Large Language Models and Generative AI in Hospital Medicine-Slides

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This document provides a comprehensive overview of Large Language Models (LLMs) and their applications in healthcare. It begins by explaining machine learning (ML) concepts, highlighting that LLMs are a type of ML model designed to predict words in sequences using deep neural networks called transformers, introduced in 2017. It describes how LLMs convert language into numerical tokens and employ attention mechanisms for contextual understanding.<br /><br />The document covers effective ways to prompt LLMs, including few-shot and chain-of-thought prompting, which enable LLMs to perform reasoning tasks and understand complex medical data. For example, it demonstrates summarizing extensive patient lab results—such as complete blood count, metabolic panels, and coagulation profiles—into concise, clinically relevant tables that assist diagnosis and treatment evaluation.<br /><br />In addressing concerns, the document highlights common challenges in using LLMs for high-risk healthcare applications. Hallucinations refer to confidently generated but factually incorrect responses, posing risks in clinical decision-making. Mitigation strategies include augmenting LLM outputs with external databases, knowledge graphs, and retrieval-augmented generation.<br /><br />It also discusses social biases embedded in AI models due to training data reflecting societal disparities, which can lead to missed diagnoses or biased personnel decisions. Environmental concerns emphasize the significant carbon footprint of training and deploying LLMs, with emerging regulatory frameworks—such as California’s climate-related data laws—aimed at accountability.<br /><br />Data security is another critical concern, particularly protecting patient health information (PHI) via encryption, compliance certifications (e.g., HITRUST), and secure data handling agreements. The document highlights potential solutions like deploying medically trained LLMs and integrating them as decision support, documentation assistants, and clinical note generators.<br /><br />Finally, real-world medical LLM applications include aiding diagnosis in challenging cases, reducing bias, decision support alerts, information retrieval, and clinical documentation, underscoring the promising but cautious integration of LLMs in healthcare.

Keywords

Large Language Models

Healthcare Applications

Machine Learning

Transformers

Prompting Techniques

Medical Data Summarization

Hallucinations in AI

Bias in AI Models

Data Security in Healthcare

Environmental Impact of AI