| Topic | Content |
| Demystifying LLMs and AI in Healthcare | - AI’s Potential: Concisely present AI’s impact on diagnosis, treatment, research, and streamlining workflow.
- What are LLMs? Explain LLMs in simple terms, how they process language, and their ability to generate different text outputs.
- LLM Basics:
- Tokens: How LLMs break down language into meaningful units.
- Prompt Engineering: The art of crafting effective instructions for LLMs.
- APIs (briefly): How to connect with LLMs via platforms.
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| AI for Streamlining Clinical & Administrative Workflow | - Clinical Documentation: Using LLMs for summarizing patient records, generating reports, and automating notes.
- Triage and Pre-screening: Demonstrate how LLMs can assist in initial symptom assessment and patient inquiries.
- Medical Coding: LLMs for streamlining ICD-10 coding and billing processes.
- Administrative Tasks:
- Roster Creation: LLMs to assist in scheduling based on availability and constraints.
- Clinical Document Summarization & Analysis: LLMs to condense reports, extract insights, and create summaries.
- Working with statistical data (e.g. clinical census): Using LLMs to conduct data analysis and generate valuable insights from it.
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| LLMs for Enhanced Decision-Making | - Diagnosis Support: LLMs to access relevant medical literature, suggest differential diagnoses, and analyze medical images
- Treatment Recommendations: Generating personalized treatment plans with medication suggestions and dosage calculations.
- Clinical Research: Using LLMs to find relevant studies, support literature review, and synthesize research findings.
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| AI-Powered Learning for Clinicians | - Knowledge Retrieval: Using LLMs to quickly access accurate medical information, guidelines, and the latest research.
- Personalized Revision: Generating practice quizzes, summaries of key concepts, or differential diagnosis comparisons based on speciality.
- Keeping Up-to-Date: LLMs to identify and summarize new developments in the field, relevant to the clinician’s area of practice.
- Other use cases for learning purposes.
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| Responsible AI and Considerations | - Data Privacy and Security: Emphasize ethical data use and patient confidentiality.
- Bias and Limitations: Discuss how LLMs can reflect biases and their limitations in replacing clinical judgment.
- The Future with AI: Share the potential for responsible AI integration into healthcare practices.
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| Q&A and Next Steps | Ask away! |
