Accelerating Pathology Report Digitization: A Multi-Engine OCR and LLM Framework for Healthcare Applications
Synopsis
This study presents a framework called DiText-OCR (Dual-integrated Text Extraction using Hybrid OCR Engines), which leverages multiple OCR tools and domain-specific dictionaries to digitize diverse text types including printed text and low-quality scans, then processes the extracted text with Large Language Models (LLMs) for named entity recognition, relationship extraction, and data structuring, integrating the resulting structured data into healthcare databases and systems to support clinical decision support, research, and analytics while ensuring interoperability; the text also notes challenges in handling non-standard report formats, maintaining patient privacy, and addressing current limitations of OCR and LLM technologies in medical contexts, with future work aiming to integrate the
Interpretation
Proposes the DiText-OCR framework, combining multiple OCR engines with domain-specific dictionaries to digitize diverse text types such as printed text and low-quality scans. Relative to a single-OCR pipeline, the framework uses a combination of multiple engines plus domain dictionaries to handle varied text quality in pathology reports. Evidence comes from the framework description in the paper; the loaded markdown contains no experimental data, sample sizes, or comparison results.
After OCR extraction, LLMs are used for named entity recognition, relationship extraction, and data structuring. Moves LLMs beyond text generation into the information extraction and structuring stage of pathology reports, forming a continuous pipeline from OCR to structured output. Evidence is the method statement in the paper description; no extraction accuracy or evaluation metrics are provided.
Structured data are integrated into healthcare databases and systems for clinical decision support, research, and analytics, with an emphasis on interoperability. Extends digitization results from the text level to structured data usable by downstream systems, pointing toward clinical applications and data analytics scenarios. Evidence is the application vision described in the paper; no deployment scale or actual usage outcomes are given.
Perspective
The framework targets the digitization and structuring of pathology reports, applicable to reports containing printed text and low-quality scans, and is planned for integration with electronic health records, extension to other medical documents, and use in advanced research and predictive analytics; its value lies mainly in providing structured data for clinical decision support, research, and analytics while ensuring interoperability.
The loaded markdown is incomplete and contains no figures, experimental data, or evaluation metrics, so the actual accuracy and generalization of OCR and LLMs on pathology reports cannot be judged; how non-standard report formats are handled, the specific mechanisms for protecting patient privacy, and how the current limitations of OCR and LLMs in medical contexts are mitigated remain open questions to watch.