Preparing Allergists to Practice in 2050 Using AI

Abstract

As artificial intelligence (AI) becomes deeply embedded in clinical practice, the field of allergy and immunology is poised for transformation by 2050. AI 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 critically evaluate AI outputs and uphold transparent, human-centered care. AI 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 healthcare environment.

Introduction

The field of allergy and immunology is expected to undergo major transformations by the year 2050, driven by advancements in biomedical research as well as in artificial intelligence (AI) and other emerging technologies. AI has been defined as “computer systems capable of performing tasks that typically require human intelligence, such as pattern recognition, learning from data, and decision-making”. (1) It is anticipated that AI will shift from a supportive tool to a collaborative partner in patient care and research, enabling the analysis of vast datasets for personalized diagnoses, tailored healthcare solutions and research with enhanced discovery, prediction, and generation. (2) (3) (4) Electronic Health Records (EHR) are evolving to summarize a patient’s medical history, highlighting critical details, in order to streamline data access as well as to summarize patients encounters with the hope of improved patient care and decreased provider burden. (5) (6) By 2050, graduate medical education (GME) in allergy and immunology also will need to undergo transformation to prepare specialists for this evolving landscape. Emerging technologies such as virtual reality, wearable sensors, simulated patients and robotics are reshaping the practice of allergy/immunology. These innovations provide immersive training environments and advanced diagnostic methods alongside novel therapeutic approaches both in the clinic and in the patient’s home. (7) (8) This dynamic partnership between physicians and AI promises a future of enhanced patient care and innovative approaches to healthcare delivery. In this review, we will attempt to describe some examples of how allergists-immunologists will need to prepare to practice in the future.

AI in Education

The evolution of AI into healthcare requires a reimagination of its role in both graduate and continuing medical education. As AI and other technologies evolve, curricula will need to integrate AI skillsets, including foundational literacy in machine learning (ML), data science, and clinical decision support systems (CDSS) so that providers understand these tools, their governance structures, and are able to participate in and assess their outputs for accuracy, bias, and clinical relevance. Table 1 outlines how AI can enhance training and clinical competencies in the field of Allergy and Immunology, using the Accreditation Council for Graduate Medical Education (ACGME) framework. It is divided into competencies, procedural skills and curriculum topics. Each topic has specific tasks or areas of clinical knowledge, such as “Writing Allergen Immunotherapy Prescriptions” or “Drug Desensitization. Finally, it describes how AI tools, such as decision-support systems, image analysis, or predictive modeling, can support or improve those competencies. Figure 1 presents a conceptual framework for integrating AI into graduate medical education in allergy and immunology.

Table 1:

ACGME-defined procedural competencies, core curriculum and educational components with suggestions for how Artificial Intelligence can potentially help Allergy and Immunology fellows obtain them.

Category	Competency or Topic	AI-Enhanced Role in Education and Practice	Reference
Procedural Competencies	Writing Allergen Immunotherapy Prescriptions	Decision-support tools personalize immunotherapy regimens based on location, laboratory, and clinical data.	(70)
	Drug Desensitization or Incremental Challenge	Risk prediction models guide challenge or desensitization decisions and protocols	(8)
	Immediate Hypersensitivity Skin Testing	Computer vision image analysis interprets skin test results and changes over time	(71)
	Writing an Immunoglobulin Prescription	Dosing and product selection is based on lab results, clinical response, and indication.	(72)
	Interpretation of Pulmonary Function Testing	Models determine disease entity and clinical trajectory	(73)
	Food Challenge Testing	AI-driven monitoring tools track subtle symptom progression in real-time	(74)
Core Curriculum Topics	Asthma	Machine Learning models predict asthma exacerbations based on patient data. Retrospective review of prior assessments informs future histories and exams.	(75)
	Atopic Dermatitis	Computer Vision-assisted skin imaging assists in differential diagnoses and treatment plans	(76)
	Drug Allergy	AI analyzes pharmacovigilance data for drug allergy trends	(8)
	Food Allergy	Machine learning interprets food allergy history and testing in combination with component-resolved diagnostics to predict food tolerance	(77)
	Primary and Acquired Immunodeficiency	Text mined EHR data aids machine learning models in early immunodeficiency detection in EHR data. Machine learning provides probabilistic scores and integrates diverse data streams to predict immunogenetic diagnosis.	(78)
	Rhinitis	Machine learning algorithms analyze wearable devices to track symptom patterns and medication adherence	(79)
	Sinusitis	Computer vision-assisted imaging enhances sinusitis diagnosis and clinical decision support systems suggest evidence-based management options.	(80)
	Stinging Insect Allergy	Natural language processing-assisted risk assessment of anaphylaxis risk, and personalizing of venom immunotherapy.	(81)
	Urticaria and Angioedema	Deep learning helps to identify triggers of chronic urticaria and assists in personalized treatment options including biologics	(82)
	Anaphylaxis	NLP-powered models stratify anaphylaxis risk and enhance emergency response readiness.	(83)
Key Educational Components	Clinical Training	Chatbod-powered simulators train fellows to manage rare and complex clinical cases that might not otherwise be seen in training.	(84)
	Medical Knowledge	Adaptive learning platforms tailor education to fellow knowledge gaps	(85)
	Practice-Based Learning and Improvement	Precision education models track fellow performance, suggests improvements, and provides real-time feedback	(17)
	Interpersonal and Communication Skills	NLP-powered patient simulations train empathetic communication skills	10)
	Professionalism	NLP models using text to speech evaluate communication strategies of trainees	(86)
	Systems-Based Practice	Deep learning evaluation of outcome metrics teaches trainees to optimize healthcare workflows and resource utilization	(59)
	Scholarly Activities	NLP streamlines literature reviews, identifies research gaps.	(87)

