Candidate conditions, highlighted with source text
Upload a clinical record and watch our AI extract candidate ICD-10 (diagnosis code) conditions for reviewer validation, link them to exact source text, and deliver an annotated PDF for audit, billing, or review.
Drop a PDF or .txt file here, or click to upload
PDF or .txt · Max 20 MB · One free try
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Last updated:
Reviewed by: Arkangel AI revenue cycle review team, Medical coding and AI content
AI helps medical coders by reading clinical notes, extracting candidate ICD-10 conditions, and linking each suggestion to the exact source text. Arkangel AI keeps coders in control: teams can inspect evidence, accept or reject findings, and use highlighted PDFs to support audits, billing review, and documentation improvement.
A practical comparison for coding leaders deciding how to review more records without losing source traceability.
| Need | Arkangel AI | Manual chart review |
|---|---|---|
| Record coverage | Reviews uploaded records consistently and flags candidate conditions | Depends on available coder time and sampling strategy |
| Source traceability | Links each finding to the exact clinical text | Requires manual note-taking and page references |
| Scanned documents | Supports OCR workflows for image-based PDFs after sign-in | Requires manual reading or separate OCR tooling |
| Consistency | Applies configured extraction criteria to supported records | Varies by reviewer, workload, and chart complexity |
We have worked with top healthcare institutions and built AI systems that help revenue cycle teams code faster, reduce denials, and find missed value in clinical documentation.
Our tools support clinical documentation review by surfacing candidate findings and source text for professional validation.
We believe coding teams deserve world-class AI tools that improve revenue integrity, reduce administrative burden, and help experts stay focused on high-judgment work.
Arkangel AI helps healthcare organizations streamline their revenue cycle with intelligent automation that works alongside your team.
Automate ICD-10 and CPT coding workflows
Identify missed revenue opportunities
Reduce claim denials
Improve compliance and quality standards
Optimize billing and coding time
Work with existing clinical documentation, including messy or partially structured data
Rising Denials
Denied claims require teams to review documentation and coding before resubmission.
Industry Consolidation
A growing share of physicians now work in hospital-owned or corporate-owned settings.
Admin Burden
Denial pressure and administrative burden continue rising across the revenue cycle.
We process structured and unstructured medical records, charts, notes, and billing-related documentation.
Our AI identifies ICD-10, CPT, and relevant billing opportunities using structured logic, retrieval systems, OCR, and validation layers.
We help detect inconsistencies, missed coding opportunities, and compliance risks before claim submission.
Coders and operators remain in control. Arkangel AI acts as a copilot that helps them move faster, work with more confidence, and free up time for higher-value work.
We meet the highest security and data standards. Your patient information stays protected with enterprise-grade controls and continuous monitoring.
This is not about replacing coders. It is about helping them work faster, with better support, more consistency, and more time for complex judgment calls.
Inspect candidate findings alongside supporting text to guide your team’s review.
Arkangel AI is a tool to amplify the work teams are already doing and free up time, not take control away from them.
If you're a coding leader, revenue cycle operator, or healthcare executive, we'd love to show you what this could look like for your team.
Book a CallJose Zea
For years, I have worked with leading healthcare institutions to optimize revenue, improve coding accuracy, and reduce costly operational inefficiencies. With Arkangel AI, we are building tools that help coding and billing teams save time, improve accuracy, and focus on what really matters: patients.
