Alexandre Chan Pharm.D., MPH, FCCP, William L. Baker Pharm.D., MPH, FCCP, Daniel Abazia Pharm.D., Jerry Bauman Pharm.D., FCCP, C. Lindsay DeVane Pharm.D., FCCP, Kellie J. Goodlet Pharm.D., Natalie Hall Pharm.D., James Kevin Hicks Pharm.D., PhD, FCCP, Ellen Jones Pharm.D., Chi-Hua Lu Pharm.D., Donald C. Moore Pharm.D., FCCP, Nicholas R. Nelson Pharm.D., Kaylee Putney Pharm.D., MBA, Aracely Sosa Pharm.D., Toby Trujillo Pharm.D., FCCP, Crystal Zhou Pharm.D
{"title":"Impact of artificial intelligence on future clinical pharmacy research and scholarship","authors":"Alexandre Chan Pharm.D., MPH, FCCP, William L. Baker Pharm.D., MPH, FCCP, Daniel Abazia Pharm.D., Jerry Bauman Pharm.D., FCCP, C. Lindsay DeVane Pharm.D., FCCP, Kellie J. Goodlet Pharm.D., Natalie Hall Pharm.D., James Kevin Hicks Pharm.D., PhD, FCCP, Ellen Jones Pharm.D., Chi-Hua Lu Pharm.D., Donald C. Moore Pharm.D., FCCP, Nicholas R. Nelson Pharm.D., Kaylee Putney Pharm.D., MBA, Aracely Sosa Pharm.D., Toby Trujillo Pharm.D., FCCP, Crystal Zhou Pharm.D","doi":"10.1002/jac5.70003","DOIUrl":null,"url":null,"abstract":"<p>Almost every facet of modern biomedical research involves artificial intelligence (AI). This ACCP commentary forecasts the role of AI in clinical pharmacy research and scholarship. The potential benefits/opportunities together with the limitations/challenges of AI are reviewed for stages of the scientific method including (1) developing the research question(s), study design, and execution; (2) data analysis; and (3) reporting and dissemination of clinical pharmacy research. Benefits and opportunities of AI in clinical pharmacy research include streamlining hypothesis generation and facilitating study design, overcoming limitations of traditional statistical analysis techniques, facilitating manuscript development and dissemination, and expediting peer review. Limitations and challenges of AI include the introduction of biases in subject recruitment; generation of false information, also known as “AI hallucinations”; concern of “black box” analyses that are difficult to validate; potential legal liabilities; lack of accountability; and the need for investigators to ensure the accuracy and integrity of AI-generated content. In summary, rapid progress of AI capabilities has great potential to revolutionize and accelerate clinical pharmacy research and scholarship; however, it is also imperative to recognize and mitigate the challenges and limitations introduced by AI.</p>","PeriodicalId":73966,"journal":{"name":"Journal of the American College of Clinical Pharmacy : JACCP","volume":"8 4","pages":"311-316"},"PeriodicalIF":1.3000,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jac5.70003","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American College of Clinical Pharmacy : JACCP","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jac5.70003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
Almost every facet of modern biomedical research involves artificial intelligence (AI). This ACCP commentary forecasts the role of AI in clinical pharmacy research and scholarship. The potential benefits/opportunities together with the limitations/challenges of AI are reviewed for stages of the scientific method including (1) developing the research question(s), study design, and execution; (2) data analysis; and (3) reporting and dissemination of clinical pharmacy research. Benefits and opportunities of AI in clinical pharmacy research include streamlining hypothesis generation and facilitating study design, overcoming limitations of traditional statistical analysis techniques, facilitating manuscript development and dissemination, and expediting peer review. Limitations and challenges of AI include the introduction of biases in subject recruitment; generation of false information, also known as “AI hallucinations”; concern of “black box” analyses that are difficult to validate; potential legal liabilities; lack of accountability; and the need for investigators to ensure the accuracy and integrity of AI-generated content. In summary, rapid progress of AI capabilities has great potential to revolutionize and accelerate clinical pharmacy research and scholarship; however, it is also imperative to recognize and mitigate the challenges and limitations introduced by AI.