Smartphone-Based Biosensors: Current Trends, Challenges, and Future Prospects

Smartphone-based biosensors are emerging as transformative tools for personalized medicine and real-time health monitoring. This study critically examines recent advancements in mass-sensitive, optical, and electrochemical biosensing technologies, highlighting their application in detecting disease biomarkers. Beyond a summary of technological progress, this paper presents a strategic roadmap that addresses persistent barriers to clinical deployment, including sensor calibration inconsistencies, lack of interoperability, and limited scalability. The proposed framework leverages explainable artificial intelligence (AI) for enhanced diagnostic interpretation and outlines pathways for low-cost, scalable manufacturing using advanced nanomaterials. By bridging technical innovation with practical implementation, this work contributes a replicable model for developing equitable, reliable smartphone biosensors capable of expanding access to preventive and individualized healthcare worldwide.

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Publication Details

Published
2025-09-18
DOI
https://doi.org/10.3390/engproc2025106010
Citations
2
Primary Topic
Biosensors and Analytical Detection
Type
article
Field-Weighted Citation Impact
1.35
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article

Smartphone-Based Biosensors: Current Trends, Challenges, and Future Prospects

Idama Omokaro, Adinife Patrick Azodo, Tochukwu Canice Mezue
2 citations
Biosensors and Analytical Detection
1.35
article

Smartphone-Based Biosensors: Current Trends, Challenges, and Future Prospects

Idama Omokaro, Adinife Patrick Azodo, Tochukwu Canice Mezue
article en
2 citations

Abstract

Smartphone-based biosensors are emerging as transformative tools for personalized medicine and real-time health monitoring. This study critically examines recent advancements in mass-sensitive, optical, and electrochemical biosensing technologies, highlighting their application in detecting disease biomarkers. Beyond a summary of technological progress, this paper presents a strategic roadmap that addresses persistent barriers to clinical deployment, including sensor calibration inconsistencies, lack of interoperability, and limited scalability. The proposed framework leverages explainable artificial intelligence (AI) for enhanced diagnostic interpretation and outlines pathways for low-cost, scalable manufacturing using advanced nanomaterials. By bridging technical innovation with practical implementation, this work contributes a replicable model for developing equitable, reliable smartphone biosensors capable of expanding access to preventive and individualized healthcare worldwide.

Wilmington University (US), Delta State Polytechnic Ogwashi-Uku (NG), Kwararafa University (NG)
Openalex Percentile: Top 17%
Biosensors and Analytical Detection
1.35
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