Preparing Allergists to Practice in 2050 Using Artificial Intelligence.

Document Type

Article

Publication Date

11-2025

Identifier

DOI: 10.1016/j.jaip.2025.09.012; PMCID: PMC12697010

Abstract

As artificial intelligence (AI) becomes deeply embedded in clinical practice, the field of allergy and immunology is poised for transformation by 2050. Artificial intelligence is expected to evolve from a decision support tool to a collaborative partner in diagnostics, treatment personalization, and medical education. Allergy training programs will need to prepare fellows for a technologically advanced landscape by integrating AI literacy, data science, and virtual simulation into curricula. Fellowship programs will need to adopt adaptive learning platforms, high-fidelity simulations, and AI-powered clinical decision support to improve diagnostic acumen, procedural competency, and patient care. This evolution also demands attention to the ethical and legal challenges of AI implementation, including preserving patient autonomy, addressing algorithmic bias, and safeguarding data privacy. Fellows must develop skills to evaluate AI outputs critically and uphold transparent, human-centered care. Artificial intelligence will probably also reshape research practices through predictive analytics, digital twins, and automated trial matching, accelerating discovery in allergic and immunologic disease. Despite these advances, limitations such as the black box problem, lack of emotional intelligence, and misinformed patient self-diagnoses pose challenges. Clinicians will require new communication strategies, including brief cognitive behavioral interventions, to address AI-derived misconceptions and maintain trust. Rather than replacing allergists, AI is likely to expand their roles, freeing time for patient interaction while reinforcing their responsibility as interpreters, educators, and ethical stewards of digital tools. This review explores how graduate medical education and clinical practice in allergy and immunology must evolve to ensure that future allergists remain competent, compassionate, and technologically fluent in a dynamic AI-enhanced health care environment.

Journal Title

J Allergy Clin Immunol Pract

Volume

13

Issue

11

First Page

2927

Last Page

2935

MeSH Keywords

Artificial Intelligence; Humans; Allergy and Immunology; Allergists

PubMed ID

40976355

Keywords

Artificial intelligence; Electronic health record; Large language models; Machine learning; Medical education

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