Leveraging Artificial Intelligence to Bridge Gaps in Pediatric Oncology Care for Marginalized Spanish-Speaking Communities

Grigorii Khvatskii, Angélica García-Martínez, Deng Pan, Matthew Belcher, Gerónimo Medrano Loera, Dayana Pineda Pérez, Juan Emmanuel Ferrari Muñoz-Ledo, Horacio Márquez-González, Nuno Moniz, Nitesh V. Chawla

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(LMICs) pediatric cancer patients and their caregivers often suffer from effects of underfunded, fragmented and outdated healthcare systems.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.

External IDs:dblp:conf/ijcai/KhvatskiiGPBLPM25