Innovative applications of artificial intelligence (AI) are creating new avenues for information technology (IT) solutions in multiple sectors such as industry and health. Medical informatics researchers globally invest considerable effort in managing diseases of essential organs, which presents a complicated medical condition (including those related to lungs, heart, brain, kidneys, pancreas, and liver). Research into medical conditions such as Pulmonary Hypertension (PH), impacting both the lungs and the heart, becomes increasingly complex due to the simultaneous involvement of multiple organ systems. Therefore, prompt detection and diagnosis of PH are critical for overseeing the disease's progression and preventing associated fatalities.
Knowledge of current AI methods in PH is the object of this investigation. Through a quantitative analysis of scientific output on PH, coupled with an examination of the research networks, a systematic review will be achieved. Assessing research performance using a bibliometric approach involves utilizing diverse statistical, data mining, and data visualization methods, encompassing scientific publications and their accompanying indicators, for example, direct measures of scientific production and impact.
Data for citations is predominantly gleaned from the Web of Science Core Collection and Google Scholar. The publications at the top exhibit a broad range of journals, such as IEEE Access, Computers in Biology and Medicine, Biology Signal Processing and Control, Frontiers in Cardiovascular Medicine, and Sensors, as suggested by the results. Universities prominent in the field include those from the United States (Boston University, Harvard Medical School, Stanford University) and the United Kingdom (Imperial College London), showcasing the most relevant affiliations. The keywords most frequently cited are Classification, Diagnosis, Disease, Prediction, and Risk.
A critical aspect of reviewing the PH scientific literature is this bibliometric study. Researchers and practitioners can use this guideline or tool to analyze and interpret the key scientific challenges and problems in AI modeling applications relevant to public health. One aspect is that it enhances the visibility of the advancements made and the boundaries noted. Accordingly, this leads to their widespread and extensive circulation. 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.
In the context of reviewing the scientific literature on PH, this bibliometric study is of paramount importance. Serving as a helpful guideline or instrument, this resource enables researchers and practitioners to grasp the critical scientific challenges and issues in applying AI modeling to public health. One consequence is the improved perception of progress realized and the restrictions discovered. Following this, their wide and broad dissemination is achieved. domestic family clusters infections Additionally, it furnishes substantial aid in grasping the progression of scientific AI activities focused on managing the diagnosis, treatment, and prognosis of PH. Finally, each activity of data collection, processing, and leveraging data is underpinned by a discussion of ethical considerations, to uphold the legitimate rights of patients.
Misinformation, a byproduct of the COVID-19 pandemic, proliferated across various media platforms, thereby increasing the severity of hate speech. A worrying upswing in online hate speech has unfortunately translated to a 32% increase in hate crimes within the United States in the year 2020. The Department of Justice's 2022 findings. My paper explores the immediate effects of hate speech and contends that it merits widespread acknowledgement as a public health issue. My current discussion extends to artificial intelligence (AI) and machine learning (ML) tactics for reducing hate speech, and the moral quandaries these techniques raise. The potential for future enhancements to AI and machine learning models is also explored. My analysis of public health and AI/ML methodologies reveals a crucial point: standalone application of these approaches is neither efficient nor sustainable. In conclusion, I recommend a third strategy that integrates artificial intelligence/machine learning techniques alongside public health. By integrating the reactive capabilities of AI/ML with the preventive strategies of public health, a novel approach to combating hate speech is forged.
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. Consequently, the smartphone app's (a tracking device) participatory Value-Sensitive Design is explored and explicated throughout its various phases (conceptual, empirical, and technical). From the construction and elicitation of values, through iterative engagement of expert and non-expert stakeholders, to the delivery of an embodied prototype tailored to those values. How moral dilemmas and value conflicts, often stemming from diverse needs and vested interests, are resolved in practice, forms the core of creating a unique digital artifact. This artifact demonstrates moral imagination, fulfilling vital ethical-social needs without jeopardizing technical proficiency. Dementia management and care are enhanced by an AI tool that is demonstrably more ethical and democratic, owing to its accurate representation of varied citizens' values and app expectations. Our concluding remarks highlight the suitability of the co-design methodology presented herein for fostering more comprehensible and reliable artificial intelligence, thereby driving forward human-focused technical-digital advancement.
The ubiquity of algorithmic worker surveillance and productivity scoring tools, fueled by artificial intelligence (AI), is becoming a defining characteristic of the contemporary workplace. GBM Immunotherapy In the realms of white-collar and blue-collar professions, along with gig economy positions, these tools are put to use. 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 operation of these instruments is a direct affront to human dignity and the fundamental rights of all people. The construction of these tools is, unfortunately, based on fundamentally erroneous postulates. 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. https://www.selleckchem.com/products/gdc-0068.html The roadmap's section presents actionable recommendations for adjustments to policies and regulations, which are suitable for federal agencies and labor unions to implement. The paper anchors its policy recommendations in significant policy frameworks promoted or established by the United States. Amongst the guiding documents for ethical AI are the Universal Declaration of Human Rights, the Organisation for Economic Co-operation and Development (OECD) Principles for the Responsible Stewardship of Trustworthy AI, Fair Information Practices, and the White House Blueprint for an AI Bill of Rights.
A distributed, patient-focused approach is rapidly emerging in healthcare, replacing the conventional, specialist-driven model of hospitals with the Internet of Things (IoT). The evolution of medical procedures has created a more demanding and comprehensive healthcare framework for patients. Through the use of sensors and devices integrated into an IoT-enabled intelligent health monitoring system, continuous patient analysis is facilitated, occurring around the clock. IoT's influence is reshaping system architecture, thereby advancing the practical application of sophisticated systems. Remarkable as they are, healthcare devices serve as a prime example of IoT implementation. A wide array of patient monitoring techniques is accessible through the IoT platform. By reviewing papers from 2016 to 2023, this review introduces an IoT-enabled intelligent health monitoring system. This survey examines big data within IoT networks and also includes a discussion of edge computing, an IoT computing approach. The merits and demerits of sensors and smart devices are examined in this review of intelligent IoT-based health monitoring systems. This survey gives a succinct account of the smart devices and sensors utilized within IoT-based smart healthcare systems.
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. A core tenet of the DT is to offer a thorough, practical, and tangible explanation for any element, asset, or system. Yet, the taxonomy evolves with remarkable dynamism, its complexity escalating throughout the lifespan, leading to an overwhelming volume of generated data and insights. Correspondingly, the development of blockchain facilitates the potential of digital twins to re-imagine themselves and serve as a pivotal strategy for the application of IoT-based digital twins to transfer data and value across the internet. This assurance includes complete transparency, the reliability of traceability, and the immutability of transactions. Therefore, the integration of digital twins, IoT, and blockchain technologies has the capability to transform many industries by offering a higher level of security, increased transparency, and absolute data trustworthiness. A survey of the diverse applications of digital twins, incorporating Blockchain technology, is the subject of this work. Additionally, this subject matter entails difficulties and subsequent avenues for future research. Along with this paper, we propose a concept and architecture for integrating digital twins with IoT-based blockchain archives, which allows for real-time monitoring and control of physical assets and processes in a secure and decentralized way.