Topics: Humans; Privacy; Blockchain; Computer Communication Networks; Electronic Health Records; Delivery of Health Care
PubMed: 37569079
DOI: 10.3390/ijerph20156539 -
BMJ Health & Care Informatics Dec 2021Different stakeholders may hold varying attitudes towards artificial intelligence (AI) applications in healthcare, which may constrain their acceptance if AI developers... (Review)
Review
OBJECTIVES
Different stakeholders may hold varying attitudes towards artificial intelligence (AI) applications in healthcare, which may constrain their acceptance if AI developers fail to take them into account. We set out to ascertain evidence of the attitudes of clinicians, consumers, managers, researchers, regulators and industry towards AI applications in healthcare.
METHODS
We undertook an exploratory analysis of articles whose titles or abstracts contained the terms 'artificial intelligence' or 'AI' and 'medical' or 'healthcare' and 'attitudes', 'perceptions', 'opinions', 'views', 'expectations'. Using a snowballing strategy, we searched PubMed and Google Scholar for articles published 1 January 2010 through 31 May 2021. We selected articles relating to non-robotic clinician-facing AI applications used to support healthcare-related tasks or decision-making.
RESULTS
Across 27 studies, attitudes towards AI applications in healthcare, in general, were positive, more so for those with direct experience of AI, but provided certain safeguards were met. AI applications which automated data interpretation and synthesis were regarded more favourably by clinicians and consumers than those that directly influenced clinical decisions or potentially impacted clinician-patient relationships. Privacy breaches and personal liability for AI-related error worried clinicians, while loss of clinician oversight and inability to fully share in decision-making worried consumers. Both clinicians and consumers wanted AI-generated advice to be trustworthy, while industry groups emphasised AI benefits and wanted more data, funding and regulatory certainty.
DISCUSSION
Certain expectations of AI applications were common to many stakeholder groups from which a set of dependencies can be defined.
CONCLUSION
Stakeholders differ in some but not all of their attitudes towards AI. Those developing and implementing applications should consider policies and processes that bridge attitudinal disconnects between different stakeholders.
Topics: Artificial Intelligence; Attitude; Delivery of Health Care; Humans; Names
PubMed: 34887331
DOI: 10.1136/bmjhci-2021-100450 -
Infectious Diseases of Poverty Jun 2019The role of governance in strengthening tuberculosis (TB) control has received little research attention. This review provides evidence of how institutional designs and... (Review)
Review
BACKGROUND
The role of governance in strengthening tuberculosis (TB) control has received little research attention. This review provides evidence of how institutional designs and organisational practices influence implementation of the national TB control programme (NTP) in Nigeria.
MAIN TEXT
We conducted a scoping review using a five-stage framework to review published and grey literature in English, on implementation of Nigeria's NTP and identified themes related to governance using a health system governance framework. We included articles, of all study designs and methods, which described or analysed the processes of implementing TB control based on relevance to the research question. The review shows a dearth of studies which examined the role of governance in TB control in Nigeria. Although costed plans and policy coordination framework exist, public spending on TB control is low. While stakeholders' involvement in TB control is increasing, institutional capacity is limited, especially in the private sector. TB-specific legislation is absent. Deployment and transfer of staff to the NTP are not transparent. Health workers are not transparent in communicating service entitlements to users. Despite existence of supportive policies, integration of TB control into the community and general health services have been weak. Willingness to pay for TB services is high, however, transaction cost and stigma among patients limit equity. Effectiveness and efficiency of the NTP was hindered by inadequate human resources, dilapidated service delivery infrastructure and weak drug supply system. Despite adhering to standardized recording and reporting format, regular monitoring and evaluation, revision of reporting formats, and electronic data management system, TB surveillance system was found to be weak. Delay in TB diagnosis and initiation of care, poor staff attitude to patients, lack of privacy, poor management of drug reactions and absence of infection control measures breach ethical standards for TB care.
CONCLUSIONS
This scoping review of governance of TB control in Nigeria highlights two main issues. Governance for strengthening TB control programmes in low-resource, high TB burden settings like Nigeria, is imperative. Secondly, there is a need for empirical studies involving detailed analysis of different dimensions of governance of TB control.
Topics: Delivery of Health Care; Health Personnel; Health Policy; Health Workforce; Humans; Leadership; Nigeria; Tuberculosis
PubMed: 31203814
DOI: 10.1186/s40249-019-0556-2 -
Cureus Aug 2023The integration of artificial intelligence (AI) into healthcare promises groundbreaking advancements in patient care, revolutionizing clinical diagnosis, predictive... (Review)
Review
The integration of artificial intelligence (AI) into healthcare promises groundbreaking advancements in patient care, revolutionizing clinical diagnosis, predictive medicine, and decision-making. This transformative technology uses machine learning, natural language processing, and large language models (LLMs) to process and reason like human intelligence. OpenAI's ChatGPT, a sophisticated LLM, holds immense potential in medical practice, research, and education. However, as AI in healthcare gains momentum, it brings forth profound ethical challenges that demand careful consideration. This comprehensive review explores key ethical concerns in the domain, including privacy, transparency, trust, responsibility, bias, and data quality. Protecting patient privacy in data-driven healthcare is crucial, with potential implications for psychological well-being and data sharing. Strategies like homomorphic encryption (HE) and secure multiparty computation (SMPC) are vital to preserving confidentiality. Transparency and trustworthiness of AI systems are essential, particularly in high-risk decision-making scenarios. Explainable AI (XAI) emerges as a critical aspect, ensuring a clear understanding of AI-generated predictions. Cybersecurity becomes a pressing concern as AI's complexity creates vulnerabilities for potential breaches. Determining responsibility in AI-driven outcomes raises important questions, with debates on AI's moral agency and human accountability. Shifting from data ownership to data stewardship enables responsible data management in compliance with regulations. Addressing bias in healthcare data is crucial to avoid AI-driven inequities. Biases present in data collection and algorithm development can perpetuate healthcare disparities. A public-health approach is advocated to address inequalities and promote diversity in AI research and the workforce. Maintaining data quality is imperative in AI applications, with convolutional neural networks showing promise in multi-input/mixed data models, offering a comprehensive patient perspective. In this ever-evolving landscape, it is imperative to adopt a multidimensional approach involving policymakers, developers, healthcare practitioners, and patients to mitigate ethical concerns. By understanding and addressing these challenges, we can harness the full potential of AI in healthcare while ensuring ethical and equitable outcomes.
PubMed: 37692617
DOI: 10.7759/cureus.43262