{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T12:17:15Z","timestamp":1787833035935,"version":"build-2784847793"},"reference-count":206,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>The integration of artificial intelligence (AI) and machine learning (ML) with electrochemical sensing has revolutionized analytical diagnostics by overcoming traditional limitations such as signal drift, peak overlapping, and matrix interference. However, despite the exponential growth of this field, a unified framework evaluating translational feasibility remains absent. This review critically analyzes AI\/ML architectures applied to electrochemical sensors and biosensors from 2016 to 2025. To the best of our knowledge, this work introduces the first coded Network Evidence Map to quantitatively map the co-occurrence of methodological strengths, weaknesses, and translational barriers across the examined literature. The analysis reveals that while deep learning and ensemble models excel in signal deconvolution and multiplexing, the field is severely constrained by systemic bottlenecks. Network pathways demonstrate that over 83% of studies lack uncertainty quantification, and data scarcity coupled with restricted data-sharing policies critically undermines model reproducibility. Furthermore, batch-to-batch hardware variability measurably co-occurs with the opacity of black-box algorithms, hindering regulatory approval. We conclude that advancing from laboratory proof-of-concept to real-world point-of-care deployment necessitates a paradigm shift toward open-source electrochemical repositories, explainable AI (XAI), physics-informed machine learning, and hardware-software co-design.<\/jats:p>","DOI":"10.3390\/bios16090470","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T10:58:21Z","timestamp":1787828301000},"page":"470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in Electrochemical Sensing: A Network Evidence Map of Translational Barriers and Pathways to Point-of-Care Deployment"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4399-4880","authenticated-orcid":false,"given":"Muhammad","family":"Saqib","sequence":"first","affiliation":[{"name":"Chemical Engineering Division, School of Earth Sciences and Engineering, National Research Tomsk Polytechnic University, 30 Lenin Avenue, 634050 Tomsk, Russia"},{"name":"UNESCO Laboratory of Environmental Electrochemistry, Department of Analytical Chemistry, Faculty of Science, Charles University, Hlavova 8\/2030, CZ 128 43 Prague, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5938-9562","authenticated-orcid":false,"given":"Elena I.","family":"Korotkova","sequence":"additional","affiliation":[{"name":"Chemical Engineering Division, School of Earth Sciences and Engineering, National Research Tomsk Polytechnic University, 30 Lenin Avenue, 634050 Tomsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunquan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4696-2072","authenticated-orcid":false,"given":"Neda","family":"Firoz","sequence":"additional","affiliation":[{"name":"National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2205-075X","authenticated-orcid":false,"given":"Mrinal","family":"Vashisth","sequence":"additional","affiliation":[{"name":"National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7824-7299","authenticated-orcid":false,"given":"Amrit L.","family":"Hui","sequence":"additional","affiliation":[{"name":"National Research Tomsk State University, 36 Lenin Avenue, 634050 Tomsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga I.","family":"Lipskikh","sequence":"additional","affiliation":[{"name":"Chemical Engineering Division, School of Earth Sciences and Engineering, National Research Tomsk Polytechnic University, 30 Lenin Avenue, 634050 Tomsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2656-6873","authenticated-orcid":false,"given":"Pradip Kumar","family":"Kar","sequence":"additional","affiliation":[{"name":"Parasitology Laboratory, Department of Zoology, Cooch Behar Panchanan Barma University, Vivekananda Street, Cooch Behar 736101, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sangiorgi, L., Sberveglieri, V., Carnevale, C., De Nardi, S., Nunez-Carmona, E., and Raccagni, S. 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