VITA's advantages in accuracy and completeness persisted under the neutral judge; its communication scores were lower, and this results indicate that a purpose-built clinical RAG system remains competitive with frontier LLMs on an open benchmark, consistent with corpus specificity as a design variable that improves grounding at some cost to communication polish.
Abstract
General-purpose large language models (LLMs) have recently been reported to match or exceed specialized clinical AI tools on medical benchmarks, but such comparisons draw on a narrow set of systems and on benchmarks developed largely in high-income settings. We evaluate VITA, a retrieval-augmented generation (RAG) system purpose-built for contextual knowledge retrieval in India and other low- and middle-income (LMIC) settings. VITA retrieves from a curated corpus of disease-specific guidelines, India-specific antimicrobial resistance data, national formulary constraints, and resource-limited care protocols; its architecture and corpus are proprietary, but the benchmark, the physician-written rubrics, and our full response and scoring outputs are public for independent verification. On 4,023 English-language HealthBench questions (80.5% of the benchmark), scored with a GPT-4.1 judge, VITA ranked first with 51.9% of possible rubric points, ahead of GPT-5.4 (46.1%), o4-mini (44.3%), Gemini 3.1 Pro (42.6%), and Claude Sonnet 4.6 (37.3%), and scored highest on 45.4% of questions. To test robustness to newer models and judge lineage, a 500-question subset was re-run against current-generation models (GPT-5.5, Claude Opus 4.8, Gemini 3.5 Pro, Grok 4.3) and graded by a neutral open-weight judge (DeepSeek-V4-Pro) sharing no lineage with any system tested. Here the gap narrowed to parity: VITA and GPT-5.5 were statistically indistinguishable on mean per-question score, while VITA led on points-weighted score and won the most questions. VITA's advantages in accuracy and completeness persisted under the neutral judge; its communication scores were lower. These results indicate that a purpose-built clinical RAG system remains competitive with frontier LLMs on an open benchmark, consistent with corpus specificity as a design variable that improves grounding at some cost to communication polish.
A novel benchmark comprising over 9,000 real-world, point-of-care, multilingual, and multimodal clinical question-answer pairs sourced from frontline health workers in Nigeria reveals several critical insights into the suitability of LLMs as clinical decision support systems in low-resource contexts.
Tobi Olatunji, C. Aka, C. Okocha et al.· medRxiv· 0 citations
Abstract Objectives Clinical practice guidelines are a cornerstone of evidence-based medicine, yet their implementation in routine care remains inconsistent. Large language models (LLMs), particularly with Retrieval-Augmented Generation (RAG), have shown strong performance in medical question answering, but their ability to use knowledge from German-language guidelines has not been systematically evaluated due to a lack of a dedicated benchmark. We therefore developed such a benchmark and evaluated guideline-based question answering with different LLMs and retriever configurations. Methods We developed cpgQA-DE, an expert-validated benchmark dataset of 200 multiple-choice questions derived from 10 current German clinical practice guidelines across five specialties. All questions were reviewed for correctness, relevance and complexity. The dataset includes case-based and knowledge-based questions with metadata on guideline source, specialty, relevance and difficulty. We evaluated LLM performance using a RAG-based pipeline built on a corpus of German guidelines, comparing multiple model–retriever combinations. Results RAG integration substantially improved accuracy across all tested models and for most retrievers. The best-performing configuration, GPT-5 combined with the multilingual-e5-large retriever, achieved an accuracy of 95%. Notably, the open-weight model gpt-oss-120b reached 90% accuracy when used with RAG. Conclusions cpgQA-DE enables systematic and reproducible offline evaluation of guideline-aware question answering systems in the German healthcare context. The observed performance gains with RAG support the use of LLMs augmented with quality-assured external knowledge. Such systems may help bridge the evidence-practice gap by providing guideline-based recommendations at the point of care, while strong performance of open-weight models suggests potential for on-premises deployment in privacy-sensitive clinical environments.
Johannes Schwietering, G. Lichtner· BMJ Health & Care Informatic...· 0 citations
It is suggested that domain-adapted encoder models may be preferable for similar structured clinical NER settings, although larger and externally validated benchmarks are needed before generalizing to other languages, clinical corpora, model families, or deployment environments.
