FAQs
Frequently Asked Questions
Platform Features
What is ReviewGenX and how does it support medical record review?
ReviewGenX is MOS’s proprietary AI-powered platform that automates and streamlines medical record review using NLP (Natural Language Processing), ICR (Intelligent Character Recognition), and machine learning. It extracts, organizes, and highlights key data points from unstructured records – enabling faster, more accurate analysis for legal, insurance, and healthcare teams.
How does AI extract relevant information from unstructured records?
Our AI-powered medical record review platform interprets unstructured medical documents, including handwritten notes and scanned images, to isolate clinically significant data such as diagnoses, procedures, and treatment timelines. This intelligent parsing ensures reviewers can quickly identify critical case information without manual search.
Does AI medical review generate summaries tailored to different audiences?
Yes. AI medical review automatically generates audience-specific summaries for attorneys, claims professionals, and clinicians. Each version highlights the most relevant findings, treatment history, and decision-support insights for that role.
What types of medical records can the AI system process?
The review engine supports a wide range of file types—PDFs, Word documents, scanned images, and EHR exports. It processes both typed and handwritten content, ensuring comprehensive data capture across diverse record sources.
How does the platform handle duplicate pages?
Smart deduplication algorithms identify and remove repeated or near-identical pages before the review begins. This eliminates redundancy, prevents reviewer fatigue, and ensures clean, accurate case datasets.
AI Capabilities
What technologies power the review engine?
Is there a conversational AI interface for search?
Yes, the DK Insight Agent allows users to ask questions in natural language and receive instant, citation-backed answers. This conversational interface streamlines information retrieval across large document sets.
