{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T22:50:52Z","timestamp":1777243852105,"version":"3.51.4"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,6,20]],"date-time":"2019-06-20T00:00:00Z","timestamp":1560988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,6,20]],"date-time":"2019-06-20T00:00:00Z","timestamp":1560988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This study investigated the speech recognition abilities of popular voice assistants when being verbally asked about commonly dispensed medications by a variety of participants. Voice recordings of 46 participants (12 of which had a foreign accent in English) were played back to Amazon\u2019s Alexa, Google Assistant, and Apple\u2019s Siri for the brand- and generic names of the top 50 most dispensed medications in the United States. A repeated measures ANOVA indicated that Google Assistant achieved the highest comprehension accuracy for both brand medication names (<jats:italic>M<\/jats:italic>\u2009=\u200991.8%, SD\u2009=\u20094.2) and generic medication names (<jats:italic>M<\/jats:italic>\u2009=\u200984.3%, SD\u2009=\u200911.2), followed by Siri (brand names <jats:italic>M<\/jats:italic>\u2009=\u200958.5%, SD\u2009=\u200911.2; generic names <jats:italic>M<\/jats:italic>\u2009=\u200951.2%, SD\u2009=\u200916.0), and the lowest accuracy by Alexa (brand names <jats:italic>M<\/jats:italic>\u2009=\u200954.6%, SD\u2009=\u200910.8; generic names <jats:italic>M<\/jats:italic>\u2009=\u200945.5%, SD\u2009=\u200915.4). An interaction between voice assistant and participant accent was also found, demonstrating lower comprehension performance overall for those with a foreign accent using Siri (<jats:italic>M<\/jats:italic>\u2009=\u200948.8%, SD\u2009=\u200911.8) and Alexa (<jats:italic>M<\/jats:italic>\u2009=\u200941.7%, SD\u2009=\u200912.7), compared to participants without a foreign accent (Siri <jats:italic>M<\/jats:italic>\u2009=\u200957.0%, SD\u2009=\u200911.7; Alexa <jats:italic>M<\/jats:italic>\u2009=\u200953.0%, SD\u2009=\u200910.9). No significant difference between participant accents were found for Google Assistant. These findings show a substantial performance lead for Google Assistant compared to its voice assistant competitors when comprehending medication names, but there is still room for improvement.<\/jats:p>","DOI":"10.1038\/s41746-019-0133-x","type":"journal-article","created":{"date-parts":[[2019,6,20]],"date-time":"2019-06-20T11:02:54Z","timestamp":1561028574000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Do you understand the words that are comin outta my mouth? Voice assistant comprehension of medication names"],"prefix":"10.1038","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0035-7128","authenticated-orcid":false,"given":"Adam","family":"Palanica","sequence":"first","affiliation":[]},{"given":"Anirudh","family":"Thommandram","sequence":"additional","affiliation":[]},{"given":"Andrew","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Fossat","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,6,20]]},"reference":[{"key":"133_CR1","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1080\/02763869.2018.1404391","volume":"37","author":"MB Hoy","year":"2018","unstructured":"Hoy, M. B. Alexa, Siri, Cortana, and more: an introduction to voice assistants. Med. Ref. Serv. Q. 37, 81\u201388 (2018).","journal-title":"Med. Ref. Serv. Q."},{"key":"133_CR2","unstructured":"Pew Research Center. Fact-tank https:\/\/www.pewresearch.org\/fact-tank\/2017\/12\/12\/nearly-half-of-americans-use-digital-voice-assistants-mostly-on-their-smartphones\/ (2017)."},{"key":"133_CR3","unstructured":"Jeffs, M. OK Google, Siri, Alexa, Cortana; Can You Tell Me Some Stats On Voice Search? https:\/\/edit.co.uk\/blog\/google-voice-search-stats-growth-trends\/ (2018)."},{"key":"133_CR4","doi-asserted-by":"crossref","DOI":"10.2196\/mhealth.9705","volume":"6","author":"AE Chung","year":"2018","unstructured":"Chung, A. E., Griffin, A. C., Selezneva, D. & Gotz, D. Health and fitness apps for hands-free voice-activated assistants: content analysis. JMIR Mhealth Uhealth 6, e174 (2018).","journal-title":"JMIR Mhealth Uhealth"},{"key":"133_CR5","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1001\/jamainternmed.2016.0400","volume":"176","author":"AS Miner","year":"2016","unstructured":"Miner, A. S. et al. Smartphone-based conversational agents and responses to questions about mental health, interpersonal violence, and physical health. JAMA Intern. Med. 176, 619\u2013625 (2016).","journal-title":"JAMA Intern. Med."},{"key":"133_CR6","volume":"20","author":"TW Bickmore","year":"2018","unstructured":"Bickmore, T. W. et al. Patient and consumer safety risks when using conversational assistants for medical information: an observational study of Siri, Alexa, and Google Assistant. JMIR 20, e11510 (2018).","journal-title":"JMIR"},{"key":"133_CR7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0194811","volume":"13","author":"M Boyd","year":"2018","unstructured":"Boyd, M. & Wilson, N. Just ask Siri? A pilot study comparing smartphone digital assistants and laptop Google searches for smoking cessation advice. PLoS ONE 13, e0194811 (2018).","journal-title":"PLoS ONE"},{"key":"133_CR8","doi-asserted-by":"publisher","first-page":"43","DOI":"10.3390\/pharmacy6020043","volume":"6","author":"AV Fuentes","year":"2018","unstructured":"Fuentes, A. V., Pineda, M. D. & Venkata, K. C. N. Comprehension of top 200 prescribed drugs in the us as a resource for pharmacy teaching, training and practice. Pharmacy 6, 43 (2018).","journal-title":"Pharmacy"},{"key":"133_CR9","unstructured":"Statista. Total number of Medical Prescriptions Dispensed in the U.S. from 2009 to 2016 https:\/\/www.statista.com\/statistics\/238702\/us-total-medical-prescriptions-issued\/ (2019)."},{"key":"133_CR10","unstructured":"ClinCalc. The Top 200 Drugs of 2019 https:\/\/clincalc.com\/DrugStats\/ (2019)."},{"key":"133_CR11","unstructured":"Wired. 8 People Test Their Accents on Siri, Echo and Google Home https:\/\/www.youtube.com\/watch?v=gNx0huL9qsQ (2017)."},{"key":"133_CR12","unstructured":"Stone Temple. 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