Education and Training
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.
Stanford is currently not accepting patients for this trial.
Stanford Investigator(s):
Intervention(s):
- other: Traditional workflow triage
- other: Machine learning workflow triage
- other: Random workflow triage
Eligibility
Inclusion Criteria:
- Radiologist at Stanford Hospital and Clinics
Exclusion Criteria:
- None
Ages Eligible for Study
18 Years - N/A
Genders Eligible for Study
All
Not currently accepting new patients for this trial
Contact Information
Stanford University
School of Medicine
300 Pasteur Drive
Stanford,
CA
94305
Not Recruiting