{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T01:15:20Z","timestamp":1783386920552,"version":"3.54.6"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032305411","type":"print"},{"value":"9783032305428","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-30542-8_18","type":"book-chapter","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:43:11Z","timestamp":1783384991000},"page":"275-286","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Artificial Intelligence for Biosensors: A Systematic Literature Review with Bibliometrics Approach"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8179-2457","authenticated-orcid":false,"given":"Sarthak","family":"Sengupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7026-6531","authenticated-orcid":false,"given":"Anindya","family":"Bose","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2277-8346","authenticated-orcid":false,"given":"Shamsuzzaman","family":"Ansari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9471-153X","authenticated-orcid":false,"given":"David","family":"Fonseca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9987-5584","authenticated-orcid":false,"given":"Francisco Jos\u00e9","family":"Garc\u00eda-Pe\u00f1alvo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0816-1445","authenticated-orcid":false,"given":"Fernando","family":"Moreira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"key":"18_CR1","doi-asserted-by":"publisher","unstructured":"Sengupta, S., Bose, A., Moreira, F., Escudero, D.F., Garc\u00eda-Pe\u00f1alvo, F.J., Collazos, C.: A study on sensors in higher education. In: Zaphiris, P., Ioannou, A. (eds.) Learning and Collaboration Technologies. HCII 2024. LNCS, vol. 14723. Springer, Cham. (2024). https:\/\/doi.org\/10.1007\/978-3-031-61685-3_16","DOI":"10.1007\/978-3-031-61685-3_16"},{"issue":"5","key":"18_CR2","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1108\/SR-03-2024-0282","volume":"44","author":"A Bose","year":"2024","unstructured":"Bose, A., Sengupta, S., Biswas, S.: Development of conductive carbon-based interdigitated electrode array integrated with microfluidic channel for blood glucose sensing. Sens. Rev. 44(5), 587\u2013597 (2024). https:\/\/doi.org\/10.1108\/SR-03-2024-0282","journal-title":"Sens. Rev."},{"issue":"2","key":"18_CR3","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1108\/SR-10-2020-0232","volume":"41","author":"A Bose","year":"2021","unstructured":"Bose, A., Sengupta, S.: Fabrication and characterization of pillar interdigitated electrode for blood glucose sensing. Sens. Rev. 41(2), 200\u2013207 (2021). https:\/\/doi.org\/10.1108\/SR-10-2020-0232","journal-title":"Sens. Rev."},{"key":"18_CR4","doi-asserted-by":"publisher","unstructured":"Sengupta, S.: Social media for digital health management. J. Bus. Strat. Finan. Manage. 6(2). (2024). https:\/\/doi.org\/10.12944\/JBSFM.06.02.02","DOI":"10.12944\/JBSFM.06.02.02"},{"key":"18_CR5","doi-asserted-by":"publisher","unstructured":"Sengupta, S., Vaish, A., Escudero, D.F., Garc\u00eda-Pe\u00f1alvo, F.J., Bose, A., Moreira, F.: A review on modular framework and artificial intelligence-based smart education. In: Zaphiris, P., Ioannou, A. (eds.) Learning and Collaboration Technologies. HCII 2023. LNCS, vol. 14040. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-34411-4_10","DOI":"10.1007\/978-3-031-34411-4_10"},{"key":"18_CR6","doi-asserted-by":"publisher","unstructured":"Sengupta, S., Vaish, A., Bose, A., Fonseca, D., Garcia-Penalvo, F.J., Moreira, F.: Development of an artificial intelligence-based smart education framework. In: Smith, B.K., Borge, M. (eds.) Learning and Collaboration Technologies. HCII 2025. LNCS, vol. 15807. Springer, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-031-93567-1_26","DOI":"10.1007\/978-3-031-93567-1_26"},{"key":"18_CR7","doi-asserted-by":"publisher","unstructured":"Vashistha, R., Dangi, A.K., Kumar, A., et al.: Futuristic biosensors for cardiac health care: an artificial intelligence approach. 