Proceedings from the inaugural Artificial Intelligence in Primary Immune Deficiencies (AIPID) conference

Abstract

We summarize here the proceedings of the inaugural Artificial Intelligence in Primary Immune Deficiencies (AIPID) conference, where experts and advocates gathered to advance research into applications of Artificial Intelligence (AI), Machine Learning (ML) and other computational tools in the diagnosis and management of Inborn Errors of Immunity (IEI). The conference focused on key themes such as expediting IEI diagnoses, challenges in data collection, roles of natural language processing (NLP) and large language models (LLMs) in interpreting electronic health records (EHRs), and ethical considerations in implementation. Innovative AI-based tools trained on EHRs and claims databases have discovered new patterns of warning signs for IEIs, facilitating faster diagnoses, and enhancing patient outcomes. Challenges persist in training AIs due to data limitations especially in rare diseases, overlapping phenotypes, and biases inherent in current datasets. Furthermore, experts highlighted the significance of ethical considerations, data protection, and the necessity for open science principles. The conference delved into regulatory frameworks, equity in access, and the imperative for collaborative efforts to overcome these obstacles and harness AI's transformative potential. Concerted efforts are still needed to successfully integrate AI into daily clinical immunology practice.