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Operationalizing and Implementing Pretrained, Large Artificial Intelligence Linguistic Models in the US Health Care System: Outlook of Generative Pretrained Transformer 3 (GPT-3) as a Service Model
Operationalizing and implementing pretrained large AI linguistic models in the United States healthcare system: An outlook of GPT-3 as a service
Emre Sezgin;
Joseph Sirrianni;
Simon L Linwood
ABSTRACT
Generative Pre-trained Transformer (GPT) models have been popular recently with their enhanced capability and performance. In contrast to many existing Artificial Intelligence (AI) models, GPT can perform with very limited training data. GPT-3 is one of the latest releases in this pipeline, demonstrating human-like logical and intellectual responses to prompts: some examples are including writing essays, complex question answering, matching pronouns to their noun, and sentiment analysis. However, its implementation in healthcare is still a question mark in terms of operationalization and its use in clinical practice and research. In this viewpoint paper, we outlined three major operational factors that drive the adoption of GPT-3 in the United States healthcare system: (1) Health Insurance Portability and Accountability Act (HIPAA) compliance, (2) building trust with healthcare providers, and (3) establishing the broader access to the GPT-3 tools.
Citation
Please cite as:
Sezgin E, Sirrianni J, Linwood SL
Operationalizing and Implementing Pretrained, Large Artificial Intelligence Linguistic Models in the US Health Care System: Outlook of Generative Pretrained Transformer 3 (GPT-3) as a Service Model