-
Frontiers in Sports and Active Living 2023Here, we performed a non-systematic analysis of the strength, weaknesses, opportunities, and threats (SWOT) associated with the application of artificial intelligence to...
Strengths, weaknesses, opportunities, and threats associated with the application of artificial intelligence in connection with sport research, coaching, and optimization of athletic performance: a brief SWOT analysis.
Here, we performed a non-systematic analysis of the strength, weaknesses, opportunities, and threats (SWOT) associated with the application of artificial intelligence to sports research, coaching and optimization of athletic performance. The strength of AI with regards to applied sports research, coaching and athletic performance involve the automation of time-consuming tasks, processing and analysis of large amounts of data, and recognition of complex patterns and relationships. However, it is also essential to be aware of the weaknesses associated with the integration of AI into this field. For instance, it is imperative that the data employed to train the AI system be both diverse and complete, in addition to as unbiased as possible with respect to factors such as the gender, level of performance, and experience of an athlete. Other challenges include e.g., limited adaptability to novel situations and the cost and other resources required. Opportunities include the possibility to monitor athletes both long-term and in real-time, the potential discovery of novel indicators of performance, and prediction of risk for future injury. Leveraging these opportunities can transform athletic development and the practice of sports science in general. Threats include over-dependence on technology, less involvement of human expertise, risks with respect to data privacy, breaching of the integrity and manipulation of data, and resistance to adopting such new technology. Understanding and addressing these SWOT factors is essential for maximizing the benefits of AI while mitigating its risks, thereby paving the way for its successful integration into sport science research, coaching, and optimization of athletic performance.
PubMed: 37920303
DOI: 10.3389/fspor.2023.1258562 -
Sensors (Basel, Switzerland) Oct 2023The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital...
The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital service quality and has proved its sustainable growth. However, the absence of security may breach the health insurance portability and accountability act (HIPAA), which has an exclusive set of rules for the privacy of medical data. Therefore, the goal of this work is to design and implement the adaptive Autonomous Protocol (AutoPro) on the patient's emote ealthare (RHC) monitoring data for the hospital using fully homomorphic encryption (FHE). The aim is to perform adaptive autonomous FHE computations on recent RHM data for providing health status reporting and maintaining the confidentiality of every patient. The autonomous protocol works independently within the group of prime hospital servers without the dependency on the third-party system. The adaptiveness of the protocol modes is based on the patient's affected level of slight, medium, and severe cases. Related applications are given as glucose monitoring for diabetes, digital blood pressure for stroke, pulse oximeter for COVID-19, electrocardiogram (ECG) for cardiac arrest, etc. The design for this work consists of an autonomous protocol, hospital servers combining multiple prime/local hospitals, and an algorithm based on fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant by the Gentry, Sahai, and Waters (GSW) scheme. The concrete-ML model used within this work is trained using an open heart disease dataset from the UCI machine learning repository. Preprocessing is performed to recover the lost and incomplete data in the dataset. The concrete-ML model is evaluated both on the workstation and cloud server. Also, the FHE protocol is implemented on the AWS cloud network with performance details. The advantages entail providing confidentiality to the patient's data/report while saving the travel and waiting time for the hospital services. The patient's data will be completely confidential and can receive emergency services immediately. The FHE results show that the highest accuracy is achieved by support vector classification (SVC) of 88% and linear regression (LR) of 86% with the area under curve (AUC) of 91% and 90%, respectively. Ultimately, the FHE-based protocol presents a novel system that is successfully demonstrated on the cloud network.
Topics: Humans; Blood Glucose Self-Monitoring; Computer Security; Blood Glucose; Confidentiality; Privacy; Delivery of Health Care
PubMed: 37896596
DOI: 10.3390/s23208504 -
Patterns (New York, N.Y.) Oct 2023The utilization of artificial intelligence (AI) applications has experienced tremendous growth in recent years, bringing forth numerous benefits and conveniences....
The utilization of artificial intelligence (AI) applications has experienced tremendous growth in recent years, bringing forth numerous benefits and conveniences. However, this expansion has also provoked ethical concerns, such as privacy breaches, algorithmic discrimination, security and reliability issues, transparency, and other unintended consequences. To determine whether a global consensus exists regarding the ethical principles that should govern AI applications and to contribute to the formation of future regulations, this paper conducts a meta-analysis of 200 governance policies and ethical guidelines for AI usage published by public bodies, academic institutions, private companies, and civil society organizations worldwide. We identified at least 17 resonating principles prevalent in the policies and guidelines of our dataset, released as an open source database and tool. We present the limitations of performing a global-scale analysis study paired with a critical analysis of our findings, presenting areas of consensus that should be incorporated into future regulatory efforts.
PubMed: 37876898
DOI: 10.1016/j.patter.2023.100857 -
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 -
Scientific Reports Oct 2023Cyberphysical systems connect physical devices and large private network environments in modern communication systems. A fundamental worry in the establishment of large...
Cyberphysical systems connect physical devices and large private network environments in modern communication systems. A fundamental worry in the establishment of large private networks is mitigating the danger of transactional data privacy breaches caused by adversaries using a variety of exploitation techniques. This study presents a privacy-preserving architecture for ensuring the privacy and security of transaction data in large private networks. The proposed model employs digital certificates, RSA-based public key infrastructure, and the blockchain to address user transactional data privacy concerns. The model also guarantees that data in transit remains secure and unaltered and that its provenance remains authentic and secure during node-to-node interactions within a large private network. The proposed model has increased the encryption speed by about 17 times, while the decryption process is expedited by 4 times. Therefore, the average overall acceleration obtained was 16.5. Both the findings of the security analysis and the performance analysis demonstrate that the proposed model can safeguard transactional data during communications on large private networks more effectively and securely than the existing solutions.
