  X-ray images are vital in medical diagnostics, but their effectiveness is
limited without clinical context. Radiologists often find chest X-rays
insufficient for diagnosing underlying diseases, necessitating comprehensive
clinical features and data integration. We present a novel technique to enhance
the clinical context through augmentation techniques with clinical tabular
data, thereby improving its applicability and reliability in AI medical
diagnostics. To address this, we introduce a pioneering approach to clinical
data augmentation that employs large language models (LLMs) to generate patient
contextual synthetic data. This methodology is crucial for training more robust
deep learning models in healthcare. It preserves the integrity of real patient
data while enriching the dataset with contextually relevant synthetic features,
significantly enhancing model performance. DALL-M uses a three-phase feature
generation process: (i) clinical context storage, (ii) expert query generation,
and (iii) context-aware feature augmentation. DALL-M generates new, clinically
relevant features by synthesizing chest X-ray images and reports. Applied to
799 cases using nine features from the MIMIC-IV dataset, it created an
augmented set of 91 features. This is the first work to generate contextual
values for existing and new features based on patients' X-ray reports, gender,
and age and to produce new contextual knowledge during data augmentation.
Empirical validation with machine learning models, including Decision Trees,
Random Forests, XGBoost, and TabNET, showed significant performance
improvements. Incorporating augmented features increased the F1 score by 16.5%
and Precision and Recall by approximately 25%. DALL-M addresses a critical gap
in clinical data augmentation, offering a robust framework for generating
contextually enriched datasets.