Figure 1. Integrating Artificial Intelligence into Graduate Medical Education in Allergy and Immunology.

This conceptual framework illustrates key domains for incorporating artificial intelligence (AI) into graduate medical education. Central pillars include AI literacy, simulation, clinical reasoning, precision education, interdisciplinary collaboration, faculty development, and oversight. Surrounding these are targeted strategies such as machine learning, clinical decision support systems, immersive education, personalized learning, and real-time performance data. The model also emphasizes ethical training, governance, and multisource evaluations. Together, these elements support the responsible, effective, and adaptive integration of AI into training the next generation of allergy and immunology specialists.

Several studies have already demonstrated that integrating AI into medical training can enhance trainee performance. One study compared the effectiveness of AI tutoring to expert instruction in teaching a simulated surgical procedure. The authors found that a virtual operating assistant providing automated audiovisual feedback resulted in significantly higher surgical performance and skill transfer scores compared with traditional expert instruction or no specialized training. (9) Another study explored a deep neural network heatmap assisted learning impact on medical students’ interpretation of hip fractures. The findings indicated that those using AI-assisted learning resulted in gained accuracy, compared to conventional learning. (10) A systematic review found that AI-generated feedback led to significantly higher expertise scores and improved overall performance among trainees compared to traditional expert feedback or control groups. (11) These studies collectively suggest that some AI integration into medical education can enhance learning efficiency and skill acquisition among trainees. However, despite several studies having been published on the potential of AI in education, there was only one identified study that addressed education in allergy.

AI is likely to make significant contributions in the training of allergists/immunologists, especially in their mastery of rare disorders that are encountered infrequently in clinical settings. Simulation-based learning modules could provide this exposure leading to increased preparedness in the management of these patients in clinical practice. High-fidelity simulation platforms and virtual reality may offer customizable cases, immersive adaptable simulations and real-time monitoring, and performance scoring to foster clinical reasoning and communication skills. Virtual patient simulation with Large Language Model (LLM) enhanced communication improved student perception of learning clinical reasoning skills and was viewed as more authentic than a computer-based platform. (12) Complementing this approach, one particular simulation (CureFun) harnesses the potential of LLMs in a model-agnostic framework to simulate virtual patients, facilitating natural dialogue, evaluating interactions, and offering targeted suggestions to enhance clinical inquiry skills. These platforms address the high cost and logistical challenges associated with traditional human simulated patients, significantly broadening access to high-quality, scalable clinical training. (13)

Advancements in AI-powered simulation systems extend beyond dialogue-based interactions to incorporate diverse modalities for comprehensive clinical training. One study found that simulations improved the management of anaphylaxis and team confidence. It also reducing staff reluctance to perform high-risk challenges in the ambulatory setting. (14) A meta-analysis found that use of virtual patients improved clinical skills and was effective at improving knowledge. The skills that improved were clinical reasoning, procedural skills, and a mix of procedural and team skills. (15) Another systematic review found that compared with noncomputer instruction, a positive effect favored virtual patients for knowledge, reasoning, and various other skills. Comparisons of different virtual patient designs suggested that repetition until demonstration of mastery, advance organizers, enhanced feedback, and explicitly contrasting cases improved learning outcomes. (16) Collectively, these technological innovations not only enhance the authenticity and reliability of simulated clinical encounters, but also provide immediate, constructive feedback, thereby elevating the standard of medical education and offering promising avenues for future study and integration into the field of Allergy and Immunology and clinical practice.

