Possible or Definite? A Benchmark for Evaluating Diagnostic Uncertainty Preservation in Clinical Text
arXiv:2606.18471v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for clinical text tasks such as summarization and revision. While most studies evaluate the fluency and coherence of LLM-generated text, whether LLMs correctly preserve diagnostic uncertainty remains underexplored. In clinical practice, phrases such as ``possible pneumonia'' communicate the strength of available evidence and directly guide decisions about follow-up testing and treatment. Altering these uncertainty expressions can change the clinical meaning entirely. In this paper, we systematica
The increasing deployment of large language models in sensitive applications like healthcare necessitates rigorous evaluation of their safety and reliability, particularly in preserving nuanced information.
Incorrect preservation of diagnostic uncertainty in clinical text by LLMs could lead to significant patient harm through misdiagnosis or inappropriate treatment decisions.
This benchmark provides a critical tool for medical AI developers and healthcare providers to specifically assess and mitigate risks related to how LLMs handle crucial uncertainty expressions in clinical contexts.
- · AI ethicists
- · Healthcare providers
- · Patients
- · Medical AI developers who prioritize safety
- · AI developers lacking robust evaluation frameworks
- · Generative AI models with poor uncertainty preservation
Increased focus on 'explainable AI' and 'responsible AI' within clinical LLM development.
New regulatory guidelines specific to AI models in medical diagnosis, emphasizing accuracy in uncertainty communication.
Shift in medical liability discussions to include AI model performance in preserving diagnostic nuances.
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