ArticleJournal of the American Medical Informatics Association : JAMIA2025
Preparing clinical research data for artificial intelligence readiness: insights from the National Institute of Diabetes and Digestive and Kidney Diseases data centric challenge.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- From Medical Records to AI-Ready Datasets: A Practical Guide for Clinical Researchers.Journal of clinical medicine · 2026Article
- Artificial Intelligence Readiness in Clinical Trial Operations: A Narrative Review and Site-Level Governance Framework.Healthcare (Basel, Switzerland) · 2026Review
- Artificial intelligence (AI)-aided clinical data management: Applications, human-in-the-loop workflows, and regulatory considerations.Global health & medicine · 2026Review
- Prioritising Data Quality Governance for AI in Prostate Cancer: A Methodological Proof-of-Concept Study Using Neural Networks for Risk Stratification.Diagnostics (Basel, Switzerland) · 2026Article
- Harnessing data to advance health and health equity.Journal of the American Medical Informatics Association : JAMIA · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
objectivesThe success of artificial intelligence (AI) and machine learning (ML) approaches in biomedical research depends on the quality of the underlying data. The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Data Centric Challenge was designed to address the challenge of making raw clinical research data AI ready, with a focus on type 1 diabetes studies available in the NIDDK Central Repository (NIDDK-CR). This paper aims to present a structured methodology for enhancing the AI readiness of clinical datasets. MATERIALS AND
methodsWe detail a systematic approach for data aggregation and preprocessing, including binning continuous data, processing text features, managing missing values, and encoding for categorical variables while maintaining the data integrity and compatibility with ML algorithms.
resultsWe applied the proposed methodology to transform raw clinical data from type 1 diabetes studies in the NIDDK-CR into a structured, AI-ready dataset. The evaluation process validated the effectiveness of our AI-readiness enhancement steps and explored the potential use cases in type 1 diabetes research. DISCUSSION: The methodology discussed in this paper will serve as guidance for preparing data for AI-driven clinical research, with the resulting AI-ready data to serve as a training tool for building and improving AI/ML model performance.
conclusionWe present a generalizable framework for preparing clinical research data for AI applications. The resulting datasets lay a strong foundation for downstream AI/ML applications, setting the stage for a new era of data-driven discoveries.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.