MedicalCode AI is an autonomous AI ICD-10 coding system. It reads the full chart, extracts every codable concept, and returns sequenced ICD-10-CM and ICD-10-PCS codes — each one traced back to the chart span that justified it, in seconds rather than half-hours.
ICD-10 (International Classification of Diseases, 10th Revision) is the global standard for classifying diagnoses and inpatient procedures. In the United States it splits into two related but distinct systems: ICD-10-CM (Clinical Modification, for diagnoses) and ICD-10-PCS (Procedure Coding System, for inpatient procedures). Together they carry roughly 150,000 codes, updated annually.
Coding ICD-10 well requires reading the full clinical chart — admission note, operative report, progress notes, discharge summary — extracting every codable fact, picking the most specific code from a tree with thousands of children, and then sequencing Principal vs Secondary diagnoses per the ICD-10-CM Official Guidelines. A human coder typically spends 15 to 30 minutes per inpatient case and longer on high-acuity charts. That is the work ICD-10 AI is designed to compress.
Diagnosis codes used on every U.S. claim — what is wrong with the patient, with full specificity for laterality, episode, and severity. Maintained by CDC/NCHS. MedicalCode AI returns the full Principal + Secondary diagnosis set, sequenced and traced back to the chart spans that justified each code.
Inpatient procedure codes built from a 7-character grammar: section, body system, root operation, body part, approach, device, qualifier. Maintained by CMS. The AI walks the grammar character-by-character against the candidate code set retrieved from the live PCS corpus — never assembled from memory.
A large language model ingests the full chart in a single pass and emits every codable clinical concept — diagnoses, procedures, devices, laterality, episode of care. The same extraction feeds both ICD-10-CM and ICD-10-PCS so the two systems share the same clinical reasoning.
Each extracted concept is mapped against an indexed copy of the official ICD-10-CM and ICD-10-PCS code books. The model is shown only the candidate code groups that actually fit the case, with their official descriptions, and selects from that retrieved set. Hallucinated codes are structurally impossible.
Final ICD-10 codes are picked from the candidate set, Principal vs Secondary diagnoses are sequenced per ICD-10-CM Official Guidelines, and every code carries a link back to the chart span and the reasoning chain the model used to pick it over its siblings.
ICD-10 AI is the use of large language models to read clinical documentation and produce ICD-10-CM (diagnosis) and ICD-10-PCS (inpatient procedure) codes that a human coder would assign. The "AI" part does the reading, lookup, and sequencing work; a human coder still reviews and signs off. MedicalCode AI returns the full coded chart in seconds, not the 15–30 minutes a coder typically spends on a moderately complex inpatient case.
No. The model never generates ICD-10 codes from memory. Every candidate code shown to the LLM is retrieved from an indexed copy of the official ICD-10-CM and ICD-10-PCS corpus, and the model is constrained to pick from that retrieved set. If a code is in the output, it exists in the live code book — the architecture makes hallucinated codes structurally impossible.
On case mixes we benchmark against, the AI matches or exceeds the inter-coder agreement rate that two human coders would reach on the same chart. Accuracy varies by specialty and chart quality — orthopedic and cardiology charts code very cleanly, complex sepsis-with-MCC cases are harder for both humans and the AI. We always position AI ICD-10 coding as a draft for the coder to review, not a replacement for the sign-off.
Yes. ICD-10-PCS is a 7-character procedural grammar (section, body system, root operation, body part, approach, device, qualifier), and the model walks the grammar character-by-character against the candidate codes retrieved from the live PCS corpus. Inpatient procedural coding is one of the highest-leverage parts of the platform because PCS is where coders spend the most time per chart.
Every ICD-10 code in the output is tied back to the exact span of the chart that justified it, plus the reasoning chain the model used to pick that code over its siblings. Audit packages can be exported per encounter — the same evidence a CDI specialist or external auditor would build by hand.
Yes. Sensitive identifiers (names, MRNs, dates of birth, addresses, contact details) are automatically masked before any chart reaches the AI system. We operate under a strict no-logging principle: prompts and outputs are not retained and are never used to train shared models. The platform is designed around HIPAA in the U.S., GDPR in the EU, and PDPA in Singapore.
A complete ICD-10-CM + ICD-10-PCS coded output for a moderately complex inpatient chart is returned in seconds. For comparison, a human coder typically spends 15 to 30 minutes on the same case. Speed comes from running keyword extraction once, in parallel with retrieval against the indexed code corpus.
Free to try — paste a clinical note and see the full coded ICD-10-CM and ICD-10-PCS output, with chart spans and reasoning, in seconds.
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