Abstract

Introduction: Integrating artificial intelligence (AI) in medicine offers significant potential to enhance healthcare. AllergoAssist 2.0, based on ChatGPT-4, supports pediatricians in managing allergic diseases in children. This study explores its development, implementation, and performance.

Materials and Methods: AllergoAssist 2.0 was created by training on specialized data, including guidelines from European Academy of Allergy and Clinical Immunology (EAACI), Global Initiative for Asthma (GINA), and Allergic Rhinitis and its Impact on Asthma (ARIA), as well as pediatric allergology literature. Using OpenAI’s “custom GPT” feature, the model incorporated specific clinical data. Performance was evaluated with multiple-choice questions from pediatric allergy and immunology case collections.

Results: AllergoAssist 2.0 showed superior accuracy compared to ChatGPT-4, achieving 83% correct answers in pediatric allergy and 84.9% in pediatric immunology, significantly higher than ChatGPT-4 65.72% and 70.44% (p < 0.001). This demonstrates its superior capability to provide accurate, evidence-based recommendations.

Discussion: The specialized training of AllergoAssist 2.0 allows effective handling of clinical terminologies and protocols, enhancing its utility. While the model improves the quality of care and operational efficiency, it relies on the quality of the input data and thus highlights the challenges of AI decision-making. Future improvements aim to enhance transparency and provide continuous data updates.

Conclusion: AllergoAssist 2.0 exemplifies the potential of customized AI in specialized medical fields, suggesting accurate, evidence-based support to pediatricians. This advancement is a significant step towards personalized and efficient healthcare, highlighting the transformative impact of AI in medicine

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Authors

Gianluca Mondillo - Department of Woman, Child and of General and Specialized Surgery, AOU University of Campania "Luigi Vanvitelli", Naples, Italy

Simone Colosimo - Department of Woman, Child and of General and Specialized Surgery, AOU University of Campania "Luigi Vanvitelli", Naples, Italy

Alessandra Perrotta - Department of Woman, Child and of General and Specialized Surgery, AOU University of Campania "Luigi Vanvitelli", Naples, Italy

Vittoria Frattolillo - Department of Woman, Child and of General and Specialized Surgery, AOU University of Campania "Luigi Vanvitelli", Naples, Italy

Cristiana Indolfi - Department of Woman, Child and of General and Specialized Surgery, AOU University of Campania "Luigi Vanvitelli", Naples, Italy

Fabio Decimo - Department of Woman, Child and of General and Specialized Surgery, AOU University of Campania "Luigi Vanvitelli", Naples, Italy

Michele Miraglia del Giudice - Department of Woman, Child and of General and Specialized Surgery, AOU University of Campania "Luigi Vanvitelli", Naples, Italy

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright

Copyright (c) 2024 Italian Journal of Pediatric Allergy and Immunology

How to Cite
Mondillo, G., Colosimo, S. ., Perrotta, A., Frattolillo, V., Indolfi, C., Decimo, F., & Miraglia del Giudice, M. (2024). AllergoAssist 2.0: a Specialized Artificial Intelligence for Pediatric Allergology. Italian Journal of Pediatric Allergy and Immunology, 38(3). https://doi.org/10.53151/2531-3916/2024-561
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