ClinicalMC: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models
arXiv:2606.03157v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted in healthcare, yet they still encounter significant challenges in complex clinical decision-making scenarios. Existing benchmarks primarily assess LLM performance in single-course settings and lack systematic evaluation in multi-course scenarios, where a patient's condition evolves over time. To address this gap, we propose ClinicalMC, a benchmark for multi-course clinical decision-making. It includes 1,275 Chinese and 5,804 English samples across four stages from admission to discharge. These
The rapid adoption of LLMs in healthcare over the past few years necessitates more robust and dynamic evaluation methodologies to address increasingly complex real-world scenarios.
A benchmark like ClinicalMC is critical for advancing LLM capabilities in healthcare by focusing on multi-course patient journeys, which better reflect clinical reality and highlight current limitations.
The focus of LLM evaluation in healthcare will shift from single-point assessments to more comprehensive, longitudinal performance, pushing models to handle evolving patient data and decision flows.
- · AI healthcare researchers
- · Healthcare providers adopting AI
- · Patients receiving AI-assisted care
- · LLM developers ignoring multi-stage reasoning
- · Traditional static healthcare benchmarks
The benchmark will stimulate development of LLMs capable of more sophisticated, time-series-aware clinical reasoning.
Improved clinical decision support systems could lead to better patient outcomes and more efficient healthcare resource allocation.
The success of multi-course LLMs might accelerate the integration of AI into more complex and sensitive medical workflows, potentially redefining roles within healthcare.
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Read at arXiv cs.AI