Large language models (LLMs) are increasingly being adopted in clinical practice, e.g., in medical diagnostic systems. However, their reliability is still a concern, as LLMs are prone to hallucinations and could lead to misdiagnosis, inappropriate treatment decisions, and potential harm to patients. To mitigate these risks, LLM-based medical diagnostic systems should incorporate safety guardrails capable of detecting suspicious predictions and flagging them for human review or further clinical assessment. In this work, we employ uncertainty quantification (UQ) as a safety mechanism within an enterprise diagnostic system. We evaluate 16 UQ methods and propose two task-specific modifications tailored to the medical diagnostic workflow. First, we show that methods based on semantic similarity between generated samples substantially benefit from replacing a general-purpose NLI model with a domain-specific model. Second, we adapt a supervised UQ method that relies on LLM internal states for the detection of erroneous generation from a black-box enterprise LLM accessible only through a text-generation API. Our approach leverages the internal states of an open-source, lightweight proxy white-box model. We demonstrate that this approach outperforms all other UQ methods in the considered setting.

This study investigates the feasibility of automating clinical coding in Russian, a language with limited biomedical resources. We present a new dataset for ICD coding, which includes diagnosis fields from electronic health records (EHRs) annotated with over 10,000 entities and more than 1,500 unique ICD codes. This dataset serves as a benchmark for several state-of-the-art models, including BERT, LLaMA with LoRA, and RAG, with additional experiments examining transfer learning across domains (from PubMed abstracts to medical diagnosis) and terminologies (from UMLS concepts to ICD codes). We then apply the best-performing model to label an in-house EHR dataset containing patient histories from 2017 to 2021. Our experiments, conducted on a carefully curated test set, demonstrate that training with the automated predicted codes leads to a significant improvement in accuracy compared to manually annotated data from physicians. We believe our findings offer valuable insights into the potential for automating clinical coding in resource-limited languages like Russian, which could enhance clinical efficiency and data accuracy in these contexts. Our code and dataset are available at https://github.com/auto-icd-coding/ruccod.