3 Biotech 8, 358 (2018). https:\/\/doi.org\/10.1007\/s13205-018-1368-y","DOI":"10.1007\/s13205-018-1368-y"},{"issue":"11","key":"18_CR8","doi-asserted-by":"publisher","first-page":"3346","DOI":"10.1021\/acssensors.0c01424","volume":"5","author":"F Cui","year":"2020","unstructured":"Cui, F., Yue, Y., Zhang, Y., Zhang, Z., Zhou, H.S.: Advancing biosensors with machine learning. ACS Sens. 5(11), 3346\u20133364 (2020). https:\/\/doi.org\/10.1021\/acssensors.0c01424","journal-title":"ACS Sens."},{"key":"18_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.bios.2020.112412","volume":"165","author":"X Jin","year":"2020","unstructured":"Jin, X., Liu, C., Xu, T., Su, L., Zhang, X.: Artificial intelligence biosensors: challenges and prospects. Biosens. Bioelectron. 165, 112412 (2020). https:\/\/doi.org\/10.1016\/j.bios.2020.112412","journal-title":"Biosens. Bioelectron."},{"issue":"3","key":"18_CR10","doi-asserted-by":"publisher","first-page":"4054","DOI":"10.1021\/acsnano.0c06946","volume":"15","author":"H Kim","year":"2021","unstructured":"Kim, H., et al.: Noninvasive precision screening of prostate cancer by urinary multimarker sensor and artificial intelligence analysis. ACS Nano 15(3), 4054\u20134065 (2021). https:\/\/doi.org\/10.1021\/acsnano.0c06946","journal-title":"ACS Nano"},{"key":"18_CR11","doi-asserted-by":"publisher","unstructured":"Thiruvengadam, M., et al.: Sustainable and smart nano-biosensors: Integrated solutions for healthcare, environmental monitoring, agriculture, and food safety. Indust. Crops Product. 233 (2025). https:\/\/doi.org\/10.1016\/j.indcrop.2025.121337","DOI":"10.1016\/j.indcrop.2025.121337"},{"key":"18_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.bios.2022.114825","volume":"219","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Hu, Y., Jiang, N., Yetisen, A.K.: Wearable artificial intelligence biosensor networks. Biosens. Bioelectron. 219, 114825 (2023). https:\/\/doi.org\/10.1016\/j.bios.2022.114825","journal-title":"Biosens. Bioelectron."},{"key":"18_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.teac.2022.e00160","volume":"34","author":"N Pouyanfar","year":"2022","unstructured":"Pouyanfar, N., et al.: Artificial intelligence-based microfluidic platforms for the sensitive detection of environmental pollutants: recent advances and prospects. Trends Environ. Anal. Chem. 34, e00160 (2022). https:\/\/doi.org\/10.1016\/j.teac.2022.e00160","journal-title":"Trends Environ. Anal. Chem."},{"key":"18_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2022.111054","volume":"194","author":"V Hemamalini","year":"2022","unstructured":"Hemamalini, V., et al.: Integrating bio medical sensors in detecting hidden signatures of COVID-19 with artificial intelligence. Measurement 194, 111054 (2022). https:\/\/doi.org\/10.1016\/j.measurement.2022.111054","journal-title":"Measurement"},{"issue":"5","key":"18_CR15","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1080\/14737159.2023.2203817","volume":"23","author":"B Ozdalgic","year":"2023","unstructured":"Ozdalgic, B., Yetisen, A.K., Tasoglu, S.: Smartphone and wearable diagnostics. Expert Rev. Mol. Diagn. 23(5), 357\u2013359 (2023). https:\/\/doi.org\/10.1080\/14737159.2023.2203817","journal-title":"Expert Rev. Mol. Diagn."