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For voice data generation projects, the origin of the problem may lie in the recruiting methods used during data collection efforts. This study introduces a generative AI pipeline for \u201cdataset decision support\u201d, recommending recruitment decisions based on high\u2010dimensional insights.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The publicly available GOSSIS\u20101\u2010eICU dataset was filtered to create patient populations that were relevant to voice data generation projects. Lab results and vital signs from the electronic health record were also used to train a neural network for prediction of disease type. Prediction uncertainty estimates were included in the dataset as approximate indicators of health complexity. To select the best recruitment choice for addressing imbalances, an open\u2010source large language model (LLM) was then instructed to assess dataset statistics and the characteristics of possible participants. Simulations were run in which the system constructed datasets of 250 patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In over 90% of cases, the proposed system reduced categorical imbalances and widened continuous distributions when compared to randomly sampled counterfactual datasets (\n                      <jats:italic>q<\/jats:italic>\n                      \u2010value &lt;\u20090.05). Variables included race, age, BMI, sex, disease type, oxygenation status, co\u2010morbidities, post\u2010operative status, Glasgow Coma Scale verbal response score, the Acute Physiology Score III, prediction uncertainty, vital signs, and lab results.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>LLMs may provide useful, explainable recommendations when presented with dataset distribution statistics and candidate profiles. In the future, this simulated scenario may be extended to align with conditions in emergency departments or other high\u2010volume settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>3.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/lio2.70519","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T12:31:16Z","timestamp":1787574676000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Large Language Models for Optimizing Patient Recruitment Decisions in Voice Data Generation Projects"],"prefix":"10.1002","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1196-2153","authenticated-orcid":false,"given":"James","family":"Anibal","sequence":"first","affiliation":[{"name":"Computational Health Informatics Lab, Institute of Biomedical Engineering University of Oxford  Oxford UK"},{"name":"Center for Interventional Oncology NIH Clinical Center, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geetha Krishna Chaitanya","family":"Nama","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering University of South Florida  Tampa Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shrramana","family":"Ganesh","sequence":"additional","affiliation":[{"name":"USF Health Voice Center, Department of Otolaryngology\u2010Head &amp; 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