PubMed: 37816836
DOI: 10.1038/s41598-023-44101-x -
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 -
Sensors (Basel, Switzerland) Aug 2023In the era of interconnected and intelligent cyber-physical systems, preserving privacy has become a paramount concern. This paper aims a groundbreaking proof-of-concept...
In the era of interconnected and intelligent cyber-physical systems, preserving privacy has become a paramount concern. This paper aims a groundbreaking proof-of-concept (PoC) design that leverages consortium blockchain technology to address privacy challenges in cyber-physical systems (CPSs). The proposed design introduces a novel approach to safeguarding sensitive information and ensuring data integrity while maintaining a high level of trust among stakeholders. By harnessing the power of consortium blockchain, the design establishes a decentralized and tamper-resistant framework for privacy preservation. However, ensuring the security and privacy of sensitive information within CPSs poses significant challenges. This paper proposes a cutting-edge privacy approach that leverages consortium blockchain technology to secure secrets in CPSs. Consortium blockchain, with its permissioned nature, provides a trusted framework for governing the network and validating transactions. By employing consortium blockchain, secrets in CPSs can be securely stored, shared, and accessed by authorized entities only, mitigating the risks of unauthorized access and data breaches. The proposed approach offers enhanced security, privacy preservation, increased trust and accountability, as well as interoperability and scalability. This paper aims to address the limitations of traditional security mechanisms in CPSs and harness the potential of consortium blockchain to revolutionize the management of secrets, contributing to the advancement of CPS security and privacy. The effectiveness of the design is demonstrated through extensive simulations and performance evaluations. The results indicate that the proposed approach offers significant advancements in privacy protection, paving the way for secure and trustworthy cyber-physical systems in various domains.
PubMed: 37631699
DOI: 10.3390/s23167162 -
Journal of Medical Internet Research Aug 2023The health care sector experiences 76% of cybersecurity breaches due to basic web application attacks, miscellaneous errors, and system intrusions, resulting in...
The health care sector experiences 76% of cybersecurity breaches due to basic web application attacks, miscellaneous errors, and system intrusions, resulting in compromised health data or disrupted health services. The European Commission proposed the European Health Data Space (EHDS) in 2022 to enhance care delivery and improve patients' lives by offering all European Union (EU) citizens control over their personal health data in a private and secure environment. The EU has taken an important step in homogenizing the health data environment of the European health ecosystem, although more attention needs to be paid to keeping the health data of EU citizens safe and secure within the EHDS. The pooling of health data across countries can have tremendous benefits, but it may also become a target for cybercriminals or state-sponsored hackers. State-of-the-art security measures are essential, and the current EHDS proposal lacks sufficient measures to warrant a cybersecure and resilient environment.
Topics: Humans; Ecosystem; Computer Security; Europe; European Union; Health Care Sector
PubMed: 37616048
DOI: 10.2196/48824 -
International Journal of Neonatal... Aug 2023Dried blood spot (DBS) cards from newborn screening (NBS) programs represent a wealth of biological data. They can be stored easily for a long time, have the potential...
Dried blood spot (DBS) cards from newborn screening (NBS) programs represent a wealth of biological data. They can be stored easily for a long time, have the potential to support medical and public health research, and have secondary usages such as quality assurance and forensics, making it the ideal candidate for bio-banking. However, worldwide policies vary with regard to the duration of storage of DBS cards and how it can be used. Recent advances in genomics have also made it possible to perform extended genetic testing on DBS cards in the newborn period to diagnose both actionable and non-actionable childhood and adult diseases. Both storage and secondary uses of DBS cards raise many ethical, clinical, and social questions. The openness of the key stakeholders, namely, parents and healthcare providers (HCPs), to store the DBS cards, and for what duration and purposes, and to extended genetic testing is largely dependent on local cultural-social-specific factors. The study objective is to assess the parents' and HCPs' awareness and receptivity toward DBS retention, its secondary usage, and extended genetic testing. A cross-sectional, self-administrated survey was adopted at three hospitals, out of which two were public hospitals with maternity services, between June and December 2022. In total, 452 parents and 107 HCPs completed and returned the survey. Overall, both HCPs and parents were largely knowledgeable about the potential benefits of DBS card storage for a prolonged period and its secondary uses, and they supported extended genetic testing. Knowledge gaps were found in respondents with a lower education level who did not know that a DBS card could be stored for an extended period ( < 0.001), could support scientific research ( = 0.033), and could aid public health research, and future policy implementation ( = 0.030). Main concerns with regard to DBS card storage related to potential privacy breaches and anonymity (Parents 70%, HCPs 60%). More parents, compared to HCPs, believed that storing DBS cards for secondary research does not lead to a reciprocal benefit to the child ( < 0.005). Regarding extended genetic testing, both groups were receptive and wanted to know about actionable childhood- and adult-onset diseases. More parents (four-fifths) rather than HCPs (three-fifths) were interested in learning about a variant with unknown significance ( < 0.001). Our findings report positive support from both parents and HCPs toward the extended retention of DBS cards for secondary usage and for extended genetic testing. However, more efforts to raise awareness need to be undertaken in addition to addressing the ethical concerns of both parents and HCPs to pave the way forward toward policy-making for DBS bio-banking and extended genetic testing in Hong Kong.
PubMed: 37606482
DOI: 10.3390/ijns9030045