From OCR to Multimodal Reasoning: A Comprehensive Study of Clinical Document Understanding

Abstract

Digitization of medical records stands out among the most critical obstacles in contemporary medicine due to the unstructured nature of most of the collected clinical data. The traditional approach to text mining and optical character recognition (OCR) proves inefficient when processing the complex seman tics of textual content embedded in medical records along with their spatial layout. This systematic review considers the evolution from modular OCR-based approaches to advanced Multi modal Machine Learning and Visual Document Understanding systems designed for processing medical data. Specifically, recent progress in domain specific language models, layout-aware transformers, and OCR-free pipelines is discussed in detail. In addition to this, application of compact multimodal large language models and reinforcement learning methods designed to deal with noisy medical data is considered. The key findings show that despite significant improvement in accuracy and reduction of error propagation provided by multi modal and OCR-free models, many issues still arise with regard to the problem of computation costs, hallucinations, and patient privacy.