{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:26:38Z","timestamp":1787030798031,"version":"build-2736575974"},"reference-count":72,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,16]],"date-time":"2026-08-16T00:00:00Z","timestamp":1786838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["RS-2026-25494867"],"award-info":[{"award-number":["RS-2026-25494867"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["RS-2025-02654040"],"award-info":[{"award-number":["RS-2025-02654040"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"award":["RS-2026-25494867"],"award-info":[{"award-number":["RS-2026-25494867"]}],"id":[{"id":"https:\/\/ror.org\/013aysd81","id-type":"ROR","asserted-by":"publisher"}]},{"award":["RS-2025-02654040"],"award-info":[{"award-number":["RS-2025-02654040"]}],"id":[{"id":"https:\/\/ror.org\/013aysd81","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Ministry of Education","award":["2026-ANCHOR-06-012"],"award-info":[{"award-number":["2026-ANCHOR-06-012"]}]},{"award":["2026-ANCHOR-06-012"],"award-info":[{"award-number":["2026-ANCHOR-06-012"]}],"id":[{"id":"https:\/\/ror.org\/01p262204","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Memristive devices have attracted considerable attention as promising candidates for overcoming the energy and data-transfer limitations of conventional computing architectures. In particular, their integration with biosensors offers a pathway toward compact and energy-efficient diagnostic systems. This review examines the development of memristive-device-based biosensors from device-level transduction to system-level integration. At the device level, sensing strategies have evolved from direct sensing toward indirect sensing architectures, improving stability and reusability. At the system level, conventional off-chip implementations have progressively shifted toward fully integrated on-chip implementations. Furthermore, this review highlights the emerging paradigm of in-sensor computing, in which sensing, memory, and computation are co-located within a single physical platform. This approach enables reduced data movement and supports energy-efficient operation for point-of-care applications. Finally, key challenges\u2014including CMOS compatibility, device variability, and reliable multi-threshold sensing operation\u2014are discussed as critical factors for the practical realization of memristive-device-based electrochemical biosensing systems.<\/jats:p>","DOI":"10.3390\/bios16080444","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T11:00:05Z","timestamp":1786964405000},"page":"444","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Device-Level Implementation to In-Sensor Computing in Memristive-Device-Based Biosensors: A Review"],"prefix":"10.3390","volume":"16","author":[{"given":"Hyunwook","family":"Ryu","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering, Chungnam National University, Daejeon 34134, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8253-0010","authenticated-orcid":false,"given":"Won-Chul","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Chungnam National University, Daejeon 34134, Republic of Korea"},{"name":"Division of Nano Convergence Technology Development, National NanoFab Center (NNFC), Daejeon 34141, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2114-8982","authenticated-orcid":false,"given":"Jongwon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Semiconductor Convergence, Chungnam National University, Daejeon 34134, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"078504","DOI":"10.1088\/1674-1056\/ab90e7","article-title":"In-Memory Computing to Break the Memory Wall","volume":"29","author":"Huang","year":"2020","journal-title":"Chin. 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