Possible or Definite? A Benchmark for Evaluating Diagnostic Uncertainty Preservation in Clinical Text

2026-06-16 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Health & Medical Research, Natural Language Processing · Depth: Advanced, quick

Summary

A new benchmark, "Possible or Definite?", evaluates how large language models (LLMs) preserve diagnostic uncertainty in clinical text, a critical aspect often overlooked by standard evaluation metrics. Researchers constructed a benchmark comprising 1,200 clinical documents with 9,184 uncertainty annotations categorized across five distinct levels. An evaluation of three LLMs on this benchmark revealed significant limitations: LLMs preserved original uncertainty cues poorly, often less than half the time, and struggled particularly with nuanced distinctions between adjacent uncertainty levels. This work highlights a crucial failure mode for LLMs in clinical applications, impacting safe deployment in healthcare workflows where precise communication of diagnostic certainty directly guides patient care decisions.

Key takeaway

For AI Scientists and Research Scientists developing or deploying LLMs in clinical settings, you must prioritize evaluating diagnostic uncertainty preservation. Standard fluency and coherence metrics are insufficient; your models may be altering critical clinical meaning by misrepresenting certainty levels. Implement specialized benchmarks like "Possible or Definite?" to ensure safe and accurate communication of diagnostic information, mitigating risks in patient care decisions.

Key insights

Large language models poorly preserve diagnostic uncertainty in clinical text, posing a significant safety risk.

Principles

• LLMs preserve original uncertainty cues less than half the time.
• LLMs struggle with nuanced distinctions between adjacent uncertainty levels.
• Standard evaluation metrics do not capture this failure mode.

Method

A benchmark of 1,200 clinical documents with 9,184 uncertainty annotations across five levels was constructed, then three LLMs were evaluated for uncertainty preservation.

In practice

• Integrate uncertainty preservation metrics into LLM clinical evaluations.
• Develop LLMs specifically trained on diagnostic uncertainty cues.

Topics

• Large Language Models
• Clinical Text
• Diagnostic Uncertainty
• AI Safety
• Benchmarking
• Healthcare AI

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.