Digestive HealthApril 10, 2026 10 min read
IBS How AI Analyses It
The AI behind reading IBS — and why explainability is what makes it trustworthy.
IBS is defined by patterns of stool form, frequency and triggers — exactly what passive data captures.
What the models do
Models trained on annotated data classify and quantify stool form and frequency and trigger correlation with diet and stress, converting raw capture into structured signals. Symptom diaries are burdensome and unreliable, which passive tracking fixes.
0
diaries to keep
Daily
objective record
Trigger
correlation
Evidence
for clinicians
Why explainability matters
A score nobody understands is a score nobody acts on. Every insight about IBS pairs with its contributing features, time window and a plain-language rationale.
- Stool form and frequency
- Trigger correlation with diet and stress
- Pattern stability over time
“The test you take every day beats the perfect test you take once a year.”
Human in the loop
The aim is not to replace clinical judgement but to feed it better, earlier, more objective data about IBS.
