Leveraging Artificial Intelligence to Bridge Gaps in Pediatric Oncology Care for Marginalized Spanish-Speaking Communities
Grigorii Khvatskii, Angelica Garcia Martinez, Deng Pan, Matthew Belcher, Gerónimo Medrano Loera, Dayana Pineda Pérez, Juan Emmanuel Ferrari Muñoz-Ledo, Horacio Márquez-González
Abstract
In low-and middle-income countries (LMICs) pediatric cancer patients and their caregivers often suffer from effects of underfunded, fragmented and outdated healthcare systems. One of these effects is a breakdown of communication between hospital staff and caregivers, which is felt stronger among vulnerable populations. Our proposed solution integrates Large Language Models (LLM) and Automatic Speech Recognition (ASR) technologies to enhance communication between caregivers and healthcare providers while integrating community feedback. We combine cutting-edge technology with existing hospital infrastructure to allow for easy deployment and testing. The system will improve access to health, nutrition, and parental care programs, prioritizing caregiver engagement and real-time interaction. Ultimately, our system will pave the way to more equitable access to medical care, and address structural barriers affecting marginalized communities.
BibTeX
@inproceedings{ijcai2025_leveragingartifi,
title = {Leveraging Artificial Intelligence to Bridge Gaps in Pediatric Oncology Care for Marginalized Spanish-Speaking Communities},
author = {Grigorii Khvatskii and Angelica Garcia Martinez and Deng Pan and Matthew Belcher and Gerónimo Medrano Loera and Dayana Pineda Pérez and Juan Emmanuel Ferrari Muñoz-Ledo and Horacio Márquez-González and Nuno Moniz and Nitesh V. Chawla},
booktitle = {IJCAI 2025},
year = {2025}
}