},{"key":"18_CR16","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1186\/s11671-023-03842-4","volume":"18","author":"M Ramalingam","year":"2023","unstructured":"Ramalingam, M., Jaisankar, A., Cheng, L., et al.: Impact of nanotechnology on conventional and artificial intelligence-based biosensing strategies for the detection of viruses. Discover Nano 18, 58 (2023). https:\/\/doi.org\/10.1186\/s11671-023-03842-4","journal-title":"Discover Nano"},{"issue":"22","key":"18_CR17","doi-asserted-by":"publisher","first-page":"8649","DOI":"10.1021\/acs.analchem.3c01142","volume":"95","author":"N Feng","year":"2023","unstructured":"Feng, N., et al.: Artificial intelligence-based imaging transcoding system for multiplex screening of viable foodborne pathogens. Anal. Chem. 95(22), 8649\u20138659 (2023). https:\/\/doi.org\/10.1021\/acs.analchem.3c01142","journal-title":"Anal. Chem."},{"key":"18_CR18","doi-asserted-by":"publisher","DOI":"10.1002\/elan.202300207","volume":"36","author":"W Wu","year":"2024","unstructured":"Wu, W., et al.: Chemometrics-based signal processing methods for biosensors in health and environment: a review. Electroanalysis 36, e202300207 (2024). https:\/\/doi.org\/10.1002\/elan.202300207","journal-title":"Electroanalysis"},{"key":"18_CR19","doi-asserted-by":"publisher","unstructured":"Ma, X., Shi, Y., Gao, G., Zhang, H., Zhao, Q., Zhi, J.: Application and progress of electrochemical biosensors for the detection of pathogenic viruses. J. Electroanal. Chem. 950, 117867 (2023) https:\/\/doi.org\/10.1016\/j.jelechem.2023.117867","DOI":"10.1016\/j.jelechem.2023.117867"},{"issue":"16","key":"18_CR20","doi-asserted-by":"publisher","first-page":"6195","DOI":"10.1021\/acs.analchem.3c05115","volume":"96","author":"Y Xu","year":"2024","unstructured":"Xu, Y., Jiang, W.J., Bai, Y.Y., Yang, Y.J., Zhang, Z.L.: Artificial intelligence-assisted multiparameter size discrimination of silver nanoparticles through electrochemical collision. Anal. Chem. 96(16), 6195\u20136201 (2024). https:\/\/doi.org\/10.1021\/acs.analchem.3c05115","journal-title":"Anal. Chem."},{"issue":"7","key":"18_CR21","doi-asserted-by":"publisher","first-page":"171","DOI":"10.3390\/magnetochemistry9070171","volume":"9","author":"SA Qureshi","year":"2023","unstructured":"Qureshi, S.A., Aman, H., Schirhagl, R.: Prospects of using machine learning and diamond nanosensing for high sensitivity SARS-CoV-2 diagnosis. Magnetochemistry 9(7), 171 (2023). https:\/\/doi.org\/10.3390\/magnetochemistry9070171","journal-title":"Magnetochemistry"},{"key":"18_CR22","doi-asserted-by":"publisher","first-page":"2307091","DOI":"10.1002\/adfm.202307091","volume":"33","author":"Y Xie","year":"2023","unstructured":"Xie, Y., Chen, M., Liu, X., Su, X., Li, M.: Artificial intelligence-assisted label-free spectroscopic quantification of global DNA cytosine methylation in a miniature plasmonic pickering emulsion. Adv. Funct. Mater. 33, 2307091 (2023). https:\/\/doi.org\/10.1002\/adfm.202307091","journal-title":"Adv. Funct. Mater."},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Zou, H., Zhang, C.: Editorial: artificial intelligence, biosensing, and brain stimulation in neurodegenerative diseases: progress and challenges. Front. Aging Neurosci. 16, 1362574 (2024). https:\/\/www.frontiersin.org\/journals\/aging-neuroscience\/articles\/10.3389\/fnagi.2024.1362574","DOI":"10.3389\/fnagi.2024.1362574"},{"issue":"11","key":"18_CR24","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.3390\/diagnostics14111100","volume":"14","author":"CD Flynn","year":"2024","unstructured":"Flynn, C.D., Chang, D.: Artificial intelligence in point-of-care biosensing: challenges and opportunities. Diagnostics 14(11), 1100 (2024). https:\/\/doi.org\/10.3390\/diagnostics14111100","journal-title":"Diagnostics"},{"key":"18_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.bios.2024.116773","volume":"267","author":"JY Choi","year":"2025","unstructured":"Choi, J.Y., Park, S., Shim, J.S., Park, H.J., Kuh, S.U., et al.: Explainable artificial intelligence-driven prostate cancer screening using exosomal multi-marker based dual-gate FET biosensor. Biosens. Bioelectron. 