Can I Take Another Dose? Evaluating LLM Decision-Making Under Temporal Uncertainty in OTC Dosing QA
arXiv:2606.04262v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for everyday health questions, including whether a user can safely take another dose of an over-the-counter (OTC) medication. Yet this common safety-relevant setting remains underexplored in existing medical QA evaluations, where correct answers require tracking dose timing, computing rolling 24-hour intake, following product-label constraints, and handling incomplete medication histories. We introduce DOSEBENCH, a focused benchmark of 81 curated OTC dosing scenarios focused on adult acetaminop
The proliferation of LLMs into everyday advisory roles necessitates rigorous evaluation of their safety in sensitive domains like health, which is currently underexplored.
Evaluating LLM decision-making in critical health scenarios directly impacts public safety and the trustworthiness of AI in sensitive applications.
This introduction of DOSEBENCH establishes a specific benchmark for evaluating LLMs' ability to handle temporal uncertainty and complex constraints in medical advice, revealing current limitations.
- · AI Safety Researchers
- · Healthcare Tech Companies focused on robust AI
- · Pharmaceuticals
- · LLM Developers overlooking safety protocols
- · Consumers relying on unchecked AI health advice
Identification of specific failure modes for LLMs in medical dosing will lead to targeted improvements in model architectures and training data.
Increased regulatory scrutiny and development of certification standards for AI systems providing health advice, especially those with public-facing applications.
Enhanced public confidence in AI-driven health tools will depend significantly on demonstrated reliability in safety-critical tasks, influencing broad adoption.
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Read at arXiv cs.AI