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Journals / Frontiers in Life Sciences and Related Technologies / 2026 / Volume: 7

Raman-AI synergy in advanced spectroscopy for next-generation diagnostics: Current advances and future perspectives

Diwakar Singh(Jerome Cheese Company)0000-0003-3002-2270
Pages
—
Publication Date
2026-05-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This convergence marks a paradigm shift toward personalized, accessible, and scalable healthcare and environmental solutions, ushering in a new era of smart diagnostics aligned with the principles of Healthcare 4.0 and digital medicine.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Raman spectroscopy, particularly in its enhanced form, Surface-Enhanced Raman Spectroscopy (SERS), has emerged as a powerful, label-free analytical technique for applications spanning biomedical diagnostics, pharmaceutical analysis, and environmental monitoring.
📊
Key Findings & Data:However, its widespread adoption has been hindered by challenges, including signal variability, spectral noise, and the complexity of data interpretation.
💡
Conclusion & Impact:This convergence marks a paradigm shift toward personalized, accessible, and scalable healthcare and environmental solutions, ushering in a new era of smart diagnostics aligned with the principles of Healthcare 4.0 and digital medicine.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Artificial intelligence · biomedical diagnostics · biosensors

Abstract

Raman spectroscopy, particularly in its enhanced form, Surface-Enhanced Raman Spectroscopy (SERS), has emerged as a powerful, label-free analytical technique for applications spanning biomedical diagnostics, pharmaceutical analysis, and environmental monitoring. However, its widespread adoption has been hindered by challenges, including signal variability, spectral noise, and the complexity of data interpretation. The recent integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), is transforming these limitations into opportunities, enabling automated, high-throughput, and real-time diagnostics. This review explores the dynamic synergy between AI and SERS platforms, detailing how advanced algorithms are being used for spectral preprocessing, denoising, classification, and molecular pattern recognition. This review highlights major innovations such as AI-driven SERS biosensors, as well as portable and wearable diagnostic devices, and hyperspectral imaging platforms for ultrasensitive detection, including single-molecule resolution. Applications discussed include early cancer detection, pathogen identification, antimicrobial resistance profiling, environmental contaminant sensing, and pharmaceutical quality control. Furthermore, AI models like convolutional neural networks (CNNs), transformers, and generative algorithms are enabling enhanced interpretability, robustness to spectral variability, and greater automation across workflows. Critical challenges, including data standardization, reproducibility, and model transparency, are addressed in the current review, while emphasizing the need for interdisciplinary collaboration and ethical deployment. By bridging nanotechnology, spectroscopy, and intelligent computation, AI-enhanced Raman and SERS technologies are redefining the landscape of diagnostics and sensing. This convergence marks a paradigm shift toward personalized, accessible, and scalable healthcare and environmental solutions, ushering in a new era of smart diagnostics aligned with the principles of Healthcare 4.0 and digital medicine.

Özet

Raman spectroscopy, particularly in its enhanced form, Surface-Enhanced Raman Spectroscopy (SERS), has emerged as a powerful, label-free analytical technique for applications spanning biomedical diagnostics, pharmaceutical analysis, and environmental monitoring. However, its widespread adoption has been hindered by challenges, including signal variability, spectral noise, and the complexity of data interpretation. The recent integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), is transforming these limitations into opportunities, enabling automated, high-throughput, and real-time diagnostics. This review explores the dynamic synergy between AI and SERS platforms, detailing how advanced algorithms are being used for spectral preprocessing, denoising, classification, and molecular pattern recognition. This review highlights major innovations such as AI-driven SERS biosensors, as well as portable and wearable diagnostic devices, and hyperspectral imaging platforms for ultrasensitive detection, including single-molecule resolution. Applications discussed include early cancer detection, pathogen identification, antimicrobial resistance profiling, environmental contaminant sensing, and pharmaceutical quality control. Furthermore, AI models like convolutional neural networks (CNNs), transformers, and generative algorithms are enabling enhanced interpretability, robustness to spectral variability, and greater automation across workflows. Critical challenges, including data standardization, reproducibility, and model transparency, are addressed in the current review, while emphasizing the need for interdisciplinary collaboration and ethical deployment. By bridging nanotechnology, spectroscopy, and intelligent computation, AI-enhanced Raman and SERS technologies are redefining the landscape of diagnostics and sensing. This convergence marks a paradigm shift toward personalized, accessible, and scalable healthcare and environmental solutions, ushering in a new era of smart diagnostics aligned with the principles of Healthcare 4.0 and digital medicine.

🔖

Cite This Article

Singh, D. (2026). Raman-AI synergy in advanced spectroscopy for next-generation diagnostics: Current advances and future perspectives. Frontiers in Life Sciences and Related Technologies, Volume: 7. https://doi.org/10.51753/flsrt.1824228

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