267, 116773 (2025). https:\/\/doi.org\/10.1016\/j.bios.2024.116773","journal-title":"Biosens. Bioelectron."},{"key":"18_CR26","doi-asserted-by":"publisher","unstructured":"Akka\u015f, T., Reshadsedghi, M., \u015een, M., K\u0131l\u0131\u00e7, V., Horzum, N.: The role of artificial intelligence in advancing biosensor technology: past, present, and future perspectives. Adv. Mater. 37(34), 2504796 (2025). https:\/\/doi.org\/10.1002\/adma.202504796","DOI":"10.1002\/adma.202504796"},{"key":"18_CR27","doi-asserted-by":"publisher","unstructured":"Ng, X.J.K., Khairuddin, A.S.M., Liu, H.C., Loh, T.C., Tan, J.L., Khor, S.M., Leo, B.F.: Artificial intelligence-assisted point-of-care devices for lung cancer. Clinica Chimica Acta 570, 120191 (2025). https:\/\/doi.org\/10.1016\/j.cca.2025.120191","DOI":"10.1016\/j.cca.2025.120191"},{"issue":"2","key":"18_CR28","doi-asserted-by":"publisher","first-page":"75","DOI":"10.3390\/bios15020075","volume":"15","author":"MA Garcia-Junior","year":"2025","unstructured":"Garcia-Junior, M.A., et al.: Artificial-intelligence bio-inspired peptide for salivary detection of SARS-CoV-2 in electrochemical biosensor integrated with machine learning algorithms. Biosensors 15(2), 75 (2025). https:\/\/doi.org\/10.3390\/bios15020075","journal-title":"Biosensors"},{"key":"18_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2025.161328","volume":"510","author":"R Xu","year":"2025","unstructured":"Xu, R., et al.: Flexible interdigital electrode biosensor based on laser-induced-graphene and artificial intelligence system for real-time monitoring of tissue bleeding. Chem. Eng. J. 510, 161328 (2025). https:\/\/doi.org\/10.1016\/j.cej.2025.161328","journal-title":"Chem. Eng. J."},{"key":"18_CR30","doi-asserted-by":"publisher","first-page":"2010","DOI":"10.1007\/s12161-025-02844-5","volume":"18","author":"S Pandhi","year":"2025","unstructured":"Pandhi, S., Kumari, N., Jain, A., et al.: Emerging technologies in food safety: AI-powered, nano-enabled, and biosensor-based strategies for rapid contaminant detection. Food Anal. Methods 18, 2010\u20132024 (2025). https:\/\/doi.org\/10.1007\/s12161-025-02844-5","journal-title":"Food Anal. Methods"},{"issue":"33","key":"18_CR31","doi-asserted-by":"publisher","first-page":"18092","DOI":"10.1021\/acs.analchem.5c02369","volume":"97","author":"F Nazir","year":"2025","unstructured":"Nazir, F., et al.: An all-in-one sustainable smartphone paper biosensor for water toxicity monitoring combining bioluminescence detection with artificial intelligence. Anal. Chem. 97(33), 18092\u201318100 (2025). https:\/\/doi.org\/10.1021\/acs.analchem.5c02369","journal-title":"Anal. Chem."},{"key":"18_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s11468-025-03102-4","author":"P Umaeswari","year":"2025","unstructured":"Umaeswari, P., Wekalao, J., Kaliaperumal, K.: High-sensitivity amino acid sensing using machine learning-optimized graphene-gold-silver metasurface-based surface plasmon resonance biosensor in the terahertz regime. Plasmonics (2025). https:\/\/doi.org\/10.1007\/s11468-025-03102-4","journal-title":"Plasmonics"},{"key":"18_CR33","doi-asserted-by":"publisher","first-page":"2569","DOI":"10.1007\/s11468-024-02491-2","volume":"20","author":"J