ArticleCell reports. Medicine2023
RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease.
Article in Cell reports. Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 23 citations in OpenAlex.
- Review
- Multi-omics deep learning improves FDG PET-CT-based long-term prognostication of breast cancer.NPJ precision oncology · 2026Article
- RadCLARE: an automated clinical language engine for detecting semantic errors in radiology reports.European radiology experimental · 2025Article
- Enhancing breast cancer diagnosis: non-invasive prediction of MKI-67 (Ki67) expression using ultrasound images.BMC cancer · 2025Article
- Prediction of postoperative infections by strategic data imputation and explainable machine learning.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Exploring personalized neoadjuvant therapy selection strategies in breast cancer: an explainable multi-modal response model.EClinicalMedicine · 2025Article
- Ethical considerations on the role of artificial intelligence in defining the futility in emergency surgery.International journal of surgery (London, England) · 2025Article
- Approach to a preparation of dataset combining digital mammographic images and patient clinical data from electronic medical records.Quantitative imaging in medicine and surgery · 2025Article
- Generative pre-trained transformer 4o (GPT-4o) in solving text-based multiple response questions for European Diploma in Radiology (EDiR): a comparative study with radiologists.Insights into imaging · 2025Article
- Artificial intelligence in surgery.Nature medicine · 2024Review
- BI-RADS Category Assignments by GPT-3.5, GPT-4, and Google Bard: A Multilanguage Study.Radiology · 2024Article
- Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.Journal of hematology & oncology · 2023Review
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Authors and funding
12 authors at 4 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital health data used in diagnostics, patient care, and oncology research continue to accumulate exponentially. Most medical information, and particularly radiology results, are stored in free-text format, and the potential of these data remains untapped. In this study, a radiological repomics-driven model incorporating medical token cognition (RadioLOGIC) is proposed to extract repomics (report omics) features from unstructured electronic health records and to assess human health and predict pathological outcome via transfer learning. The average accuracy and F1-weighted score for the extraction of repomics features using RadioLOGIC are 0.934 and 0.934, respectively, and 0.906 and 0.903 for the prediction of breast imaging-reporting and data system scores. The areas under the receiver operating characteristic curve for the prediction of pathological outcome without and with transfer learning are 0.912 and 0.945, respectively. RadioLOGIC outperforms cohort models in the capability to extract features and also reveals promise for checking clinical diagnoses directly from electronic health records.
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