Review

Artificial Intelligence in Imaging Scholar, Scripps Clinic Divisions of Cardiology & Radiology, CA 92037, USA

Healthcare Innovation & Practice Transformation Laboratory, Scripps Clinic Division of Cardiology, La Jolla, CA 92037, USA

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Murtaza I Bharmal

https://orcid.org/0000-0001-9642-1237

Department of Medicine, Division of Cardiology, UC Irvine School of Medicine, Irvine, CA 92617, USA

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Aditi Nayak

Center for Advanced Heart Disease, Brigham & Women’s Hospital & Harvard Medical School, Boston, MA 02115, USA

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Siddharth Patel

https://orcid.org/0000-0009-9493-1815

Department of Neurology, Machine Learning Research Fellow, Laboratory for Deep Neurophenotyping, Massachusetts General Hospital, Boston, MA 02114, USA

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Mark Butlin

https://orcid.org/0000-0002-2746-4222

Faculty of Medicine, Health & Human Sciences, Macquarie University School of Medicine, Sydney, NSW, 2000, Australia.

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Sanjeev P Bhavnani

*Author for correspondence: Tel.: +1 619 660 1842;

E-mail Address: Bhavnani.sanjeev@scrippshealth.org

https://orcid.org/0000-0001-8161-9523

Healthcare Innovation & Practice Transformation Laboratory, Scripps Clinic Division of Cardiology, La Jolla, CA 92037, USA

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Published Online:5 Jul 2023

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Abstract

Nanosensors are nanoscale devices that measure physical attributes and convert these signals into analyzable information. In preparation, for the impending reality of nanosensors in clinical practice, we confront important questions regarding the evidence supporting widespread device use. Our objectives are to demonstrate the value and implications for new nanosensors as they relate to the next phase of remote patient monitoring and to apply lessons learned from digital health devices through real-world examples.

Plain language summary – Nanosensor technologies and the digital transformation of healthcare

The convergence of biomedical engineering and technological innovation in our dynamic digital era has produced innovative devices that allow easy, accurate characterization of physiological parameters. The miniaturization of diagnostic instruments able to perform continuous monitoring of vital signs, ECG and biomarkers has resulted in the emergence of digital health technologies including smartphone-connected devices, wearable and wireless technologies, lab-on-a-chip sensors and handheld imaging devices. It has also led to novel nanosensors – nanoscale devices that measure physical attributes and convert these signals into analyzable information. Such sensors span electrochemical, electromagnetic, piezoelectric and mechanoacoustic detection, resulting in increased enthusiasm for their application for cost-effective, real-time, continuous patient monitoring. Given the impending reality of nanosensors in clinical practice, here we confront important questions regarding the evidence supporting widespread device use, highlight current literature around nanosensors and aim to provide a framework for these advancements by considering the various device, patient and clinical factors relating to nanosensors – from the technical and biomedical engineering principles to validation approaches for new sensors and how continuous waveform data is transformed for analysis at the individual level. Our objectives are to demonstrate the value and implications for new nanosensors as they relate to the next phase of remote patient monitoring and to apply lessons learned from digital health devices through real-world evidence and examples.

Keywords:

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Vol. 20, No. 3

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History

Received 15 June 2022

Accepted 16 January 2023

Published online 5 July 2023

Published in print May 2023

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Keywords

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.futuremedicine.com/doi/suppl/10.2217/pme-2022-0065

Acknowledgments

The authors wish to thank X Sheng, J Wang and C Liao for their permission and generous contribution for the nanosensor figures included in this review; they also wish to express their appreciation to Alivecor, Abbott, Dexcom and iHealthLabs for providing illustrations of digital health devices.

Financial & competing interests disclosure

S Patel is an employee of MindMics. S Bhavnani is a scientific advisor to Analytics 4 Life and Blumio; a consultant to Bristol Myers Squibb, Pfizer and Infineon; a member of the data safety monitoring board chair at Proteus Digital; has received research support from Scripps Clinic and the Qualcomm Foundation; and is a member of the innovation advisory boards at the American College of Cardiology, American Society of Echocardiography and BIOCOM (all non-profit institutions, with all positions voluntary). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

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