Wekalao","year":"2025","unstructured":"Wekalao, J., Mandela, N., Obed, A., et al.: Design and evaluation of tunable terahertz metasurface biosensor for malaria detection with machine learning optimization using artificial intelligence. Plasmonics 20, 2569\u20132593 (2025). https:\/\/doi.org\/10.1007\/s11468-024-02491-2","journal-title":"Plasmonics"},{"issue":"5","key":"18_CR34","doi-asserted-by":"publisher","first-page":"290","DOI":"10.3390\/bios15050290","volume":"15","author":"M Cheliukanov","year":"2025","unstructured":"Cheliukanov, M., et al.: Whole cells of microorganisms\u2014a powerful bioanalytical tool for measuring integral parameters of pollution: a review. Biosensors 15(5), 290 (2025). https:\/\/doi.org\/10.3390\/bios15050290","journal-title":"Biosensors"},{"key":"18_CR35","doi-asserted-by":"publisher","unstructured":"Deng, Z., Yun, Y.H., Duan, N., Wu, S.: Artificial intelligence algorithms-assisted biosensors in the detection of foodborne pathogenic bacteria: recent advances and future trends. Trends Food Sci. Technol. 161, 105072 (2025). https:\/\/doi.org\/10.1016\/j.tifs.2025.105072","DOI":"10.1016\/j.tifs.2025.105072"},{"key":"18_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.trac.2023.117345","volume":"168","author":"L Quadrini","year":"2023","unstructured":"Quadrini, L., Laschi, S., Ciccone, C., Catelani, F., Palchetti, I.: Electrochemical methods for the determination of urea: current trends and future perspective. TrAC, Trends Anal. Chem. 168, 117345 (2023). https:\/\/doi.org\/10.1016\/j.trac.2023.117345","journal-title":"TrAC, Trends Anal. Chem."},{"key":"18_CR37","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.1364\/JOSAB.559129","volume":"42","author":"MA Baqir","year":"2025","unstructured":"Baqir, M.A., Teksen, F.A., Alkurt, F.O., Karaaslan, M., Mughal, M.J., Choudhury, P.K.: Large quality factor metasurface-based biosensor for glucose monitoring. J. Opt. Soc. Am. B 42, 1651\u20131655 (2025). https:\/\/doi.org\/10.1364\/JOSAB.559129","journal-title":"J. Opt. Soc. Am. B"},{"key":"18_CR38","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1007\/s00604-025-07415-3","volume":"192","author":"BA Taha","year":"2025","unstructured":"Taha, B.A., Al-Rawi, M.A., Addie, A.J., et al.: Nanomaterial-enhanced biosensors for polycystic ovarian syndrome diagnosis and pathophysiological insights. Microchim. Acta 192, 547 (2025). https:\/\/doi.org\/10.1007\/s00604-025-07415-3","journal-title":"Microchim. Acta"},{"key":"18_CR39","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., et al.: The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71 (2021). https:\/\/doi.org\/10.1136\/bmj.n71","journal-title":"BMJ"},{"key":"18_CR40","doi-asserted-by":"publisher","unstructured":"Aria, M., Cuccurullo, C.: Bibliometrix: an R-tool for comprehensive science mapping analysis. J. Inform. 11(4), 959\u2013975, Elsevier (2017). https:\/\/doi.org\/10.1016\/j.joi.2017.08.007","DOI":"10.1016\/j.joi.2017.08.007"}],"container-title":["Lecture Notes in Computer Science","Learning and Collaboration Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-30542-8_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:43:21Z","timestamp":1783385001000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-30542-8_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032305411","9783032305428"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-30542-8_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"7 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montreal, QC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2026.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}