On-chip sensors have emerged as a transformative device in medical diagnostics, facilitating rapid, sensitive, and cost-effective detection of multiple biomarkers in complex biological fluids. Their advancement has been significantly influenced by interdisciplinary developments in materials science, microfabrication, and the integration of machine learning. This chapter provides an in-depth exploration of various fabrication methodologies for on-chip sensors, including screen printing, laser-induced graphene production, and nanocomposite functionalization. It further evaluates these methods in terms of scalability, in contrast with high-precision yet costly techniques such as lithography and 3D printing. A particular focus is directed toward the evolution of electrochemical, optical, and mechanical transduction systems, which have progressed from conventional silicon-based devices to innovative platforms enhanced by nanomaterials and driven by machine learning, featuring multiplexing capabilities. The chapter highlights significant real-world applications, including the monitoring of conditions such as myocardial infarction, sepsis, diabetes, and cancer, alongside the development of emerging non-invasive wearable devices designed for the analysis of sweat and tears. The discourse also critically examines pathways to commercialization, addressing key challenges such as sensitivity to low-abundance biomarkers, data standardization, regulatory compliance, and the enhancement of user accessibility in resource-limited environments. Case studies, including the Dexcom G7 continuous glucose monitor and Ellume’s COVID-19 test, effectively illustrate both the challenges and triumphs encountered during the process of market translation. Moreover, the chapter discusses the role of machine learning methodologies, particularly fuzzy logic frameworks, in improving data interpretation, mitigating sensor drift, and enhancing clinical decision-making processes. The socioeconomic implications of on-chip sensors are also contemplated, underscoring their potential to alleviate healthcare burdens, strengthen disease surveillance systems, and foster equitable access to diagnostic tools. The on-chip sensors exhibited high accuracy with real patient samples. In conclusion, the chapter emphasizes the future promise of CRISPR-based sensing technologies, advanced nanomaterials, AI integration, and sustainable manufacturing approaches in addressing current limitations. Consequently, these advancements position on-chip sensors as fundamental components of precision medicine and global healthcare innovation.

On-Chip Sensors Commercialized for Medical Diagnostics Applications

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发表日期：

2026年

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Vikas Kumar Shukla,
Tanushri

摘要

On-chip sensors have emerged as a transformative device in medical diagnostics, facilitating rapid, sensitive, and cost-effective detection of multiple biomarkers in complex biological fluids. Their advancement has been significantly influenced by interdisciplinary developments in materials science, microfabrication, and the integration of machine learning. This chapter provides an in-depth exploration of various fabrication methodologies for on-chip sensors, including screen printing, laser-induced graphene production, and nanocomposite functionalization. It further evaluates these methods in terms of scalability, in contrast with high-precision yet costly techniques such as lithography and 3D printing. A particular focus is directed toward the evolution of electrochemical, optical, and mechanical transduction systems, which have progressed from conventional silicon-based devices to innovative platforms enhanced by nanomaterials and driven by machine learning, featuring multiplexing capabilities. The chapter highlights significant real-world applications, including the monitoring of conditions such as myocardial infarction, sepsis, diabetes, and cancer, alongside the development of emerging non-invasive wearable devices designed for the analysis of sweat and tears. The discourse also critically examines pathways to commercialization, addressing key challenges such as sensitivity to low-abundance biomarkers, data standardization, regulatory compliance, and the enhancement of user accessibility in resource-limited environments. Case studies, including the Dexcom G7 continuous glucose monitor and Ellume’s COVID-19 test, effectively illustrate both the challenges and triumphs encountered during the process of market translation. Moreover, the chapter discusses the role of machine learning methodologies, particularly fuzzy logic frameworks, in improving data interpretation, mitigating sensor drift, and enhancing clinical decision-making processes. The socioeconomic implications of on-chip sensors are also contemplated, underscoring their potential to alleviate healthcare burdens, strengthen disease surveillance systems, and foster equitable access to diagnostic tools. The on-chip sensors exhibited high accuracy with real patient samples. In conclusion, the chapter emphasizes the future promise of CRISPR-based sensing technologies, advanced nanomaterials, AI integration, and sustainable manufacturing approaches in addressing current limitations. Consequently, these advancements position on-chip sensors as fundamental components of precision medicine and global healthcare innovation.