General Statement of Purpose
The scientific and lay press constantly present breaking news on the promise of artificial intelligence to improve the diagnosis and management of myriad health conditions. This activity is comprised of three presentations focusing on distinct areas in AI applications: 1) deep learning for classifying cervical cytology slide images; 2) automatically aligning biomedical ontologies; and 3) predicting adverse drug reactions (ADRs) using multisite health data. Collectively, these innovations have potential for improving patient outcomes and research methods by enhancing efficiency, promoting interoperability, and predicting adverse events to reduce patient morbidity and mortality. The presentations were included on the AMIA 2019 Annual Symposium agenda.
Target audience: Biomedical informatics professionals with an interest in artificial intelligence and its potential application to clinical practice
PhD Student, Missouri University of Science and Technology
PhD Candidate, The University of Texas at Dallas
Postdoctoral Researcher, IBM Research
After participating in this CME internet enduring material, the learner should be better able to:
- Weigh the challenges in applying deep learning to real-world cervical cytology data to improve accuracy in the diagnosis of cervical cancer
- Consider the effect of a machine learning training paradigm and bootstrapping for ontology alignment
- Weigh the features of a federated learning framework with other methods used to predict ADRs for patients whose records are in electronic health record systems
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.
Credit Designation Statement
The American Medical Informatics Association designates this enduring material for a maximum of 1.25 AMA PRA Category 1 credits. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
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