Mobile Diagnostic Clinics

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.

Keywords: clinics; continual monitoring; diagnosis; healthcare; real-time; remote management; sensors; wearable.

Figure 1

Mobile diagnostic clinics—A concept. Created with BioRender.com.

Figure 2

Summary of data collection devices and emerging technologies. (A) A platform to integrate smart device electrocardiogram into clinical practice. Reprinted (in part) with permission from Lambert et al. Reference. Copyright 2021 Elsevier. (B) (a) Textile-based activity and ECG monitoring platform with a (b) mobile user interface. Reprinted (adapted) with permission from Tao et al. Ref (49). Copyright 2018 John Wiley and Sons. (C) (Top) MinION portable DNA sequencer. Reprinted with permission from Mongan et al. Ref (65). Copyright 2020 Springer Nature. (Bottom) Full-length read of dsDNA through the nanopore sequencer. (a) Steps in the translocation of the DNA through the nanopore. Each section of DNA is depicted by a different color (b) Raw current traces corresponding to the steps (i–viii) in (a). Each section generates a unique current trace corresponding to decipher base sequence. Reprinted (in part) with permission from Jain et al. Ref (68). Copyright 2016 BioMed Central. (D) Continuous Glucose Monitoring (CGM) Devices. (a) Finger pricking device. (b) Schematic of CGM devices. (c) Detection principles of Glucose sensors. Reprinted with permission from Kumar Das et al. Ref (72). Copyright 2022 The Electrochemical Society. (E) Bimetallic nanocatalysts in nanoporous hydrogels for CGM via contact lens. (a) In vivo CGM of smart contact lens in diabetic rabbits (scale bar: 150 μm) (b) Correlation equation between blood and tear glucose levels using a CGM with a glucometer (green), a commercial CGM (blue) and the smart contact lens (pink) for 30 min. Reprinted (in part) with permission from Kim et al. Ref (73). Copyright 2022 John Wiley and Sons. (F) Illustration of lateral flow immunoassay for detection of thyroid-stimulating hormone (TSH). Reprinted with permission from Choi et al. Ref (88). Copyright 2017 Elsevier. (G) (a) Bioadhesive ultrasound for continuous imaging. (b) Comparison of image resolutions and monitoring durations. Reprinted (in part) with permission from Wang et al. Ref (91). Copyright 2022 The American Association for the Advancement of Science. (H) (a) Pictor Plus hand-held fundus camera. Reprinted from https://www.volk.com/pages/portable-fundus-cameras. Copyright 2024 Volk Optical. (b) Diagnosis of Diabetic Retinopathy by hand-held fundus camera. Reprinted (in part) with permission from Lu et al. Ref (99). Copyright 2022 PLoS.

Figure 3

Empowering healthcare providers with RPM. (A) Time to detection of cystic fibrosis exacerbation using an application for symptom reporting. Reprinted with permission from Wood et al. Ref (122). Copyright 2019 Elsevier. (B) Workflow diagram of COVID-19 rpm. Reprinted with permission from Tabacof et al. Ref (140). Copyright 2021 Mary Ann Liebert, Inc. (C) Wearable Remote Patient Monitoring Device for the Early Detection of Patient Deterioration. Red lines indicate high-risk warnings while the terminal black line indicates the time of actual clinical deterioration. Reprinted with permission from Itelman et al. Ref (141). Copyright 2022 JMIR Publications Inc.

Figure 4

Implementing AI for real-time data assessment. (A) Skin cancer classification performance of a deep neural network and dermatologists. An AUC of 0.94 was achieved. Reprinted with permission from Esteva et al. Ref (15). Copyright 2017 Springer Nature. (B) Insulin dose optimization using an automated artificial intelligence-based decision support system (AI-DSS) in youths with type 1 diabetes. Filled circles represent the AI-DSS arm and the open diamonds represent the physician arm. Reprinted with permission from Nimri et al. Ref (17). Copyright 2020 Springer Nature.