AI-driven “precision education” models, such as Trainee Attributable and Automatable Care Evaluations in Real-time (TRACER), optimizes performance assessment and feedback. (17) By continuously analyzing clinical encounters, documentation, and decision-making patterns, these models provide individualized feedback, highlighting competency gaps and promote self-directed learning, a critical skill for adult learners. These tools could provide more data to supplement the apprentice-teacher model using more personalized, multi-modal, data-informed approaches to medical education. As with all programs that are being created like TRACER, validation and evaluation of these models in practice are likely to be important to ensure data privacy and that clinical metrics are not compromised.

AI may also transform key educational components by optimizing workflow efficiency, enhancing communication training, and advancing scholarly contributions. In systems-based practice, it is hoped that workflow optimization will allow trainees to adapt to more efficient practice within complex medical systems. AI could also accelerate research efforts by streamlining literature exploration, improved identification of relevant studies which are already being used to index articles with text recognition allowing trainees to focus more time on critical analysis and creative approaches to research design. Through these applications, the role of AI will need to be studied to ensure that it enhances clinical competencies rather than detracting from knowledge acquisition. In theory, use of some of these technologies could foster a more research-oriented, technologically adept, and patient-centered approach to allergy and immunology. The primary concerns regarding AI in education are that it could reinforce existing biases if training data are skewed, leading to skewed experiences for learners. There is also a risk of overreliance on AI, where critical thinking and human judgment get sidelined in favor of automated responses.

AI in Clinical Practice

AI is fundamentally transforming allergy practice in many ways.

Documentation & Patient Education

Listening technologies such as ambient AI can transcribe conversations and format them into clinician notes, allowing allergists to spend more time with patients and reduce documentation fatigue (18). Large language models (LLMs) and augmented reality platforms also enhance disease-oriented education by providing patient-centric information. (19, 20)

Diagnostics

One area in which AI continues to evolve is in diagnostics, with machine learning (ML) models now achieve 92% accuracy in distinguishing asthma phenotypes, surpassing conventional diagnostic approaches (21). These pipelines integrate complex datasets including EHR, biomarker profiles, and environmental exposures to reveal subtle patterns not feasible through traditional methods. For penicillin allergy, artificial neural networks demonstrate predictive capability by correlating clinical history with immunological markers (8). Natural language processing (NLP) further reconciles discrepancies between documented drug allergies and true patient tolerance, reducing inappropriate antibiotic avoidance. (22, 23)

AI-powered image analysis of skin patch tests enables remote evaluation with high specificity across diverse skin phototypes (24) (25), and smartphone-based patch test readings have proven reliable (26). Smartphone microscopes can identify pollens, molds, and other particles. (27) The c-Air platform, integrated with a smartphone, can rapidly screen 6.5 L of air in 30 s and generate microscopic images with ~93% accuracy, allowing both clinicians and patients to assess environmental exposures (28). Symptom-tracking apps that link with pollen counts also help patients understand sensitivities outside the clinic. Ensemble learning models (e.g., ARF-OOBEE) outperform individual clinicians in diagnosing allergic rhinitis, particularly for complex overlapping cases (29, 30). Biosensor technologies integrating infrared spectroscopy with AI allow detection of food allergens at concentrations 100-fold below current laboratory thresholds, improving food safety monitoring (31).

Therapeutics & Monitoring

Personalized treatment algorithms represent some of the most transformative applications. Platforms like MASK-air combine real-time symptom reports, environmental data, and multi-omic profiles to optimize allergen immunotherapy (AIT) regimens. (32) (33) Wearable devices that integrate biometric monitoring with AI-driven risk models can anticipate anaphylaxis before clinical symptoms manifest (34), potentially reducing the need for double-blind placebo-controlled food challenges. Other AI-driven wearables may guide families through acute decision-making during allergic reactions.

