{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T05:02:30Z","timestamp":1790053350866,"version":"4.0.1"},"reference-count":25,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T00:00:00Z","timestamp":1789862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmacy"],"abstract":"<jats:p>Artificial intelligence (AI) is entering medication-use and drug-development workflows, but the AI-specific capabilities needed for pharmacist workforce readiness remain poorly characterized. This pilot environmental scan and qualitative content analysis examined publicly accessible United States employment advertisements covering 1 January 2023 through 10 August 2026. Thirty-four distinct candidate requisitions were audited; 31 met operational criteria. Ten explicitly required AI-related work and formed the primary analytic sample, while 21 AI-enabling informatics, electronic-health-record, automation, analytics, and clinical-decision-support roles were retained as contextual comparators. AI-explicit employers sought pharmacists or PharmD-eligible professionals to evaluate model-generated clinical content, correct unsafe reasoning, construct prompts and cases, curate datasets and reference answers, verify claims, apply structured evaluation frameworks, validate dosing and pharmacokinetic reasoning, provide iterative model feedback, and implement AI-enabled clinical-pharmacology use cases. These activities were synthesized into five AI workforce-readiness competencies: AI-output evaluation and validation; AI systems and workflow literacy; prompt and evaluation-task design; AI performance assessment and improvement; and responsible AI implementation and governance. The framework distinguishes these AI-specific capabilities from the pharmacotherapy, medication-safety, evidence-appraisal, and quantitative expertise required to exercise them safely. These preliminary workforce signals support staged curricular development while requiring larger prospective and stakeholder-validated studies.<\/jats:p>","DOI":"10.3390\/pharmacy14060138","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T09:01:38Z","timestamp":1789981298000},"page":"138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Distilling AI Workforce-Readiness Competencies for U.S. Pharmacists from Job Advertisements: Implications for Pharmacy Education"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2476-5115","authenticated-orcid":false,"given":"Ashim","family":"Malhotra","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical and Biomedical Sciences, College of Pharmacy, California Northstate University, 9700 West Taron Drive, Elk Grove, CA 95758, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alvin","family":"Cheung","sequence":"additional","affiliation":[{"name":"California Northstate University, 9700 West Taron Drive, Elk Grove, CA 95758, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,20]]},"reference":[{"key":"ref_1","unstructured":"International Pharmaceutical Federation (2025). 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