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Guideline-locked AI for type 1 diabetes

Answers a family can trust at 2 a.m.

MedEDiabuddy is an AI companion for parents and teenagers living with type 1 diabetes. It is locked to paediatric guidelines and DiaBuddy's validated dosing algorithms, adapted to Indian food and culture, and it sends emergencies to hospital before any AI is involved.

See the validation How it stays safe

Shortlisted for the Kaichi Kida Award Paper Session, APPES 2026, Brisbane

96% agreement with a blinded paediatric endocrinologist across 50 clinical scenarios
90% for ChatGPT and for Gemini on the same scenarios
56% for caregivers with an average of seven years' experience of diabetes
0 of 30 real family questions where both blinded reviewers preferred the nurse educator's answer
01 · The need

Families' questions don't wait for clinic hours, and general chatbots aren't built for children with diabetes.

India has more children and adolescents with type 1 diabetes than any other country — about 301,000 (IDF Diabetes Atlas 2025) — and very few diabetes educators to support them. Parents turn to search engines and general-purpose AI, which are not tied to paediatric guidelines, do not know Indian food or fasting practices, and can miss an emergency.

The knowledge gap persists Caregivers whose children had lived with diabetes for 7.3 years on average answered only 56% of everyday scenarios correctly. Mean HbA1c in their children was 10.0%.
Human support doesn't scale Diabetes educators are scarce across the Asia-Pacific; one educator cannot answer every family's message the moment it arrives.
Generic AI isn't accountable ChatGPT and Gemini scored 90% on our scenarios: good, but without guideline grounding, local context or a hard safety layer.
02 · How it works

A language model on a short leash: safety rules first, validated algorithms second, curated guidelines third.

  1. 1 Red-flag safety layerHard-coded checks run before any AI call. Signs of ketoacidosis or severe hypoglycaemia go straight to an emergency instruction.
  2. 2 Six deterministic DiaBuddy algorithmsMeal bolus, correction, basal titration, sick-day matrix, hypoglycaemia and exercise. Doses come from validated rules, not from the language model.
  3. 3 Curated guideline knowledgeISPAD 2022 consensus guidelines and their 2024 updates, ISPAE 2017 and ICMR type 1 diabetes guidelines, and the clinic's family booklet.
  4. 4 Indian contextA registry of 30 common Indian diabetes myths with evidence-based replies, carbohydrate values for more than 10,000 foods, and guidance for festivals and religious fasts.
  5. 5 An answer pitched to the readerParent, Teen and Doctor modes change the language and depth, not the clinical content.
Illustrative exchange · Parent mode
My daughter has been vomiting since morning, she is breathing fast and her urine ketones are 3+. Should I give extra insulin at home?
This needs hospital care now. Repeated vomiting, fast breathing and high ketones can be signs of diabetic ketoacidosis. Please take her to the nearest emergency department immediately and call your diabetes team on the way. Do not try to manage this at home.
Triggered by the safety layer before any AI response.
Eight domains covered in validation
Sick days Exercise Hypoglycaemia Insulin dosing Myths Nutrition Insulin storage Adolescence
03 · Evidence

Tested against experts, caregivers, leading AI models and a real diabetes educator

Kaichi Kida Award Paper Session · APPES 2026 · Brisbane · 5 October 2026 Development and validation of MedEDiabuddy, a guideline-curated AI companion for paediatric type 1 diabetes Presented by Dr Riya Shah for the Regency CDER and MedEClasses team. Shortlisted from abstracts across the Asia-Pacific.
Watch the talk
STUDY 1 · BLINDED VIGNETTE VALIDATION

50 scenarios, scored against a blinded expert

MedEDiabuddy, ChatGPT, Gemini and 30 caregivers answered the same 50 vignettes across eight domains. Answers were scored for expert concordance, guideline adherence and completeness.

Safety review

48 of 50 answers matched the expert, each scoring 5/5 for guideline adherence and completeness. Of the two that didn't, one (advising a doctor's review) carried no harm. One — declining an extra insulin dose during a high reading without measurable ketones — was rated potentially unsafe through under-treatment. Both errors erred on the side of caution, and the rule was corrected in the next version.

STUDY 2 · REAL-WORLD COMPARISON

AI versus the clinic's diabetes nurse educator

30 real questions from caregivers were answered by both MedEDiabuddy and our diabetes nurse educator. Two paediatric endocrinologists, blind to the source, scored every answer.

SafetyHigherP < 0.001
CompletenessHigherP < 0.001
Guideline adherenceHigherP < 0.001

Effect sizes were large on all three measures. In none of the 30 encounters did both reviewers rate the educator's answer above MedEDiabuddy's.

The reviewers used the scales differently (ICC 0.49–0.67; 77–92% of scores within one point), but the direction of effect was identical for both. A larger arm of about 120 encounters is under way.

Children's mean age14.9 years
Diabetes duration7.3 years
Mean HbA1c10.0%
Lower-middle income families53%
04 · Key observations

What we learned

  1. Curation beats scale A model restricted to paediatric guidelines and validated rules outperformed far larger general-purpose models on the questions families actually ask.
  2. It fails safe Both errors were conservative, never over-treatment, and the red-flag layer means emergencies never depend on the AI.
  3. It closes a 40-point gap MedEDiabuddy scored about 40 percentage points above experienced caregivers, the gap that drives avoidable emergencies.
  4. It extends the educator In real questions it matched or beat a trained nurse educator, so one educator can supervise support for many more families.
05 · Future plan

From validated prototype to a companion in every DiaBuddy app

DONE · 2026
  • Vignette validation: 96% expert concordance
  • 30-encounter blinded comparison with a nurse educator
  • Presented at APPES 2026 (Kaichi Kida session)
NEXT · 3–6 MONTHS
  • 100 real-life queries with a further independent adjudicator, then publication
  • Complete the ~120-encounter educator comparison
  • Launch as the chat option inside the DiaBuddy app
2027 · TRIAL
  • Five-centre randomised trial of DiaBuddy with MedEDiabuddy
  • Retrieval over the full guideline library
  • Copyright, trademark and CDSCO regulatory pathway
SCALE
  • Hindi and other Indian languages
  • Educator dashboard for supervising AI conversations
  • Extension to other paediatric endocrine conditions
06 · Developers

Clinicians building AI they would trust with their own patients

Dr Anurag Bajpai

Lead developer and principal investigator · FRACP

Consultant Paediatric and Adolescent Endocrinologist at Regency Centre for Diabetes, Endocrinology & Research, Kanpur, and President of ISPAE 2025–26. Developer of DiaBuddy, whose algorithms form MedEDiabuddy's clinical core, and of the MedEClasses suite of decision-support tools.

  • Trained at AIIMS New Delhi and the Royal Children's Hospital, Melbourne
  • Founder, MedEClasses Private Limited (DPIIT-recognised) and GROW Society; 10,000+ medical app downloads
  • Series Editor, Advanced Pediatric Endocrinology; h-index about 24
Dr Riya ShahPresenter, APPES 2026; blinded reviewer, Study 2
Dr Dhvani [surname]Blinded reviewer, Study 2
Dr Nimisha SachanClinical content and validation
Dr Alapan MahapatraDiaBuddy algorithms and validation
THE CLINICAL ENGINE

DiaBuddy: insulin decision support, published in Hormone Research in Paediatrics

Dose errors cut from 39% to 6%, sick-day errors 94.5% preventable, and better quality of life in a three-month pilot.

Visit DiaBuddy