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Mobile Diagnostic Clinics.
- Source :
-
ACS sensors [ACS Sens] 2024 Jun 28; Vol. 9 (6), pp. 2777-2792. Date of Electronic Publication: 2024 May 22. - Publication Year :
- 2024
-
Abstract
- This article reviews the revolutionary impact of emerging technologies and artificial intelligence (AI) in reshaping modern healthcare systems, with a particular focus on the implementation of mobile diagnostic clinics. It presents an insightful analysis of the current healthcare challenges, including the shortage of healthcare workers, financial constraints, and the limitations of traditional clinics in continual patient monitoring. The concept of "Mobile Diagnostic Clinics" is introduced as a transformative approach where healthcare delivery is made accessible through the incorporation of advanced technologies. This approach is a response to the impending shortfall of medical professionals and the financial and operational burdens conventional clinics face. The proposed mobile diagnostic clinics utilize digital health tools and AI to provide a wide range of services, from everyday screenings to diagnosis and continual monitoring, facilitating remote and personalized care. The article delves into the potential of nanotechnology in diagnostics, AI's role in enhancing predictive analytics, diagnostic accuracy, and the customization of care. Furthermore, the article discusses the importance of continual, noninvasive monitoring technologies for early disease detection and the role of clinical decision support systems (CDSSs) in personalizing treatment guidance. It also addresses the challenges and ethical concerns of implementing these advanced technologies, including data privacy, integration with existing healthcare infrastructure, and the need for transparent and bias-free AI systems.
- Subjects :
- Humans
Telemedicine
Decision Support Systems, Clinical
Artificial Intelligence
Subjects
Details
- Language :
- English
- ISSN :
- 2379-3694
- Volume :
- 9
- Issue :
- 6
- Database :
- MEDLINE
- Journal :
- ACS sensors
- Publication Type :
- Academic Journal
- Accession number :
- 38775426
- Full Text :
- https://doi.org/10.1021/acssensors.4c00636