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Journal of Medical Internet Research Aug 2023ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical...
ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical challenges from legal, humanistic, algorithmic, and informational perspectives. Legal ethics concerns arise from the unclear allocation of responsibility when patient harm occurs and from potential breaches of patient privacy due to data collection. Clear rules and legal boundaries are needed to properly allocate liability and protect users. Humanistic ethics concerns arise from the potential disruption of the physician-patient relationship, humanistic care, and issues of integrity. Overreliance on artificial intelligence (AI) can undermine compassion and erode trust. Transparency and disclosure of AI-generated content are critical to maintaining integrity. Algorithmic ethics raise concerns about algorithmic bias, responsibility, transparency and explainability, as well as validation and evaluation. Information ethics include data bias, validity, and effectiveness. Biased training data can lead to biased output, and overreliance on ChatGPT can reduce patient adherence and encourage self-diagnosis. Ensuring the accuracy, reliability, and validity of ChatGPT-generated content requires rigorous validation and ongoing updates based on clinical practice. To navigate the evolving ethical landscape of AI, AI in health care must adhere to the strictest ethical standards. Through comprehensive ethical guidelines, health care professionals can ensure the responsible use of ChatGPT, promote accurate and reliable information exchange, protect patient privacy, and empower patients to make informed decisions about their health care.
Topics: Humans; Artificial Intelligence; Reproducibility of Results; Data Collection; Disclosure; Patient Compliance
PubMed: 37566454
DOI: 10.2196/48009 -
Nurse Education Today Nov 2023The healthcare industry has increasingly been targeted by cybercrime putting patients, organizations, and employees at risk for financial loss and breach of privacy....
BACKGROUND
The healthcare industry has increasingly been targeted by cybercrime putting patients, organizations, and employees at risk for financial loss and breach of privacy. Malware events compromise system integrity and patient privacy which could lead to delays in treatment, loss of patient data, inability to provide care, and increase in patient harm. In addition, these attacks may also compromise private and personal information for those targeted.
OBJECTIVE
Nurses represent a large portion of frontline healthcare workers and are uniquely positioned to help prevent cyber-attacks. Nursing curriculum should include education about the risks to patient safety from cybercrime and the nurse's role in preventing cybercrime. Nursing education has focused on hygiene for patient safety. Adding cyber hygiene to the essential practices of pre-licensure and advanced practice nurses is a first step to protecting patients, organizations, and employees from the repercussions of a healthcare cyber-attack.
Topics: Humans; Education, Nursing; Hygiene; Curriculum; Educational Status; Health Personnel
PubMed: 37595324
DOI: 10.1016/j.nedt.2023.105940 -
Current Opinion in Pediatrics Aug 2023To better understand confidentiality issues that arise from adolescent access to patient portals. (Review)
Review
PURPOSE OF REVIEW
To better understand confidentiality issues that arise from adolescent access to patient portals.
RECENT FINDINGS
Studies have evaluated the views of teens, parents, providers, and institutional leadership on adolescent patient portals and the risks they pose to adolescent privacy. Additional investigations have shown that teen portal accounts are often inappropriately accessed by parents. Guidelines are needed to better inform the creation of secure teen patient portals. Adolescent providers and other medical staff should be aware of the information available on portals, how to ensure portals are being accessed appropriately, and the potential for confidentiality breaches that come with portal use. Medical organizations that offer portal access need to provide resources to adolescents and their families to improve understanding around the importance of confidential care and how to maintain confidentiality while still engaging meaningfully with the healthcare system through patient portals.
SUMMARY
Adolescents realize the benefits portals may offer regarding improved understanding of their health conditions, communication with their providers, and autonomy in their healthcare decisions. However, confidentiality of patient portals is a major concern and a potential barrier to adolescent portal utilization. Adolescent providers should be aware of the limitations of portal systems and advocate for improved confidentiality functionality to ensure teens can access the benefits of patient portals without any harm.
Topics: Adolescent Health Services; Humans; Adolescent; Patient Portals; Confidentiality; Electronic Health Records; Parents; Legal Guardians; Information Dissemination
PubMed: 37036289
DOI: 10.1097/MOP.0000000000001252 -
Entropy (Basel, Switzerland) Nov 2023With the development of mobile applications, location-based services (LBSs) have been incorporated into people's daily lives and created huge commercial revenues....
With the development of mobile applications, location-based services (LBSs) have been incorporated into people's daily lives and created huge commercial revenues. However, when using these services, people also face the risk of personal privacy breaches due to the release of location and query content. Many existing location privacy protection schemes with centralized architectures assume that anonymous servers are secure and trustworthy. This assumption is difficult to guarantee in real applications. To solve the problem of relying on the security and trustworthiness of anonymous servers, we propose a Geohash-based location privacy protection scheme for snapshot queries. It is named GLPS. On the user side, GLPS uses Geohash encoding technology to convert the user's location coordinates into a string code representing a rectangular geographic area. GLPS uses the code as the privacy location to send check-ins and queries to the anonymous server and to avoid the anonymous server gaining the user's exact location. On the anonymous server side, the scheme takes advantage of Geohash codes' geospatial gridding capabilities and GL-Tree's effective location retrieval performance to generate a -anonymous query set based on user-defined minimum and maximum hidden cells, making it harder for adversaries to pinpoint the user's location. We experimentally tested the performance of GLPS and compared it with three schemes: Casper, GCasper, and DLS. The experimental results and analyses demonstrate that GLPS has a good performance and privacy protection capability, which resolves the reliance on the security and trustworthiness of anonymous servers. It also resists attacks involving background knowledge, regional centers, homogenization, distribution density, and identity association.
