Current opinion in pulmonary medicine · 2024
Leveraging AI technology in sarcoidosis.
СтатьяОбзорПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Premjee Akiff, Li Lawrence, Garikapati Srilakashmi, Sarpong Kwabena Nketiah, Morgenthau Adam S
Аннотация
PURPOSE OF REVIEW: Sarcoidosis 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 FINDINGS: The 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.
SUMMARY: The 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.
RECENT FINDINGS: The 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.
SUMMARY: The 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.