{ "abstract": "Systematic reviews of randomized controlled trials (RCTs) support evidence-based clinical advancement. Clinical trial reporting guidelines, including CONSORT, establish common data elements that support systematic synthesis of published trial outcomes. However, the rapid rise of digital health technologies presents new challenges for reporting guidelines, and substantial real-world reporting heterogeneity has been observed. Therefore, machine-assisted evaluation of reporting adherence offers a pathway for more agile information management to accommodate the diverse and disruptive nature of digital health technology-enabled RCTs. Here, we propose an accelerated retrieval-to-abstraction literature synthesis framework to conduct article screening, text and table data preprocessing, and full-text review using an unmodified pretrained large language model. We apply this methodology in two digital health technology-enabled RCT case studies evaluating adherence to technology and digital health equity guideline reporting items. Large language model performance was on par with inter-annotator agreement F1 scores (≥0.9) in both screening and information-extraction tasks. These findings support the scalability and rigor of accelerated evidence synthesis and demonstrate the importance of common reporting elements across digital health technology-enabled RCTs. Author summary: Evidence-based medicine aligns high quality scientific evidence with real-world healthcare by integrating research findings into clinical best practices. Typically, the gold standard for updating current practice with new evidence integration is conducted through the systematic review of multiple similar clinical trials. Aggregate level insights are helpful to ensure practice changes are guided by rigorous and consistent findings. In recent years, technology has been an area of increased interest both in trial evaluation and clinical adoption to improve both patient and provider experiences. However, the rapid and diverse nature of technology innovation presents challenges for systematic evidence synthesis of similar digital health technology-enabled trials. Large language models are a type of artificial intelligence particularly well suited for organization of large scale text data, which is the main task of a systematic review and reporting guideline adherence evaluation. Therefore, we designed a retrieval-to-abstraction literature synthesis framework to help us understand current technology and digital health equity reporting practices observed in published clinical trials. This work presents an important contribution because we found that a large language model preformed as well as human annotators, while being faster and more cost-effective at the information extraction task.", "abstract_shown": true, "affiliations": [ "Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, United States of America", "Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, Minnesota, United States of America", "McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas, United States of America", "Integrated Science Program, Northwestern University, Chicago, Illinois, United States of America", "Department of Library Services, Mayo Clinic, Rochester, Minnesota, United States of America", "Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, United States of America" ], "article_type": "Research Article", "arxiv": null, "author_total": 15, "authors": [ { "affs": [ 0 ], "name": "Taylor B. Harrison" }, { "affs": [ 1 ], "name": "Dian Hu" }, { "affs": [ 2 ], "name": "Heling Jia" }, { "affs": [ 3 ], "name": "Shyang Hong Tan" }, { "affs": [ 4 ], "name": "Fang Chen" }, { "affs": [ 5 ], "name": "Zhongyu Zhang" }, { "name": "Qiuhao Lu" }, { "name": "Jinlian Wang" }, { "name": "Liwei Wang" }, { "name": "Ming Huang" }, { "name": "Larry J. Prokop" }, { "name": "Matt Hoy" }, { "name": "Jennifer St. Sauver" }, { "name": "Sunyang Fu" }, { "name": "Hongfang Liu" } ], "bibtex": "https://research.draconitic.com/paper/f4hqpwn6w38yt.bib", "cited_by": 0, "doi": "10.1371/journal.pdig.0001768", "fulltext_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001768", "id": "f4hqpwn6w38yt", "issns": [ "2767-3170" ], "landing_url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001768", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "1804|Meta-analysis and systematic reviews" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Language" ], [ "plos", "/Biology and life sciences/Psychology/Cognitive psychology/Language" ], [ "plos", "/Computer and information sciences/Computer architecture/User interfaces/Virtual reality" ], [ "plos", "/Engineering and technology/Electronics engineering/Computer engineering/Man-computer interface/Virtual reality" ], [ "plos", "/Medicine and health sciences/Clinical medicine/Clinical trials" ], [ "plos", "/Medicine and health sciences/Clinical medicine/Clinical trials/Randomized controlled trials" ], [ "plos", "/Medicine and health sciences/Mental health and psychiatry/Mental health therapies" ], [ "plos", "/Medicine and health sciences/Mental health and psychiatry/Mental health therapies/Psychotherapy" ], [ "plos", "/Medicine and health sciences/Pharmacology/Drug research and development/Clinical trials" ], [ "plos", "/Medicine and health sciences/Pharmacology/Drug research and development/Clinical trials/Randomized controlled trials" ], [ "plos", "/Research and analysis methods/Chemical synthesis" ], [ "plos", "/Research and analysis methods/Clinical trials" ], [ "plos", "/Research and analysis methods/Clinical trials/Randomized controlled trials" ], [ "plos", "/Research and analysis methods/Research assessment/Systematic reviews" ], [ "plos", "/Social sciences/Psychology/Cognitive psychology/Language" ] ], "openalex": "W7221195584", "pdf_url": "https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0001768&type=printable", "pmcid": null, "pmid": null, "published": "2026-10-09", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/f4hqpwn6w38yt.ris", "sources": [ { "first_seen": "2026-10-30T12:17:30Z", "native_id": "10.1371/journal.pdig.0001768", "source": "plos", "url": "https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001768" } ], "subjects": [ { "field": "medicine", "origin": "plos:clinical trials", "subfield": "clinical-trials" } ], "title": "Retrieval-to-abstraction LLM-assisted literature synthesis: Case demonstrations in digital health technology-enabled randomized controlled trial outcome reporting", "title_text": "Retrieval-to-abstraction LLM-assisted literature synthesis: Case demonstrations in digital health technology-enabled randomized controlled trial outcome reporting", "type": "peer-reviewed", "updated_at": "2026-10-30T12:17:30Z", "url": "https://research.draconitic.com/paper/f4hqpwn6w38yt", "venue": "PLOS Digital Health", "versions": [] }