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Review—Challenges in Lab-to-Clinic Translation of 5th/6th Generation Intelligent Nanomaterial-enabled Biosensors

  • Rishi Kumar Talreja
  • , Harsh Sable
  • , Vikash Chaudhary
  • , Sachin Kadian
  • , Mamta Singh
  • , Manish Kumar
  • , Jugal Kishore
  • , Vishal Chaudhary
  • , Ajit Khosla
  • Vardhman Mahavir Medical College and Safdarjung Hospital
  • Sharda University
  • Bhagini Nivedita College
  • University of North Carolina and North Carolina State University
  • Shivaji College
  • University of Delhi
  • Chandigarh University
  • Chitkara University, Punjab
  • School of Advanced Materials and Nanotechnology, Xidian University

Research output: Contribution to journalReview articlepeer-review

46 Citations (Scopus)

Abstract

Conventional diagnostic platforms often lack point-of-care, simple, economical, prompt and personalized detection features, whereas nanomaterial-supported intelligent biosensors belonging to the 5th/6th generation are vital vectors in medical diagnostics. The tunable and enhanced physicochemical properties of nanomaterials such as surface area, surface chemistry, band gap, and flexibility, nano-biosensors exhibit high sensitivity, specificity, and prompt and accurate detection. Despite substantial research and an exponentially growing market, projected to reach $46.4 billion by 2028, biosensors face considerable challenges in clinical implementation. This article underlines the manifold translational challenges, such as regulatory barriers, safety and toxicity concerns related to nanomaterials, technical and manufacturing issues, hesitancy in adopting new tools, and economic constraints. Besides discussing the perspectives of material scientists, medical doctors, data scientists, and public health professionals, this article presents a comprehensive overview of the current state and prospects of integrating next-generation nanomaterial-based artificial intelligence-supported biosensors into clinical practice. It emphasizes the need to address these barriers, which can enhance early disease detection, improve patient outcomes, and reduce the overall burden on healthcare systems. Their applications can be extended to one health management team with dedicated collaborations to achieve sustainable development goals.

Original languageEnglish
Article number041602
JournalECS Sensors Plus
Volume3
Issue number4
DOIs
Publication statusPublished - 2 Dec 2024
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  2. SDG 17 - Partnerships for the Goals
    SDG 17 Partnerships for the Goals

Keywords

  • artificial intelligence
  • biosensors
  • diagnosis
  • nanomaterials
  • sustainability

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