Smartphone-based clinical diagnostics: towards democratization of evidence-based health care.
I Hernández-NeutaF NeumannJ BrightmeyerT Ba TisN MadaboosiQingshan WeiAydogan OzcanMats NilssonPublished in: Journal of internal medicine (2018)
Recent advancements in bioanalytical techniques have led to the development of novel and robust diagnostic approaches that hold promise for providing optimal patient treatment, guiding prevention programs and widening the scope of personalized medicine. However, these advanced diagnostic techniques are still complex, expensive and limited to centralized healthcare facilities or research laboratories. This significantly hinders the use of evidence-based diagnostics for resource-limited settings and the primary care, thus creating a gap between healthcare providers and patients, leaving these populations without access to precision and quality medicine. Smartphone-based imaging and sensing platforms are emerging as promising alternatives for bridging this gap and decentralizing diagnostic tests offering practical features such as portability, cost-effectiveness and connectivity. Moreover, towards simplifying and automating bioanalytical techniques, biosensors and lab-on-a-chip technologies have become essential to interface and integrate these assays, bringing together the high precision and sensitivity of diagnostic techniques with the connectivity and computational power of smartphones. Here, we provide an overview of the emerging field of clinical smartphone diagnostics and its contributing technologies, as well as their wide range of areas of application, which span from haematology to digital pathology and rapid infectious disease diagnostics.
Keyphrases
- healthcare
- primary care
- end stage renal disease
- infectious diseases
- resting state
- high throughput
- white matter
- public health
- chronic kidney disease
- ejection fraction
- functional connectivity
- multiple sclerosis
- machine learning
- case report
- peritoneal dialysis
- patient reported outcomes
- circulating tumor cells
- big data
- artificial intelligence
- smoking cessation
- label free