PubMed: 38136449
DOI: 10.3390/e25121569 -
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 -
Neurologic Clinics Aug 2023Advances in electronic health record technology, the ever-expanding use of social media, and cybersecurity sabotage threaten patient privacy and render physicians and... (Review)
Review
Advances in electronic health record technology, the ever-expanding use of social media, and cybersecurity sabotage threaten patient privacy and render physicians and health care organizations liable for violating federal and state laws. Violating a patient's privacy is both an ethical and legal breach with potentially serious legal and reputational consequences. Even an unintentional Health Insurance Portability and Accountability Act of 1996 (HIPAA) violation can result in financial penalties and reputational harm. Staying complaint with HIPAA requires vigilance on the part of both individuals with legitimate access to protected health information (PHI) and the organizations handling that PHI.
Topics: United States; Humans; Health Insurance Portability and Accountability Act; Privacy; Social Media; Confidentiality
PubMed: 37407103
DOI: 10.1016/j.ncl.2023.03.007 -
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 -
Blockchain in Healthcare Today 2023Integrating personal health records (PHRs) and electronic health records (EHRs) facilitates the provision of novel services to individuals, researchers, and healthcare...
UNLABELLED
Integrating personal health records (PHRs) and electronic health records (EHRs) facilitates the provision of novel services to individuals, researchers, and healthcare practitioners. Simultaneously, integrating healthcare data leads to complexities arising from the structural and semantic heterogeneity within the data. The subject of healthcare data evokes strong emotions due to concerns surrounding privacy breaches. Blockchain technology is employed to address the issue of patient data privacy in inter-organizational processes, as it facilitates patient data ownership and promotes transparency in its usage. At the same time, blockchain technology creates new challenges for e-healthcare systems, such as data privacy, observability, and online enforceability. This article proposes designing and formalizing automatic conflict resolution techniques in decentralized e-healthcare systems. The present study expounds upon our concepts by employing a running case study centered around preventive and personalized healthcare domains.
PLAIN LANGUAGE SUMMARY
This paper suggests using blockchain technology for privacy concerns in integrating personal health records and electronic health records in decentralized e-healthcare systems. This report focuses on designing automatic conflict resolution techniques to ensure patient data ownership, transparency, and privacy in inter-organizational processes. This paper proposes designing automatic conflict resolution techniques in decentralized e-healthcare systems, which can improve inter-organizational processes in healthcare. Using blockchain technology to integrate personal and electronic health records can ensure patient data ownership and promote transparency in data usage, addressing privacy concerns in healthcare systems. This paper emphasizes the importance of data privacy and protection in healthcare systems, highlighting the need for compliance with laws and regulations. The research results, including the proof-of-concept prototype, can provide practical insights into implementing conflict resolution techniques in decentralized e-healthcare systems.
PubMed: 38187955
DOI: 10.30953/bhty.v6.276 -
Sensors (Basel, Switzerland) Sep 2023In smart cities, unmanned aerial vehicles (UAVS) play a vital role in surveillance, monitoring, and data collection. However, the widespread integration of UAVs brings...
In smart cities, unmanned aerial vehicles (UAVS) play a vital role in surveillance, monitoring, and data collection. However, the widespread integration of UAVs brings forth a pressing concern: security and privacy vulnerabilities. This study introduces the SP-IoUAV (Secure and Privacy Preserving Intrusion Detection and Prevention for UAVS) model, tailored specifically for the Internet of UAVs ecosystem. The challenge lies in safeguarding UAV operations and ensuring data confidentiality. Our model employs cutting-edge techniques, including federated learning, differential privacy, and secure multi-party computation. These fortify data confidentiality and enhance intrusion detection accuracy. Central to our approach is the integration of deep neural networks (DNNs) like the convolutional neural network-long short-term memory (CNN-LSTM) network, enabling real-time anomaly detection and precise threat identification. This empowers UAVs to make immediate decisions in dynamic environments. To proactively counteract security breaches, we have implemented a real-time decision mechanism triggering alerts and initiating automatic blacklisting. Furthermore, multi-factor authentication (MFA) strengthens access security for the intrusion detection system (IDS) database. The SP-IoUAV model not only establishes a comprehensive machine framework for safeguarding UAV operations but also advocates for secure and privacy-preserving machine learning in UAVS. Our model's effectiveness is validated using the CIC-IDS2017 dataset, and the comparative analysis showcases its superiority over previous approaches like FCL-SBL, RF-RSCV, and RBFNNs, boasting exceptional levels of accuracy (99.98%), precision (99.93%), recall (99.92%), and -Score (99.92%).
PubMed: 37836907
DOI: 10.3390/s23198077