Leveraging simulation to provide a practical framework for estimating the novel scope of risk of large language models in healthcare - Report - MDSpire
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Leveraging simulation to provide a practical framework for estimating the novel scope of risk of large language models in healthcare
Clinical Report: Utilizing Simulation to Assess Risks of Large Language Models
Overview
This study demonstrates a simulation-based methodology for assessing risks associated with large language models as software medical devices (LLM-SaMD). It highlights the variability in model performance across different safety tasks.
Background
Large language models (LLMs) are increasingly integrated into healthcare, but their probabilistic outputs can lead to significant patient safety concerns. Existing medical device risk management frameworks may not fully address the unique risks posed by LLMs.
Data Highlights
Task
P1 Range
P2 Range
Suicidal Ideation
1.1×10⁻⁸ to 1.6×10⁻⁴
4.9×10⁻⁵ to 5.1×10⁻³
Therapy Request
Varied
Varied
Therapy-like Interaction Detection
Varied
Varied
Key Findings
Fourteen open-source LLMs were evaluated on three safety-classification tasks.
Model performance improved with size, particularly in generating neutral and non-therapeutic content.
Frequent errors were noted in detecting suicidal ideation and therapy-like interactions.
Estimated probabilities (P1 and P2) for hazards varied significantly across tasks.
Simulation can link model failure modes to pathways of harm, aiding in risk assessment.
Clinical Implications
Simulation-based risk estimation offers a method for evaluating the safety of LLM-SaMD in various clinical contexts.
Conclusion
Simulation can help address the challenges posed by LLMs in healthcare.
Systematic review of 8 observational studies found limited evidence on associations between prenatal asthma-medication exposure and neurodevelopmental outcomes, with autism spectrum disorder the only outcome suitable for meta-analysis.
