Advanced AI-powered document intelligence system for medico-legal case processing, automating classification and extraction from massive multi-patient medical document compilations spanning thousands of pages.
Fine-tuned LLM implementation using Claude and GPT-3.5 achieving 95% classification accuracy for distinguishing initial reports, progress reports, evaluation reports, and other medical document types from complex PDF files containing scanned documents and mixed patient records.
Intelligent document splitting and extraction pipeline processing jumbo documents with thousands of pages, isolating individual patient reports, classifying medical document types, and summarizing contents into standardized formats required for medico-legal case analysis and proceedings.
Legal teams handling medico-legal cases faced overwhelming document volumes with thousands of pages containing mixed medical reports from multiple patients, requiring extensive manual review and organization before case analysis could begin.
Manual classification of medical reports into initial assessments, progress notes, evaluations, and other categories consumed significant time and expertise while introducing inconsistencies and errors in document categorization critical for legal proceedings.
Extracting and summarizing relevant medical information from scanned documents, PDFs, and handwritten notes into standardized formats required specialized medical knowledge and labor-intensive manual transcription prone to quality variations.
Fine-Tuned LLM Models - Custom-trained Claude and GPT-3.5 models optimized for medical terminology and report classification
95% Classification Accuracy - High-precision automated categorization of medical documents into initial reports, progress reports, and evaluation types
Jumbo Document Processing - Intelligent pipeline handling multi-thousand page compilations containing mixed patient records
PDF and OCR Integration - Advanced processing of scanned documents, PDFs, and mixed-format medical records
Document Splitting Intelligence - Automated identification and separation of individual patient reports from consolidated files
Multi-Patient Handling - Sophisticated logic distinguishing and organizing reports across multiple patients within single documents
Report Type Classification - Automated categorization into initial reports, progress reports, evaluation reports, and specialized medical documents
Structured Extraction Pipeline - AI-driven extraction of key medical information, diagnoses, treatments, and temporal data
Format Standardization - Automated summarization converting diverse report formats into standardized structures for legal analysis
Medico-Legal Optimization - Purpose-built features addressing specific requirements of legal case documentation and proceedings
Our development approach focused on building robust LLM-based classification and extraction systems capable of handling the complexity and scale of medico-legal document processing requirements.
We fine-tuned both Claude and GPT-3.5 models on medical report data, achieving 95% classification accuracy before implementing the extraction pipeline for standardized content summarization.
Fine-tuned Claude and GPT-3.5 language models on diverse medical report datasets including initial assessments, progress notes, evaluation reports, and specialized medical documents.
Achieved 95% classification accuracy through iterative training, validation, and optimization enabling reliable automated categorization of medical document types for medico-legal case processing.
Developed intelligent document processing pipeline handling jumbo files with thousands of pages, integrating OCR for scanned documents and PDF parsing for digital records.
Implemented sophisticated document splitting logic identifying patient boundaries, report transitions, and document type changes within consolidated multi-patient medical compilations.
Built extraction engine identifying and structuring medical information including diagnoses, treatments, patient history, and temporal sequences from classified documents.
Deployed summarization system converting diverse medical report formats into standardized structures required for medico-legal analysis, currently in implementation phase for production deployment.
Successfully achieved 95% classification accuracy in automated medical report categorization, significantly reducing manual review time and improving consistency in document organization for medico-legal cases.
The fine-tuned LLM models reliably distinguish initial reports, progress reports, and evaluation documents while handling the complexity of multi-thousand page compilations containing mixed patient records and scanned documents.
Extraction and summarization implementation progressing toward full deployment, positioned to transform medico-legal document processing by automating the conversion of diverse medical reports into standardized formats required for legal case analysis and proceedings.
