TY - JOUR
T1 - Leveraging AI technology in sarcoidosis
AU - Premjee, Akiff
AU - Li, Lawrence
AU - Garikapati, Srilakashmi
AU - Sarpong, Kwabena Nketiah
AU - Morgenthau, Adam S.
N1 - Publisher Copyright:
© 2024 Lippincott Williams and Wilkins. All rights reserved.
PY - 2024
Y1 - 2024
N2 - Purpose of reviewSarcoidosis is a systemic, granulomatous disease of uncertain cause. Diagnosis may be difficult, prognosis uncertain and response to treatment unpredictable. The application of artificial intelligence to sarcoidosis may provide clinical decision support for these challenges. This review will provide an overview of current and potential future applications of artificial intelligence in sarcoidosis.Recent findingsThe predominant application of artificial intelligence in sarcoidosis is imaging. Imaging models may differentiate sarcoidosis from other pulmonary disorders. Models, which predict survival and identify key factors relevant to prognosis are also available. The application of cluster analysis to organize sarcoidosis patients into developmental phenotypes is underway. Machine learning algorithms to evaluate the treatment response of sarcoidosis patients do not yet exist but similar models may evaluate patients with other inflammatory disease. The potential applications of artificial intelligence to sarcoidosis is vast, but there are practical limitations that warrant consideration. These include: the accessibility of data, biases in data, cost and privacy.SummaryThe application of artificial intelligence in medicine is still in its early stages but models are poised to support the diagnostic and prognostic challenges in sarcoidosis patients. The predictive power of these artificial intelligence is likely to come from combining various models, trained on content-rich datasets from phenotypically heterogeneous sarcoidosis patients.
AB - Purpose of reviewSarcoidosis is a systemic, granulomatous disease of uncertain cause. Diagnosis may be difficult, prognosis uncertain and response to treatment unpredictable. The application of artificial intelligence to sarcoidosis may provide clinical decision support for these challenges. This review will provide an overview of current and potential future applications of artificial intelligence in sarcoidosis.Recent findingsThe predominant application of artificial intelligence in sarcoidosis is imaging. Imaging models may differentiate sarcoidosis from other pulmonary disorders. Models, which predict survival and identify key factors relevant to prognosis are also available. The application of cluster analysis to organize sarcoidosis patients into developmental phenotypes is underway. Machine learning algorithms to evaluate the treatment response of sarcoidosis patients do not yet exist but similar models may evaluate patients with other inflammatory disease. The potential applications of artificial intelligence to sarcoidosis is vast, but there are practical limitations that warrant consideration. These include: the accessibility of data, biases in data, cost and privacy.SummaryThe application of artificial intelligence in medicine is still in its early stages but models are poised to support the diagnostic and prognostic challenges in sarcoidosis patients. The predictive power of these artificial intelligence is likely to come from combining various models, trained on content-rich datasets from phenotypically heterogeneous sarcoidosis patients.
KW - artificial intelligence
KW - machine learning
KW - natural language processing
KW - recurrent neural network
UR - http://www.scopus.com/inward/record.url?scp=85198980236&partnerID=8YFLogxK
U2 - 10.1097/MCP.0000000000001085
DO - 10.1097/MCP.0000000000001085
M3 - Review article
C2 - 38989774
AN - SCOPUS:85198980236
SN - 1070-5287
VL - 30
SP - 570
EP - 575
JO - Current Opinion in Pulmonary Medicine
JF - Current Opinion in Pulmonary Medicine
IS - 5
ER -