arXiv:2606.08769v1 Announce Type: cross Abstract: Automatic evaluation is critical for high-stakes text generation, where errors often involve omitted findings, hallucinated content, polarity reversals, location changes, uncertainty mismatches, and temporal-comparison errors rather than low surface similarity alone. Radiology report generation provides a challenging test case because generated reports must preserve structured clinical evidence across sources. We present RadOT-Eval, an interpretable structured-evidence optimal transport framework for offline auditing of radiology report generat
The increasing sophistication of generative AI models for high-stakes text generation, particularly in medical fields, necessitates more robust and auditable evaluation frameworks to ensure safety and accuracy.
This development addresses a critical challenge in AI adoption for sensitive domains, by providing an interpretable method to evaluate the fidelity and reliability of AI-generated content in medical contexts, directly impacting trustworthiness and utility.
The introduction of RadOT-Eval provides a new, auditable standard for evaluating AI in radiology, moving beyond surface-level metrics to assess structured clinical evidence, which could improve adoption and reduce risks.
- · AI developers in healthcare
- · Healthcare providers
- · Patients
- · Medical AI auditing firms
- · AI models without robust evaluation methods
- · Developers prioritizing speed over accuracy and interpretability
Improved reliability and safety of AI-generated radiology reports.
Increased trust and adoption of AI systems in clinical decision-making processes.
The methodology could be generalized to other high-stakes text generation tasks, accelerating AI integration into other regulated industries.
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Read at arXiv cs.AI