An end-to-end system for explainable clinical coding across languages and diverse medical data sources
An AI system that explains its reasoning when automatically coding medical records could reduce administrative burden and improve transparency in healthcare workflows across languages.
This paper presents a multilingual explainable AI system for automated clinical coding that assigns ICD-10 codes to medical records across languages and data sources. The system improves transparency in AI-assisted billing/coding workflows with potential to reduce manual coding burden.
What the study was
- Study design
- Methodological/Validation study (NLP system)
- Category
- Diagnostics
- Maturity
- Exploratory
- Journal
- BMC Medical Informatics and Decision Making
Why it surfaced
Incremental AI-NLP advance in clinical coding; multilingual support is useful but relevance to biomedical research pipeline limited; no patient outcome data.
A plain-language summary of published research — not medical advice. Talk to a clinician about your own care.