The burgeoning field of artificial intelligence (AI) unlocks new possibilities for information technology (IT) across various applications, from industry to healthcare. In the field of medical informatics, a considerable amount of scientific work focuses on managing diseases affecting critical organs, thus resulting in a complex disease (including those of the lungs, heart, brain, kidneys, pancreas, and liver). Simultaneous involvement of multiple organs, like in Pulmonary Hypertension (PH) impacting both lungs and heart, complicates scientific research. In light of this, early detection and diagnosis of PH are essential for monitoring the disease's advancement and preventing associated mortality rates.
This discussion centers on current AI applications relevant to PH. Through a quantitative analysis of scientific output on PH, coupled with an examination of the research networks, a systematic review will be achieved. By using various statistical, data mining, and data visualization methods, a bibliometric approach assesses research performance through scientific publications and diverse indicators, including direct measures of scientific output and influence.
Data for citations is predominantly gleaned from the Web of Science Core Collection and Google Scholar. The results indicate the presence of various journals, including IEEE Access, Computers in Biology and Medicine, Biology Signal Processing and Control, Frontiers in Cardiovascular Medicine, and Sensors, within the top publications. The most notable affiliations are represented by universities in the United States (Boston University, Harvard Medical School, and Stanford University), and the United Kingdom (Imperial College London). Classification, Diagnosis, Disease, Prediction, and Risk stand out as the most cited keywords in academic publications.
The scientific literature concerning PH is reviewed effectively through this indispensable bibliometric study. This guideline or tool serves as a framework for researchers and practitioners to comprehend the core scientific challenges and issues in AI modeling applied to public health. It is possible to, on the one hand, improve the visibility of any advancement or restrictions found. Thus, their wide distribution is advanced and amplified. Additionally, it affords valuable assistance in grasping the development of scientific AI approaches utilized in the management of PH diagnosis, treatment, and prognosis. Lastly, ethical considerations are presented in each facet of data acquisition, manipulation, and utilization to safeguard patient rights.
This bibliometric study is indispensable to a thorough review of the scientific literature regarding PH. For researchers and practitioners, this resource, presented as a guideline or tool, is designed to provide an understanding of the core scientific challenges and difficulties involved in applying AI models in public health. It allows for a greater demonstration of the advancement achieved or the limits observed. As a result, it promotes their extensive and wide distribution. selleck chemicals Consequently, it gives useful support for deciphering the progression of scientific AI endeavors applied to managing the diagnosis, treatment, and prognosis of PH. In closing, each data collection, handling, and use activity explicitly addresses ethical considerations to maintain patient rights.
The COVID-19 pandemic's aftermath witnessed a proliferation of misinformation across various media platforms, ultimately intensifying the problem of hate speech. The amplification of hateful online discourse has had a devastating impact, leading to a 32% rise in hate crimes within the United States in 2020. As documented in the 2022 Department of Justice report. This paper scrutinizes the present-day impact of hate speech, and advocates for its acceptance as a public health crisis. Current artificial intelligence (AI) and machine learning (ML) strategies to counter hate speech are also evaluated, alongside the ethical considerations inherent in using these technologies. Further advancements in AI/ML are contemplated, along with considerations for future implementation. My assessment of the disparate public health and AI/ML methodologies leads to the conclusion that individual application of these approaches is insufficiently efficient and unsustainable. In light of this, I propose a third option which blends artificial intelligence/machine learning with public health. The proposed method for combating hate speech leverages both the reactive nature of AI/ML and the preventative measures of public health.
