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Updated: Jun 23, 2025

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Developing a Chatbot to Support Individuals With Neurodevelopmental Disorders: Tutorial.

Ashwani Singla1, Ritvik Khanna1, Manpreet Kaur1

  • 1Department of Pediatrics, University of Alberta, Edmonton, AB, Canada.

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|June 18, 2024
PubMed
Summary
This summary is machine-generated.

Families seeking health information for neurodevelopmental disabilities (NDDs) can now use CAMI, a novel chatbot. CAMI provides trusted resources, improving health literacy for individuals with NDDs and their families.

Keywords:
attention-deficit/hyperactivity disorderautismchatbotintellectual disabilityknowledge graphneurodevelopmental disabilityuser interface

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Neurodevelopmental Disabilities Research

Background:

  • Families of individuals with neurodevelopmental disabilities (NDDs) face significant challenges accessing reliable online health information.
  • Existing support, like in-person coaching, is limited, and general internet searches often prove insufficient for developing health literacy.
  • Chatbots offer potential for accessible information delivery, but their application in specialized healthcare, such as NDDs, is underdeveloped.

Purpose of the Study:

  • To develop and evaluate CAMI (Coaching Assistant for Medical/Health Information), a novel chatbot designed to provide families with trusted resources for neurodevelopmental disabilities (NDDs).
  • To improve access to core knowledge and services relevant to individuals with NDDs and their families through an AI-powered conversational agent.
  • To explore the integration of knowledge graphs and user-centered design in creating specialized health information chatbots.

Main Methods:

  • Development of the CAMI chatbot using the Django framework, incorporating a knowledge graph to map entities and relationships within the NDD domain.
  • Utilizing a combination of the Unified Medical Language System and expert-identified entities for NDD-specific named entity recognition from user queries.
  • Enhancing entity detection through the enrichment of vocabulary with synonyms and lay language terms, informed by end-user collaboration.

Main Results:

  • CAMI successfully provided relevant resources for the majority of user queries related to neurodevelopmental disabilities (NDDs).
  • Enriching the chatbot's vocabulary with synonyms and lay terms significantly improved its ability to detect relevant entities in user input.
  • The knowledge graph effectively connected symptoms, diagnoses, and resources, enabling the chatbot to offer comprehensive information.

Conclusions:

  • CAMI represents the first chatbot specifically designed to offer resources for neurodevelopmental disabilities (NDDs), demonstrating the potential of conversational AI in this area.
  • Engaging end-users and integrating their input with standard ontologies is crucial for enhancing the language recognition capabilities of health information chatbots.
  • Knowledge graphs provide a powerful method for integrating complex medical information, with implications for developing specialized chatbots across various health domains and optimizing expert input.