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Dr. Emily Tsai is a board certified radiologist with subspecialty training in thoracic imaging and image-guided procedures. Her clinical focus is on diseases affecting the lungs and airways, including cancer, interstitial lung disease, COPD, and infection. Her research focuses on quality improvement and patient outcomes. Recent projects include assessment of incidental findings and cost-effectiveness of CT screening for lung cancer, as well as application of clinical tools and machine learning to improve workflow and triage of emergent studies.
Lung cancer screeningClinical applications of machine learningComparative effectiveness researchImage-guided biopsy and intervention
Clinical Validation of Machine Learning Triage of Chest Radiographs
Artificial intelligence and machine learning have the potential to transform the practice of
radiology, but real-world application of machine learning algorithms in clinical settings has
been limited. An area in which machine learning could be applied to radiology is through the
prioritization of unread studies in a radiologist's worklist. This project proposes a
framework for integration and clinical validation of a machine learning algorithm that can
accurately distinguish between normal and abnormal chest radiographs. Machine learning triage
will be compared with traditional methods of study triage in a prospective controlled
clinical trial. The investigators hypothesize that machine learning classification and
prioritization of studies will result in quicker interpretation of abnormal studies. This has
the potential to reduce time to initiation of appropriate clinical management in patients
with critical findings. This project aims to provide a thoughtful and reproducible framework
for bringing machine learning into clinical practice, potentially benefiting other areas of
radiology and medicine more broadly.
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