Artificial intelligence and the hunt for immunological disorders

Abstract

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.

Figure 1:. Machine Learning Examples

Classic machine learning approaches include unsupervised methods such as clustering algorithms (A) which find natural relationships between the data. Additionally, supervised learning via linear models(B), decision trees (C) and support vector machines (D) use labeled data to train models for regression or classification. Deep learning methods such as neural networks (E) pass inputs to subsequent layers where learning occurs at each node. Prediction error can be minimized by passing information backwards through a cost function before feeding forward for ultimate output classification. (Network model example constructed with http://alexlenail.me/NN-SVG/index.html)

Figure 2:. Paradigm for a Data Pipeline Enabling CDS & Patient Decision-making

An example data pipeline schema originating from the clinician-patient interaction. Data entered into the EHR flows into a database/data warehouse where it can be structured, undergo analysis (asterisk) and transformation to information. This information can be fed back to clinicians to inform subsequent encounters (CDS-clinical decision support) or it may enable patient guidance about personal healthcare decisions as provided through the EHR patient portal. Reused with permission from [10].