ReviewThe journal of allergy and clinical immunology. In practice2025
Preparing Allergists to Practice in 2050 Using Artificial Intelligence.
Review in The journal of allergy and clinical immunology. In practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Effectiveness of an artificial intelligence-assisted training program on cleaning competency among hospital environmental service staff.BMC health services research · 2026Trial
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.