AOCOPM 2024 Midyear Educational Conference-346719

346719 - Article 11

Pdf Summary

The document is a presentation on the integration and implications of large language models (LLMs) and artificial intelligence (AI) in occupational medicine, presented by David O. Shumway, DO. It emphasizes the rapid changes AI brings to healthcare, highlighting its potential to enhance clinical and administrative practices. The document defines AI as the simulation of human-like cognitive functions in machines, enabling them to perform tasks such as learning, reasoning, problem-solving, and language processing.<br /><br />Historically, AI has been utilized in healthcare for electronic health records (EHR), decision support systems, and more recently, in imaging, radiology, echocardiography, and endoscopy to improve diagnostic accuracy. Key AI technologies include machine learning, deep learning, natural language processing (NLP), and optical character recognition (OCR).<br /><br />The document outlines different types of LLMs, such as GPT-Lineage LLMs and Google’s PaLM-Lineage, discussing their applications and how they are trained. It lists various AI tools and platforms used in practice, such as Chat-GPT, Microsoft Copilot, and "Ask SAGE,” an AI tool used by the Department of Defense (DOD).<br /><br />AI's applications in medicine today extend to roles like medical scribes, diagnostic assistants, and administrative support, with the potential to streamline processes such as documentation, scheduling, billing, and patient correspondence. Future possibilities for AI in healthcare include full-scope medical practice management, enhanced remote patient monitoring, and real-time video analysis for standardized assessments.<br /><br />The presentation encourages medical practitioners to embrace AI technology, experiment with its applications, and stay informed about ongoing research, with an emphasis on safe, responsible use and validation of AI outputs.

Keywords

large language models

artificial intelligence

occupational medicine

David O. Shumway

healthcare innovation

machine learning

natural language processing

medical applications

AI tools

responsible AI use