Comprehensive Disclaimer Appendix for AutoML Assistant

1. Accuracy & Verification: AutoML Assistant’s responses are based on learned patterns and may be inaccurate or incomplete. All results must be independently verified by the user with domain expertise before use in scientific conclusions or clinical decisions. AutoML Assistant is for preliminary exploration and model prototyping only.

2. Data Context & Scope: Complex prompts or terminology may yield unexpected results. AutoML Assistant's output is limited strictly to indexed data. AutoML Assistant is a research tool only and is not for clinical diagnosis or patient care. AutoML Assistant uses conversations as context for the LLM but does not use them for model training or fine-tuning. Conversation history is stored within DNAnexus and accessible only in the project where the user is running the AutoML Assistant interface.

3. User Responsibility: You are fully responsible for the proper use and interpretation of AutoML Assistant's results. Developers are not liable for consequences from misuse or misinterpretation.

4. Non-Deterministic Results: Users should be aware that AutoML Assistant may provide different responses to the same query and that LLM-generated summaries are probabilistic, not absolute.

5. Verification Requirement: All outputs, including statistical interpretations and gene-trait associations, must be verified by a qualified professional using primary data sources or raw code.

6. Limited Context: AutoML Assistant analyzes data based on the provided parameters and cannot account for biological variables or metadata not explicitly uploaded or integrated.

7. This tool is for Research Use Only (RUO). AutoML Assistant is not a medical device and has not been cleared by the FDA or any other regulatory body for clinical diagnostics or treatment decisions.

8. Not Medical Advice: AutoML Assistant does not provide medical diagnoses, treatment recommendations, or prognostic assessments.

9. Experimental Nature: Insights generated (e.g., pathway enrichment or biomarker identification) are hypotheses for further experimental validation, not established biological facts.

10. Database Latency: AutoML Assistant leverages a generic LLM that is trained using an internal knowledge base that has a "cutoff date" and may not reflect the most recent publications or updated genomic assemblies (e.g., GRCh38 vs. CHM13).

11. Computational Accuracy: While AutoML Assistant can generate code to analyze data, the user is responsible for ensuring the underlying statistical tests (e.g., p-values, Fold Change, or q-values) are appropriate for their specific experimental design.

12. Users are strictly prohibited from uploading, entering, or otherwise providing Personally Identifiable Information (PII) or Protected Health Information (PHI) as defined by HIPAA, GDPR, or applicable local laws. This AutoML Assistant is intended solely for the analysis of fully de-identified, pseudonymized, or synthetic research data.