Illustrating the ethical implications of applied AI, the Sammen Om Demens project, a citizen science initiative, designs and implements a smartphone app for people with dementia, highlighting interdisciplinary collaborations and the active participation of citizens, end-users, and anticipated beneficiaries of digital innovation. Accordingly, a thorough exploration and explanation of the smartphone app's (a tracking device) participatory Value-Sensitive Design are presented across its three phases: conceptual, empirical, and technical. Embodied prototypes, built upon and customized to the values of expert and non-expert stakeholders, result from value construction and elicitation processes, after multiple iterations. Focusing on how moral dilemmas and value conflicts, which frequently stem from diverse people's needs or vested interests, are resolved, a unique digital artifact is produced. This artifact utilizes moral imagination to fulfill vital ethical-social desiderata without impeding technical efficiency. An AI-based tool for dementia care and management, more ethical and democratic, successfully reflects the multifaceted values and expectations of diverse citizens through the app's functionality. Ultimately, the co-design approach explored in this research is deemed appropriate for producing more interpretable and trustworthy AI, concurrently promoting human-centered technical-digital innovation.
Productivity scoring tools and algorithmic worker surveillance, both powered by artificial intelligence (AI), are rapidly proliferating and becoming deeply integrated into the workplace landscape. Bio-mathematical models White-collar, blue-collar, and gig economy roles all benefit from the application of these tools. Due to a lack of legal safeguards and robust collaborative efforts, employees find themselves at a disadvantage when confronting employers who utilize these instruments. The application of these tools is detrimental to the inherent worth and freedoms of humanity. These tools' development is, unfortunately, built on fundamentally mistaken premises. The preliminary section of this paper offers stakeholders (policymakers, advocates, workers, and unions) an understanding of the underlying assumptions in workplace surveillance and scoring technologies, alongside an analysis of employer use and its effect on human rights. immediate postoperative Actionable policy and regulatory changes, presented in the roadmap section, are suitable for implementation by federal agencies and labor unions. This paper's policy recommendations stem from major policy frameworks that have been either developed by or aligned with the principles of the United States. The Organisation for Economic Co-operation and Development (OECD) AI Principles, the Universal Declaration of Human Rights, the White House AI Bill of Rights, and Fair Information Practices are key documents for ethical AI.
A distributed, patient-focused approach is emerging in the healthcare industry, driven by the Internet of Things (IoT) and replacing the older, hospital-and-specialist-centric model. The evolution of medical procedures has created a more demanding and comprehensive healthcare framework for patients. Employing sensors and devices in an IoT-enabled intelligent health monitoring system, a 24-hour patient analysis is conducted. IoT's impact on system architecture is demonstrably positive, leading to more effective applications of intricate systems. IoT applications find their most spectacular manifestation in healthcare devices. In the IoT platform, a variety of patient monitoring techniques are readily available. The reviewed literature from 2016 to 2023 informs this review's description of an IoT-enabled intelligent health monitoring system. The survey further explores big data within IoT networks, along with the edge computing facet of IoT computing technology. Intelligent IoT-based health monitoring systems were evaluated in this review, specifically concerning the utilized sensors and smart devices and their respective advantages and disadvantages. IoT smart healthcare systems leverage sensors and smart devices, as detailed in this concise study presented in the survey.
Digital Twin technology has garnered significant attention from researchers and businesses in recent years, driven by its advancements in information technology, communication networks, cloud computing, IoT, and blockchain. In essence, the DT aims to offer a comprehensive, concrete, and operational clarification of any element, asset, or system. Nonetheless, a highly dynamic taxonomy, developing in complexity over the lifespan, produces a massive quantity of engendered data and related information. In tandem with the progression of blockchain technology, digital twins possess the capability to redefine their role and become a key strategic component for supporting the use of IoT-based digital twins in the transfer of data and value onto the internet, promising complete transparency, dependable traceability, and unalterable transaction records. Consequently, the integration of digital twins with IoT and blockchain technologies holds the promise of transforming diverse industries, bolstering security, enhancing transparency, and assuring data integrity. A survey of the diverse applications of digital twins, incorporating Blockchain technology, is the subject of this work. This field also includes a discussion of potential obstacles and research opportunities for the future. In this paper, we describe a concept and architecture for integrating digital twins with IoT-based blockchain archives, allowing real-time monitoring and control of physical assets and processes in a secure and decentralized methodology.