ArticleAdvanced sensor research2025
Integration of Nanoengineering with Artificial Intelligence and Machine Learning in Surface-Enhanced Raman Spectroscopy (SERS) for the Development of Advanced Biosensing Platforms.
Article in Advanced sensor research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Research progress of AI assisted nano-SERS technology in rapid identification of multi species oral pathogenic biofilms.Mikrochimica acta · 2026Review
- Transforming Paper into Plasmonic Sensors: One-Step Fabrication of High-Enhancement SERS Nanosubstrates via Surface Energy Control.Small methods · 2026Article
- Emerging Plasmonic Nanomaterials for SERS-Based Disease Diagnostics: Innovations, Clinical Challenges, and AI Integration.Molecules (Basel, Switzerland) · 2026Review
- AI-Assisted Surface-Enhanced Raman Spectroscopy for Cardiovascular Diagnostics: From Plasmonic Materials to Clinical Translation.Nanomaterials (Basel, Switzerland) · 2026Review
- AI/ML-Assisted SERS Biosensing for Biomolecular Detection: From Direct Spectral Response to Integrated Diagnostic Systems.Biosensors · 2026Review
- Advancing Extracellular Vesicle Research: A Review of Systems Biology and Multiomics Perspectives.Proteomics · 2026Review
- Revolutionizing Food Safety: A Systematic Review of Nanotechnology-Based Aflatoxin Detection (2010-2023).Nanotechnology, science and applications · 2026Review
- Novel Respiratory Disease Diagnosis Tool: Development of an Au-ReSSmall science · 2026Article
- Colorimetric methods using gold nanoparticles for forensic investigations.Forensic science, medicine, and pathology · 2025Review
- The Potential for Extracellular Vesicles in Nanomedicine: A Review of Recent Advancements and Challenges Ahead.Advanced biology · 2025Review
- A SERS-Based Antibody-Aptamer Sandwich Assay for Extracellular Vesicle-Associated Tau Detection Using Gold Nanoparticles.Advanced sensor research · 2025Article
- Precision oncology through SERS: emerging approaches for improved cancer diagnosis and prognosis.Nanomedicine (London, England) · 2025Article
- Gallium Resistance inApplied microbiology (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful tool for biomedical diagnosis, combining heightened sensitivity with molecular precision. The integration of artificial intelligence (AI) and machine learning (ML) has further elevated its capabilities, refining data interpretation, pattern prediction and bolstering diagnostic accuracy. This review chronicles advancements in SERS diagnostics, emphasizing the collaboration between ML and innovative nanostructures, substrates, and nanoprobes for SERS enhancement. We highlight breakthroughs in SERS-based point-of-care techniques and the nuanced detection of key biomarkers, from nucleic acids to proteins and metabolites. The article also addresses prevailing challenges, such as the need for standardized SERS methodologies and optimized platforms. Moreover, we touch upon the potential of portable SERS systems for clinical deployment and current efforts and challenges in clinical trials. In essence, this review positions the fusion of Nanoengineering, AI, ML, and SERS as the frontier for next-generation biomedical diagnostics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.