{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T14:28:06Z","timestamp":1790605686519,"version":"4.1.0"},"reference-count":12,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T00:00:00Z","timestamp":1742774400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>Warfarin is a common anticoagulant drug for thrombo-prophylaxis in stroke and venous thromboembolism, which has many advantages but also some disadvantages including narrow therapeutic window, vast drug interactions (and wide variability with foods\/herbs), as well as unpredictability of pharmacodynamics and\/or kinetics. Complicating factors can present as challenges for the outpatient clinicians trying to strike that balance due to the potential consequences of over or under dose anticoagulation with associated increased risk of bleeding and\/or thromboembolic events, respectively. Because warfarin interactions can drastically affect therapeutic outcomes, patient to healthcare provider communication regarding such potential drug-drug or diet-warfarin interactions is crucial for compliance with the medication and achieving successful treatment. Furthermore, language barriers cause low patient satisfaction scores and poor quality\/safety health care. In fact, the advancement and improvements in healthcare technology promise artificial intelligence (AI) as one of ideal options to optimize delivery of health care. The goal of this study is to develop Warfa-Check, a bilingual AI-based web app that matches both speakers of Arabic and English. The application helps users recognize potential warfarin-associated drug interactions with a simple user interface that accepts text, picture or voice commands. Warfa-Check, developed with Python and Flask as well as OpenAI\u2019s GPT-4 API with natural language processing tools trained to correctly interpret outbound warfarin interactions. Multiple validation methods and beta testing have been done to ensure that the app is data-driven, as well color coded alerts for interaction severity provide clear feedback to end-users. This easy-to-use application helps patients identify drug interactions in both English and Arabic. Warfa-Check represents a valuable avenue for improving the safety of our residents, simplifying medication management in high-risk individuals and streamlining workflow. Future development plans are to develop into other anticoagulants, and integrate with Electronic Health Records (EHRs).<\/jats:p>","DOI":"10.1371\/journal.pdig.0000756","type":"journal-article","created":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T05:03:18Z","timestamp":1742878998000},"page":"e0000756","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Development of an artificial intelligence-enhanced warfarin interaction checker platform"],"prefix":"10.1371","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1489-5538","authenticated-orcid":true,"given":"Monther Abdolmohsin","family":"Alsultan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Alabdulmuhsin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deema","family":"AlBunyan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"key":"pdig.0000756.ref001","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/B978-0-12-385467-4.00008-7","article-title":"Chapter 8 - pharmacogenomics and warfarin therapy.","volume-title":"Therapeutic drug monitoring [Internet]","author":"J Martin","year":"2012"},{"key":"pdig.0000756.ref002","first-page":"291","article-title":"Chapter 16 - Cardiovascular pharmacogenetics.","volume-title":"Genomic and precision medicine (Third Edition) [Internet]","author":"JH Li","year":"2018"},{"key":"pdig.0000756.ref003","volume-title":"Evaluation of Prescriptions Drug-Drug Interactions in a Public Hospital in Al-Kharj, Saudi Arabia","author":"NJ Ahmed"},{"issue":"3","key":"pdig.0000756.ref004","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1517\/14740338.5.3.433","article-title":"Warfarin and its interactions with foods, herbs and other dietary supplements","volume":"5","author":"EA Nutescu","year":"2006","journal-title":"Expert Opin Drug Saf"},{"key":"pdig.0000756.ref005","first-page":"11","article-title":"Challenges to physician\u2013patient communication about medication use: a window into the skeptical patient\u2019s world","volume":"6","author":"T Bezreh","year":"2011","journal-title":"Patient Preference and Adherence"},{"issue":"2","key":"pdig.0000756.ref006","doi-asserted-by":"crossref","first-page":"e122","DOI":"10.5001\/omj.2020.40","article-title":"Implications of language barriers for healthcare: a systematic review","volume":"35","author":"H Al Shamsi","year":"2020","journal-title":"Oman Med J"},{"issue":"10","key":"pdig.0000756.ref007","first-page":"e46777","article-title":"Language barriers in understanding healthcare information: Arabic-speaking students\u2019 comprehension of diabetic questionnaires in Arabic and English languages","volume":"15","author":"Z Gazzaz","year":"2023","journal-title":"Cureus"},{"issue":"8","key":"pdig.0000756.ref008","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1007\/s00586-007-0369-z","article-title":"Healthcare technology and technology assessment","volume":"16","author":"J Herndon","year":"2007","journal-title":"Eur Spine J"},{"key":"pdig.0000756.ref009","doi-asserted-by":"crossref","first-page":"100108","DOI":"10.1016\/j.glmedi.2024.100108","article-title":"Artificial intelligence in healthcare delivery: Prospects and pitfalls","volume":"3","author":"D Olawade","year":"2024","journal-title":"J Med Surg Public Health"},{"key":"pdig.0000756.ref010"},{"key":"pdig.0000756.ref011","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1186\/s40545-023-00624-2","article-title":"ChatGPT in pharmacy practice: a cross-sectional exploration of Jordanian pharmacists\u2019 perception, practice, and concerns","volume":"16","author":"K Hammour","year":"2023","journal-title":"J Pharm Policy Pract"},{"issue":"1","key":"pdig.0000756.ref012","first-page":"58","article-title":"Significance of machine learning in healthcare: features, pillars and applications","volume":"3","author":"M Javaid","year":"2022","journal-title":"Int J Intell Networks"}],"container-title":["PLOS Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0000756","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T05:04:07Z","timestamp":1742879047000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0000756"}},"subtitle":[],"editor":[{"given":"Jennifer N Avari","family":"Silva","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,3,24]]},"references-count":12,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3,24]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pdig.0000756","relation":{},"ISSN":["2767-3170"],"issn-type":[{"value":"2767-3170","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,24]]}}}