ABSTRACT
The application of artificial intelligence (AI) to healthcare has garnered significant enthusiasm in recent years. Despite the adoption of new analytic approaches, medical education on AI is lacking. We aim to create a usable AI primer for medical education. We discuss how to generate a clinical question involving AI, what data are suitable for AI research, how to prepare a dataset for training and how to determine if the output has clinical utility. To illustrate this process, we focused on an example of how medical imaging is employed in designing a machine learning model. Our proposed medical education curriculum addresses AI's potential and limitations for enhancing clinicians' skills in research, applied statistics and care delivery.
The application of artificial intelligence (AI) to healthcare has generated increasing interest in recent years; however, medical education on AI is lacking. With this primer, we provide an overview on how to understand AI, gain exposure to machine learning (ML) and how to develop research questions utilizing ML. Using an example of a ML application in imaging, we provide a practical approach to understanding and executing a ML analysis. Our proposed medical education curriculum provides a framework for healthcare education which we hope will propel healthcare institutions to implement ML laboratories and training environments and improve access to this transformative paradigm.
Subject(s)
Artificial Intelligence , Education, Medical , Delivery of Health Care , Humans , Machine LearningABSTRACT
CGP results from >60,000 cases were screened to identify NTRK fusion events from cases of neuroendocrine tumors. 2417 NET patients from diverse anatomic sites were identified. From this dataset, six cases harbored NTRK fusions which included intra- and inter-chromosomal translocations. A NTRK fusion frequency of approximately 0.3% was found across all subtypes of NETs. Three cases involved translocations of NTRK1 with unique fusion partners (GPATCH4, PIP5K1A, CCDC19). Co-occurring alterations occurred in five cases. NTRK alterations were identified in nearly the full spectrum of NETs, including from the small intestine, pancreas, lung, and others. With the late stage clinical development of NTRK TKIs (including entrectinib and larotrectinib), these findings may further inform targeted approaches to therapy in NET.
