Artificial intelligence (AI) is increasingly utilized in healthcare to help doctors and patients make better health-care decisions. We utilize AI applications for clinical decision making and clinical decision support (CDS). Mostly focused in diagnostic imaging, we develop applications utilizing rule-based and machine learning algorithms. These include applications in predictive modeling and natural language processing that are integrated into CDS systems and evidence-based imaging initiatives. We also utilize machine learning for augmenting evidence that is disseminated to the medical community. Ultimately, the goal is to enhance CDS systems that are available in clinical practice using evidence-based medicine.
We address challenges that relate to applying AI into medical practice include improving the quality of the data that we feed into AI systems, developing ways to evaluate whether an AI system performs adequately for specific tasks, and ensuring minimal workflow disruption for providers. Machine learning using supervised algorithms are developed and evaluated.
- Use of Machine Learning to Identify Follow-Up Recommendations in Radiology Reports.
- Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry.
Traditional AI models are developed and utilized in several studies for risk stratification and prediction.
- Risk Stratification Model: Lower-Extremity Ultrasonography for Hospitalized Patients with Suspected Deep Vein Thrombosis.
- A Clinical Model for the Early Diagnosis of Acute Pancreatitis in the Emergency Department.