A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research-A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee.

Artificial and augmented intelligence (AI) and machine learning (ML) methods are expanding into the health care space. Big data are increasingly used in patient care applications, diagnostics, and treatment decisions in allergy and immunology. How these technologies will be evaluated, approved, and assessed for their impact is an important consideration for researchers and practitioners alike. With the potential of ML, deep learning, natural language processing, and other assistive methods to redefine health care usage, a scaffold for the impact of AI technology on research and patient care in allergy and immunology is needed. An American Academy of Asthma Allergy and Immunology Health Information Technology and Education subcommittee workgroup was convened to perform a scoping review of AI within health care as well as the specialty of allergy and immunology to address impacts on allergy and immunology practice and research as well as potential challenges including education, AI governance, ethical and equity considerations, and potential opportunities for the specialty. There are numerous potential clinical applications of AI in allergy and immunology that range from disease diagnosis to multidimensional data reduction in electronic health records or immunologic datasets. For appropriate application and interpretation of AI, specialists should be involved in the design, validation, and implementation of AI in allergy and immunology. Challenges include incorporation of data science and bioinformatics into training of future allergists-immunologists.

A new species under genus Sergentomyia and subgenus Sergentomyia (Diptera: Psychodidae) from Puducherry Union Territory, Southern India, with keys to the species.

Sergentomyia (Sergentomyia) pondicherriensis is a new species of phlebotomine sand fly belonging to the genus Sergentomyia and subgenus Sergentomyia is described with illustrations of adult females and males. Other six species under the subgenus Sergentomyia are Se. (Ser.) punjabensis (Sinton), Se. (Ser.) dentata (Sinton), Se. (Ser.) theodori (Parrot), Se. (Ser.) murgabiensis (Perfiliew), Se. (Ser.) mervynae Pringle, and Se. (Ser.) fallax afghanica Artemiev. Sergentomyia pondicherriensis sp. nov was collected from termite mounds, in one of the villages of Puducherry Union Territory, Southern India. A revised key to the species of the subgenus Sergentomyia is also included.

Artificial intelligence and the hunt for immunological disorders.

PURPOSE OF REVIEW: Artificial intelligence has pervasively transformed many industries and is beginning to shape medical practice. New use cases are being identified in subspecialty domains of medicine and, in particular, application of artificial intelligence has found its way to the practice of allergy-immunology. Here, we summarize recent developments, emerging applications and obstacles to realizing full potential. RECENT FINDINGS: Artificial/augmented intelligence and machine learning are being used to reduce dimensional complexity, understand cellular interactions and advance vaccine work in the basic sciences. In genomics, bioinformatic methods are critical for variant calling and classification. For clinical work, artificial intelligence is enabling disease detection, risk profiling and decision support. These approaches are just beginning to have impact upon the field of clinical immunology and much opportunity exists for further advancement. SUMMARY: This review highlights use of computational methods for analysis of large datasets across the spectrum of research and clinical care for patients with immunological disorders. Here, we discuss how big data methods are presently being used across the field clinical immunology.