Key Points Summary

• Three practical exercises for analyzing blood biomarker reports in Alzheimer's disease research
• Exercise 1 focuses on translating biomarker information for different healthcare stakeholders
• Exercise 2 involves creating structured summary tables of biomarker characteristics
• Exercise 3 examines limitations, biases, and confounding factors in biomarker studies
• Emphasis on clear communication and critical analysis of biomarker data

The reports generated through AI platforms like ChatGPT and Perplexity serve as powerful deep research tools, offering comprehensive analysis. These AI-assisted reports can efficiently compile and organize complex scientific data, providing structured summaries that maintain accuracy while being adaptable for different audience needs. The AI's ability to process and reframe technical information helps bridge the gap between raw research data and practical clinical applications.

While these AI tools enhance research efficiency, it's important to note that they should be used as assistive technologies, complementing rather than replacing human expertise in interpreting and validating scientific findings. The reports they generate can serve as valuable starting points for deeper analysis and discussion of biomarker research in Alzheimer's disease.

blood biomarkers for early Alzheimer’s, Jan 2024–May 2025

Shared Content

Exercise 1: “Explain Like I’m a Clinician”

Task:

Prompt the LLM to rephrase the biomarker report for specific users:

• Primary care physician
• Clinical trial coordinator
• Concerned patient asking about blood tests for AD

Instruction:

Ask for short paragraphs explaining what blood biomarker testing is, what tests are available now, and what they tell us — in clear, professional but simplified language.

Exercise 2: Create a Structured Summary Table