Failure 1 · recall
Manglish yields zero facts
“Pani undo? Illa, pani illa. Chuma undu.” — a complete, unambiguous consultation — extracts no clinical facts at all. The rendered note is fourteen sections of “Not documented”.
Malayalam clinical AI
A consultation here moves between Malayalam, English and Malayalam written in Latin characters — often inside a single sentence. Clinoria's research programme exists because that mixing breaks most clinical NLP assumptions, and because we would rather publish the breakage than ship around it.
Experimental research · not a supported production language
Read this first
Clinoria does not claim Malayalam support. English is the supported language. Malayalam is under active research. In the current build, Malayalam written in its own script behaves reasonably on negation and symptom extraction, while Malayalam typed in Latin characters (“Manglish”) can fail completely or, in one documented case, invert clinical polarity.
What works
This is the case the current implementation handles correctly, and it is the reason the Malayalam research is worth continuing rather than starting over.
| Concept | Status | Note |
|---|---|---|
| fever | QUESTIONED | from the question |
| fever | ABSENT | denial recognised |
| cough | PRESENT | separate clause |
Verified output The denial and the present symptom coexist: “The patient denies fever” appears alongside cough as a current finding. Negation is scoped to its clause rather than to the whole utterance.
What fails
Most Malayalam written on a phone at speed is Latin script. The same clinical content, transliterated, currently produces either nothing or the opposite of what the patient said.
Failure 1 · recall
“Pani undo? Illa, pani illa. Chuma undu.” — a complete, unambiguous consultation — extracts no clinical facts at all. The rendered note is fourteen sections of “Not documented”.
Failure 2 · polarity
“Chest pain illa.” is recorded as chest pain: PRESENT and rendered as
“The patient reports pain in the chest.” This is the most dangerous defect in the
system: it is fluent, plausible and exactly wrong.
Why this happens — the actual mechanism
Malayalam negation and scoping rules live in the normalisation layer that processes speech-recognition output. Text submitted through the typed path is deliberately not passed through that layer, on the reasonable assumption that pasted text has already been normalised by whoever typed it.
That assumption holds for English and fails for Malayalam. Setting the language to Malayalam does not change it, because the normalisation stage is skipped before language is considered.
Honest framing This is a pipeline-architecture defect surfaced by a language feature, not a model-quality problem. It is fixable, and it is unfixed today.
The research problem
In a real Kerala consultation, a symptom may be introduced in English, quantified in Malayalam, denied with a Malayalam verb on an English noun, and hedged in a third register. Each of those switches can carry a clinical distinction.
What we have
What is missing
What would change it
Language policy
If you are a Malayalam-speaking clinician
You are the missing input. Not encouragement — transcripts, with your own documentation of what the correct note should have said, under a protocol you approve. That is the only route from “the engineers think this looks right” to evidence.
Collaborate
A demonstration can run Malayalam script, Manglish and English cases side by side, including the failures documented above.