Novel approaches such as acoustic resonance therapy, using smartphone facial scanning and personalized vibration/sound waves, offer non-pharmaceutical treatment for rhinitis and congestion (35). In parallel, DNA methylation and cell-free DNA platforms are being paired with AI for “liquid biopsies” that predict biologic outcomes, now under evaluation in allergic disease (36).

Guidelines & Ethical Considerations

As diagnostic protocols and guideline generation transition toward automated, non-invasive systems, principles of transparency, credibility, and ethics must be prioritized (37). Direct-to-consumer technologies are also reshaping how patients manage disease. Continuous glucose monitors, for example, now use AI to generate individualized “glucotypes” for dietary guidance, beyond diabetes care (38). These tools empower patients but also demand careful guideline development to ensure accuracy and protect against oversimplification.

Practice & Patient–Clinician Dynamics

AI adoption will require allergists to redefine their role. During the COVID-19 pandemic, AI and telehealth reduced in-person visits by 20%, demonstrating the potential for improved efficiency (39). Rather than reducing the need for allergists, AI will likely expand demand by identifying under-recognized populations; for example, NLP of EHRs uncovered previously undiagnosed asthma cases at Mayo Clinic (40) and identified anaphylaxis associated with H1N1 vaccination in the VAERS system (41). Other ML pipelines have successfully detected immunodeficiency patients for referral (42).

Challenges remain: “Dr. Google” has evolved into AI copilots that may anchor patients to oversimplified or incorrect ideas, for instance, a spontaneous urticaria case requesting alpha-gal testing, or an asthma patient bypassing education in favor of biologics. Clinicians will need to act as navigators, contextualizing AI outputs while preserving empathy, nuance, and the patient–doctor relationship. (43)

Black Box Issues

“Black box” issues in AI refer to situations where the internal workings of an AI system are opaque or not easily understood, even by the people using them. The black box nature of AI can represent a hurdle to fully appreciating the merits and faults of AI outputs. As with any research strategy, the inaccuracies, biases, inconsistencies, limitations, and explainability of AI should be considered (4). Explainable AI is a subfield of AI that prioritizes transparency, interpretability and explainability of outcomes.(44) A recent systematic literature review of AI in disease prediction found that the most widely used explainable AI algorithms are Shapley Additive Explanations and Local Interpretable Model-agnostic Explanations. (44) Ultimately, any AI-assisted allergy/immunology research will require validation, replication and regulatory approval prior to adoption. Collaboration of clinicians to test the real-world performance of AI in clinical practice (45) will be necessary.

The black box nature of AI also can be problematic clinically because providers are expected to make transparent, evidence-based decisions that can be explained to patients, peers, and regulatory bodies. When an AI model provides a diagnosis or treatment recommendation without a clear rationale, or if the rationale is buried in complex code or statistical algorithms, this lack of transparency undermines clinical trust. In allergy and immunology, where decisions often hinge on nuanced clinical reasoning, relying on unexplained outputs can pose real risks. (46) (47)

AI and Research

Within the Allergy and Immunology field, research and practice are intimately tied, with clinical practice bringing to the fore high priority areas for innovation, and research steadily shaping how we understand and treat allergic and immunologic conditions. In addition to transforming education, training, and clinical care, AI workflows are also likely to change how we conduct allergy/immunology research.

In a recent NIAID symposium on AI and Immunology, systems and computational immunologists, medical informaticians, bioinformaticians, and clinician-scientists in allergy and immunology highlighted that AI and ML are likely to change research in three areas: discovery, prediction, and generative processes which are likely to be of great importance when instituting personalized therapy. (4). With regards to discovery, AI can be applied to high-dimensional immune and clinical profiles to better characterize states of disease and health across ages and environments. AI has the potential to synthesize and identify patterns from large and diverse sets of molecular and tissue-level data, a process that may be facilitated by NLP and innovative high-dimensional analytic methods (4). Prediction is another area where AI can facilitate the systems-wide search and discovery of biomarkers. AI can improve the processing and integration of data toward predictive models that better capture biologic complexity, already leading to biomarkers that can predict asthma based on nasal transcript levels (49), cervical cancer based on T cell receptor expression in cervical cytology samples from routine pap smears (50).

