Objective: AI-based applications are increasingly developed to support users to digitally record, manage and change their emotions, beliefs and behavior patterns. Such forms of self-tracking in the mental sphere are accompanied by a variety of medical benefits in diagnostics, prevention, and therapy. This article pursues the question of which philosophical-ethical implications must be taken into account when dealing with these advantages.
Methods: First, some AI-based applications for self-tracking of mental characteristics and processes are outlined. Subsequently, relevant philosophical-ethical implications are presented.
Results: The following aspects prove to be normatively relevant: improvement versus reduction of self-determination; improvement of self-knowledge versus alienation; positive versus negative aspects of self-responsible health care; epistemic challenges of AI applications; difficulties of conceptual and normative definitions in the applications.
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http://dx.doi.org/10.1055/a-1364-5068 | DOI Listing |
Digit Health
January 2025
Institute for Medical Informatics and Biometry, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Objective: The application of artificial intelligence (AI)-based clinical decision support systems (CDSS) in the healthcare domain is still limited. End-users' difficulty understanding how the outputs of opaque black AI models are generated contributes to this. It is still unknown which explanations are best presented to end users and how to design the interfaces they are presented in (explanation user interface, XUI).
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January 2025
Anesthesiology and Reanimation, Central Clinical Hospital, Baku, Azerbaijan.
Background: Patients who are informed about the causes, pathophysiology, treatment and prevention of a disease are better able to participate in treatment procedures in the event of illness. Artificial intelligence (AI), which has gained popularity in recent years, is defined as the study of algorithms that provide machines with the ability to reason and perform cognitive functions, including object and word recognition, problem solving and decision making. This study aimed to examine the readability, reliability and quality of responses to frequently asked keywords about low back pain (LBP) given by three different AI-based chatbots (ChatGPT, Perplexity and Gemini), which are popular applications in online information presentation today.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institute of Theoretical & Applied Informatics, Polish Academy of Sciences (IITiS-PAN), 44-100 Gliwice, Poland.
Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Advanced Institute of Convergence Technology, 145 Gwanggyo-ro, Yeongtong-gu, Suwon-si 16229, Gyeonggi-do, Republic of Korea.
According to South Korea's Ministry of Employment and Labor, approximately 25,000 construction workers suffered from various injuries between 2015 and 2019. Additionally, about 500 fatalities occur annually, and multiple studies are being conducted to prevent these accidents and quickly identify their occurrence to secure the golden time for the injured. Recently, AI-based video analysis systems for detecting safety accidents have been introduced.
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
Center for Musculoskeletal Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany.
The rapid development of artificial intelligence (AI) is impacting the medical sector by offering new possibilities for faster and more accurate diagnoses. Symptom checker apps show potential for supporting patient decision-making in this regard. Whether the AI-based decision-making of symptom checker apps shows better performance in diagnostic accuracy and urgency assessment compared to physicians remains unclear.
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