De-Identified Medical Datasets and the 2025 Readiness Gap:

Toward Equity, Scale, and Trust in Foundation Model Training

Abstract

Foundation models (FMs)—large-scale machine learning models trained on vast, diverse datasets—are reshaping the future of medical AI by powering diagnostic tools, clinical decision systems, and health information summarization. Sometimes referred to as large language models (LLMs) when applied to text, these models are increasingly deployed across clinical contexts. However, the de-identified datasets that form the backbone of FM training are often outdated, demographically limited, and difficult to access. These limitations raise profound concerns about fairness, scientific validity, and the potential for harm—particularly for marginalized populations underrepresented in training data. This paper argues that current de-identified datasets are not adequately representative or accessible for building trustworthy AI in healthcare. It critiques the prevailing assumption that de-identification alone ensures ethical readiness, showing instead how it can obscure structural biases and entrench inequality. Drawing on recent research and emerging technical and policy solutions—including synthetic data generation, automated de-identification, and global benchmarking—this paper explores what it means for datasets to be “2025-ready.” It proposes a new standard for responsible dataset design, grounded in demographic transparency, equity-centered governance, and inclusive participation in medical AI development.