Generative applications are another area that capitalize on AI’s strengths, including the optimization of participant selection for clinical research (51), the degree to which a study sample is representative of a population (52), and the classification of complex phenotypes (4). AI algorithms that match potential volunteers to clinical trials (53) could accelerate enrollment. Further, a digital twin, the virtual representation of a person or system that mirrors its real-world counterpart, is another emerging concept in AI that could move Allergy and Immunology research forward by potentially bypassing the time, labor, and resources needed to enroll, follow and test interventions on human participants (54). As one example, researchers used multicellular network models as a digital twin framework to identify potential drug targets for seasonal allergic rhinitis.(55) Using single cell transcriptome data generated on allergen-challenged Peripheral Blood Mononuclear Cells (PBMCs) from 16 participants with seasonal allergic rhinitis and 14 healthy matched controls, these multicellular networks modeled time-varying differential expression with in vitro allergen exposure of the PBMCs and allowed for the ranking upstream regulators of Th2 cytokine functions for potential drug targeting.(55)

AI must be used judiciously in allergy/immunology research, with privacy and informed consent respected. AI itself could address this, with LLMs now being developed to deidentify medical documents to preserve privacy (56). All data have missingness which is the phenomenon where data values are absent for certain variables or observations. This issue is prevalent across numerous fields, from healthcare and social sciences to finance and engineering, and can significantly impact the validity and reliability of analytical outcomes. Understanding the types, causes, and implications of missing data is crucial for researchers and analysts to ensure accurate and meaningful interpretations. When clinical cohorts and EHR data are involved, biases and noise due to missingness must be appreciated and appropriately considered.

Limitations of AI

As AI becomes increasingly integrated into clinical decision-making, it is essential that allergists receive training that goes beyond simply using AI tools. They must understand how these systems work, what their outputs mean, and where their limitations lie. Clinicians should be equipped to critically assess AI-generated recommendations, including understanding the data sources, the logic of the model, and the level of confidence behind its outputs. Without this foundational knowledge, there is a risk that clinicians may either over-rely on AI or, conversely, dismiss its utility altogether, missing opportunities to enhance patient care. (57) (58)

Education should also emphasize the context in which AI is used. For example, an algorithm that predicts asthma exacerbations based on medication adherence and environmental exposure data might perform well on average but could fail in cases with atypical presentations, rare comorbidities, or among certain populations. Inclusion of data across various social determinants should result in more robust results as adherence and environment may reflect factors that are not mechanistically related to disease states on their own. Clinicians must learn to recognize when AI outputs are likely to be reliable and when they should be questioned or supplemented with clinical judgment. Graduate and continuing medical education should include case-based learning and hands-on experiences with AI tools to keep pace with rapidly evolving technologies. In this way, allergists can become informed users and thoughtful stewards of AI, ensuring its integration improves care without compromising safety or patient trust. (59) (60)

Allergists often encounter patients with overlapping symptoms that span multiple systems, making diagnosis particularly challenging. Conditions like eosinophilic esophagitis (EoE) and gastroesophageal reflux disease (GERD) can present with similar symptoms such as dysphagia, food impaction, or feeding refusal in children, but differ substantially in pathophysiology, treatment, and long-term management. (61) In situations like this, AI-generated differential diagnoses or treatment suggestions must be carefully weighed against the clinician’s understanding of disease progression, physical exam findings, and nuanced clinical history to prevent unwarranted, invasive diagnostic tests and ballooning healthcare costs. (62)

Despite advances in simulated empathy, AI still struggles with true emotional intelligence, trust-building, and holistic problem-solving. Emotional intelligence requires self- and lived-experiences, which AI currently lacks. For patients, trust in healthcare is built through human connection and consistency. Without integrative thinking that considers context such as family or situational dynamics and social determinants, AI-based pattern recognition and superficial empathic responses fall short of replicating the depth of human relationships, simulating reassurance during physical exams, or providing whole-person care. Additional issues related to trustworthiness of AI, environmental considerations, and ethical issues such as consent and conflicts of interest by AI developers or companies are important educational principles for educators. (63)

Ethical and Legal Challenges

As digital technologies become increasingly embedded in allergy care, preserving patient autonomy must remain a central tenet of ethical practice. While AI-powered decision support tools may offer personalized treatment recommendations based on large datasets, patients must retain the right to make informed choices in partnership with their allergists. This requires that allergists be trained not only in interpreting algorithmic outputs but also in translating them transparently for patients. Informed consent needs to evolve to include explanations of how AI models work, what data they are trained on, and where uncertainties lie. Maintaining shared decision-making in a world of opaque algorithms will require allergists to act as mediators between technological complexity and human values. (64)

