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Wiktor Rybicki

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Review Open access Sep 2026

SMART HOME SENSORS FOR AMBIENT ASSISTED LIVING IN THE GERIATRIC POPULATION: CLINICAL EFFICACY, ETHICAL CONSIDERATIONS, AND SOCIOECONOMIC DISPARITIES

Background: The global demographic shift toward an aging population exacerbates the clinical burden of multi-morbidity, falls, and functional decline, alongside a persistent shortage of geriatric healthcare professionals. Ambient Assisted Living (AAL) technologies utilizing edge-AI passive sensors offer a scalable, unobtrusive approach to continuous remote monitoring. Objectives: This narrative review aims to synthesize recent clinical data regarding the efficacy of AAL systems in geriatric care while critically evaluating the ethical tensions and socioeconomic barriers impeding equitable adoption. Methods: A comprehensive synthesis of peer-reviewed literature published between 2020 and 2026 was conducted, focusing on the biomechanical tracking of falls, longitudinal monitoring of Activities of Daily Living (ADLs), dynamic consent, privacy concerns, and digital health equity. Results: Clinical evidence indicates AAL networks effectively identify early digital biomarkers of cognitive and physiological decline, mitigating injurious falls and reducing 30-day hospital readmissions. However, continuous domestic surveillance introduces significant ethical dilemmas concerning patient autonomy and the medicalization of the home environment. Furthermore, formidable financial constraints, algorithmic bias, and inadequate broadband infrastructure restrict AAL adoption to affluent demographics, exacerbating systemic health disparities. Conclusions: While AAL technologies present substantial clinical utility for proactive geriatric care, their unchecked commercial deployment risks institutionalizing diagnostic inequalities. Equitable implementation requires formal public health subsidization, equity-by-design algorithmic training, and rigorous bioethical frameworks to balance clinical beneficence with patient privacy.

Antoni Kantor, Kacper Raputa, Jakub Gościński et al. · 0 citations
Review Open access Aug 2026

AI IN PSYCHIATRIC DIAGNOSTICS – A REVIEW

Introduction: Artificial intelligence (AI) is increasingly being used in psychiatry, with side effects on solutions stemming from the subjectivity of diagnosis, limited care, and biological complexity, which is subject to threats. Mental disorders affect 293 million people worldwide and pose a burden on human health [9]. Aim: The aim of this review is to summarize the current state of knowledge on AI applications in psychiatric diagnostics, with specific focus on: (1) analysis of communication traffic of AI algorithms, (2) analysis of the results of AI-based primary control, (3) extension of methodological and ethical implications, and (4) extension of research. Methods: A review of the research literature was conducted in the field of Basic Language Processing (NLP) in digital phenotyping, AI-assisted neuroimaging, and the ethical and legal implications of implementing these technologies. Meta-analyses, specific reviews, and original empirical studies completed between 2015 and 2026 were analyzed. Results: A meta-analysis reported a cumulative AI diagnostic accuracy of 85% and a therapeutic efficacy of 84% in specific applications [8]. NLP enabled independent assessment, achieving an 86% (AUC 0.93) in studies on psychosis risk states [18]. Chatbots (Woebot, Wysa, Youper) demonstrate the consequences of problem occurrence and anxiety [9]. A review of 555 neuroimaging models revealed that 83.1% of the symptoms appear as a consequence rather than being triggered by a utility [32]. The most important ethical concerns were identified, including algorithm opacity ("black box"), liability, and data privacy [54, 56, 61]. Conclusions: AI in psychiatric diagnostics has demonstrated transformative potential, particularly in the areas of NLP and digital phenotyping, but current neuroimaging models require methodological improvements. The development of comprehensive ethical frameworks and extensions, simple algorithms, and model validation in large, population-based cohorts are essential. The ultimate success of AI in psychiatry will depend on striking a balance between technological innovation and respect for fundamental ethical values, while maintaining a paramount clinical role in diagnostic and therapeutic procedures.

Wiktor Rybicki, Radosław Dutczak, Aleksandra Sobieska et al. · 0 citations

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