AI-driven electrochemical biosensing platforms for skin cancer: Analytical advances toward intelligent theranostic and drug-delivery systems
TrAC - Trends in Analytical Chemistry, cilt.203, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Derleme
- Cilt numarası: 203
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.trac.2026.119029
- Dergi Adı: TrAC - Trends in Analytical Chemistry
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core, Chimica, Compendex, EMBASE, DIALNET, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Anahtar Kelimeler: Artificial intelligence, Biomarkers, Biosensor reproducibility, Clinical translation, Electrochemical biosensors, Electrochemical noise, Interfacial electrochemistry, Signal transduction, Skin cancer, Theranostics
- Erciyes Üniversitesi Adresli: Evet
Özet
Electrochemical biosensors have emerged as powerful analytical platforms for the early detection and monitoring of skin cancer biomarkers owing to their high sensitivity, rapid response, miniaturization, and compatibility with point-of-care diagnostics. However, their clinical translation remains limited by biofouling, signal drift, interfacial instability, matrix interference, and poor long-term reproducibility. Beyond biomolecular recognition, biosensor performance is governed by interfacial electrochemistry, electron-transfer kinetics, mass transport, and signal transduction, which collectively determines signal generation, amplification, and analytical reliability. This review critically examines these physicochemical processes from a mechanistic and systems-level perspective. Emphasis is placed on clinically relevant biomarkers for melanoma and selected non-melanoma skin cancers, including proteins, nucleic acids, extracellular vesicles, and metabolic markers, together with challenges arising from biomarker heterogeneity, low abundance, and complex biological matrices. The integration of artificial intelligence (AI) and machine learning (ML) for electrochemical data analysis is evaluated, highlighting advances in feature extraction, pattern recognition, predictive modeling, and decision support, as well as limitations related to data quality, overfitting, interpretability, and clinical generalizability. Emerging AI-assisted electrochemical theranostic systems are discussed as closed-loop platforms integrating biomarker sensing, data-driven analysis, and therapeutic actuation for personalized disease management. Finally, key barriers to clinical translation, including standardization, validation, regulatory requirements, wearable and minimally invasive sensing technologies, and skin cancer-specific therapeutic delivery, are assessed. By integrating electrochemical sensing principles, AI-driven analytics, and translational considerations, this review provides a systems-oriented framework for developing reliable, intelligent, and clinically viable electrochemical biosensing platforms for next-generation skin cancer diagnosis and theranostics.