Algorithmic bias presents another critical challenge. (65) AI models used in Allergy and Immunology are trained on data that may not adequately represent all populations, leading to skewed predictions or inappropriate care for underrepresented groups. For instance, skin testing image recognition tools may perform differently on varying skin tones unless explicitly trained on diverse datasets. (66) Addressing this risk involves building more inclusive datasets, performing algorithm audits, and demanding transparency from vendors. Allergy specialists must be equipped to evaluate the fairness of AI tools and advocate for equity in clinical decision-making, especially in a specialty where social determinants of health intersect with access to biologics, immunotherapy, and diagnostic testing.

The explosion of digital health data also raises new concerns about data privacy. (67) As electronic medical records, wearable devices, and home-based monitoring systems generate continuous patient data streams, allergists must understand the cybersecurity implications of storing, transmitting, and analyzing this information. Existing frameworks like HIPAA provide a foundation, but future regulations will need to address novel risks, such as reidentification from anonymized data or unauthorized AI inference. Allergen exposure data collected from smart environments or GPS-enabled inhalers, for example, might inadvertently reveal private patterns of behavior. Training allergists to recognize these vulnerabilities, and to advocate for secure, privacy-preserving technologies, will be essential. (68)

To ensure responsible integration of these tools, regulatory frameworks must evolve alongside technology. The 2024 EU AI Act (Regulation EU 2024/1689) was an initiative to ensure the safe and ethical development and use of AI within the European Union, categorizing AI systems into risk levels (unacceptable, high, limited, and minimal). Non-compliance with the EU AI Act can lead to significant penalties, and the act is expected to influence AI development and use in other parts of the world. The FDA’s current approach to software as a medical device (SaMD) and its Digital Health Software Pre-certification Program represent important steps, but future workforce development and policies will need to be adaptive and collaborative. (69) Allergists must contribute their clinical expertise to regulatory discussions, pushing for oversight that balances innovation with accountability. Medical education should therefore emphasize not just the use of AI, but ethical leadership, preparing allergists to shape the policies and practices that will define the next era of patient-centered, technology-enhanced care.

Conclusion

The integration of AI into allergy/immunology is not a distant possibility; it is an imminent reality. As AI capabilities expand, they are likely to fundamentally alter how allergists diagnose, treat, and educate. From AI-enhanced EHRs that summarize complex patient histories to precision algorithms that guide allergen immunotherapy and biologic use, these tools offer unprecedented potential to streamline workflows and improve outcomes. Yet, realizing these benefits will require intentional redesign of medical education throughout the continuum, with a focus on AI fluency, ethical discernment, and adaptability in a rapidly evolving digital landscape. Simulation-based training, adaptive learning platforms, and interdisciplinary exposure to bioinformatics, data science, and ethics should help providers become thoughtful stewards of AI rather than passive users. It will be incumbent on us all to ensure that new technologies do not dilute the core values of medicine, such as empathy, communication, and patient-centered decision-making.

Ultimately, the future of allergy and immunology are likely to be defined not solely by the tools we adopt, but by how we choose to use them. AI will most likely challenge traditional roles, but it also offers the opportunity to deepen clinical insight, expand access, and enrich education. By embracing these innovations while upholding the human elements of care, allergists and immunologists can lead the way in shaping a future where technology enhances, not replaces, the art of medicine.

Abbreviations used in this paper:

ACGME

Accreditation Council for Graduate Medical Education

AI

Artificial Intelligence

CDSS

Clinical Decision Support System

EHR

Electronic Health Record

EoE

Eosinophilic Esophagitis

FARE

Food Allergy Research & Education

FDA

Food and Drug Administration

GERD

Gastroesophageal Reflux Disease

GME

Graduate Medical Education

GPS

Global Positioning System

HIPAA

Health Insurance Portability and Accountability Act

LLM

Large Language Model

MASK-air

Mobile Airways Sentinel Network app (MASK-air^® mHealth tool)

ML

Machine Learning

mHealth

Mobile Health

NLP

Natural Language Processing

PBMCs

Peripheral Blood Mononuclear Cells

SaMD

Software as a Medical Device

TRACER

Trainee Attributable and Automatable Care Evaluations in Real-time