Language models adapted to the clinical domain for automated medical record summarization and diagnosis/procedure coding.
Also applied to adverse event detection and large-scale analysis of massive volumes of clinical text.
Large language models (LLMs) like GPT-4 and Llama are extraordinarily capable at general language tasks. But when applied to the clinical domain without specific adaptation, they produce a risk that healthcare professionals recognize immediately: fluent, convincing outputs that contain subtle clinical errors.
A generic LLM might summarize a medical record elegantly — but get the medication wrong, invert the timeline of a diagnosis, or miss a critical contraindication. In a regulatory context, where every piece of data needs source traceability, an LLM that 'hallucinates' parts of the output is unacceptable.
iHealth solves this through fine-tuning of our language models on real Brazilian clinical corpora — combined with retrieval-augmented generation (RAG) architectures that ensure outputs are always grounded in source data, not the model's parametric knowledge.
Our language models are fine-tuned on a corpus of real, anonymized, and curated Brazilian clinical texts — spanning more than 20 medical specialties and over 10 years of clinical documentation. This process teaches the model to understand the vocabulary, narrative structures, and clinical reasoning patterns specific to the Brazilian context, drastically reducing the clinical errors generic models make.
For tasks that require fidelity to the source document — such as medical record summarization or answering questions about a patient's history — we use RAG: the model doesn't answer from its parametric knowledge, but retrieves the relevant excerpts from the medical record and uses them as context to generate the output. Every statement is traceable to a specific excerpt of the source text — auditable and regulatorily defensible.
No LLM output is delivered without clinical validation. Our process includes review by medical specialists of representative samples of each output type — summaries, coding, adverse event detection — with documented precision, recall, and inter-rater agreement metrics. Results feed continuous retraining and improvement cycles.
For regulatory use, every output from iHealth's clinical LLM comes with an audit trail: which model generated it, which model version, which excerpts of the source document were used as context, and the estimated confidence of the output. This traceability is what allows LLM outputs to be used in regulatory dossiers and HTA submissions.
Automatic generation of structured clinical summaries from extensive medical records — with primary diagnoses, relevant history, current medications, exam results, and significant hospitalization events. Reduces review time in RWE studies and clinical research screening.
Automatic assignment of ICD-10, TUSS, and DRG codes from the complete clinical text — including free-text progress notes. Captures secondary diagnoses, complications, and procedures that manual coding frequently misses.
Automatic identification of mentions of adverse events, drug reactions, and near misses in clinical text — at scale and in real time. Supports active pharmacovigilance and generation of post-market safety evidence.
For studies that require analyzing thousands of medical records — disease burden, epidemiological mapping, patient journey — the LLM processes volumes unfeasible with human review, while maintaining methodological consistency and traceability.
Talk to our team and understand how iHealth's technology pillars translate into real results — for clinical research, market access, hospital management, or data auditing.