L. Elvas, Carolina Carvalho· Scientific Reports· 0 citations
Objectives The rapid evolution of large language models (LLMs) and their growing application in clinical text processing have created an urgent need for reliable de-identification mechanisms. While LLMs show promise in identifying sensitive health information (SHI), their capabilities require rigorous evaluation. This study aims to conduct a comprehensive benchmarking analysis of various LLM-based, traditional rule-based and hybrid de-identification methods. Methods Our benchmark analysis used five datasets (i2b2-2006, MIMIC-2008, i2b2-2014, i2b2-2016 and OpenDeID v1) from different countries. We developed three baseline and eight LLM-based models. The experimental setup encompassed nine different settings using various combinations of training and testing sets to assess model robustness and cross-dataset performance. Results In the baseline models, the approach trained on the combined corpus of all five datasets (setting 3) significantly outperformed the other settings, achieving a strict F1 micro-average score of 0.8172. Regarding LLM-based models, the supervised fine-tuning approach using the same combined configuration (setting 9) achieved the highest performance with a strict F1 score of 0.9447. Discussion The harmonisation of corpora ensured standardised data formatting and SHI management across five diverse datasets, highlighting the necessity for uniform categorisation to enhance the reliability of de-identification results. Conclusions Our findings indicate that while fine-tuned LLMs offer superior accuracy, the observed performance variability across heterogeneous electronic health record sources poses significant technical challenges. Real-world implementation must address these inconsistencies to overcome the ethical and technical hurdles associated with deploying LLMs for handling sensitive health data.
O. Panchal, Nai-Wen Chang, Zixiang Zhao et al.· BMJ Health & Care Informatic...· 0 citations
ObjectiveLarge language models (LLMs) have the potential to revolutionize how population-level health research is conducted by automatically abstracting data that would otherwise be unavailable. However, few results are available on real clinical notes. We developed an LLM leaderboard showing how open-source LLMs perform at entity extraction on unseen clinical notes.
ApproachEMR data, including free-text notes, were linked to a chart-review cohort comprising 10,659 adults admitted to a hospital in Calgary, Canada, between 2017 and 2022, with data on comorbidities. We then attempted to replicate this chart review with multiple open-source LLMs in a secure computing environment. Chart review results served as the reference standard.
ResultsThere was a wide variation in performance among the examined LLMs: the smallest, Llama 3.2 3B, had a high mean sensitivity of 0.97 but low PPV at 0.4; the largest, Llama-3-70B, showed a high mean sensitivity of 0.96 and greatly improved PPV of 0.7; in-between these in size, phi 4 demonstrated a more balanced performance with a mean sensitivity of 0.81 and PPV of 0.77. However, the results varied considerably across conditions, with quirks specific to each model.
ConclusionsLLMs are already available that can perform entity extraction well enough to be considered in place of some administrative data. With rapid developments in the field, a leaderboard based on real clinical data is vital for informing researchers on best practices for integrating the latest AI techniques into their data practices.
E. Martin, Seungwon Lee, K. Riazi et al.· International Journal of Pop...· 0 citations
Extracting structured data from electronic health records (EHRs) remains a major challenge, particularly in non-English and resource-constrained healthcare systems. This study benchmarks multiple large language models (LLMs) for the automated extraction of structured clinical variables from Portuguese-language medical notes under limited computational resources. We evaluated five LLMs (GPT-4o mini, DeepSeek-V3, Mixtral-8x7B, LLaMA 8B, and Qwen-32B) against a manually curated dataset of cardiology and infectiology outpatient records. Models were deployed in quantized versions to optimize computational efficiency. Outputs were compared with human annotations using F1 score, balanced accuracy, and recall. Among the tested models, Qwen-32B achieved the highest performance in both the infectiology domain (balanced accuracy = 0.91 [0.07]) and cardiology domain (balanced accuracy = 0.89 [0.07]). Performance varied by clinical variable, with better results for frequently and consistently documented conditions (e.g., diabetes) and lower accuracy for complex or infrequent variables (e.g., tumors). Extraction time ranged from 0.9 to 24.2 minutes per patient, depending on clinical domain and model. These findings support the feasibility of applying LLM-based natural language processing tools in resource-limited, non-English healthcare settings. Future research should assess emerging high-parameter models and explore additional clinical domains.
Breno Gabriel Araújo Sampaio de Jesus, Tomaz Castrillon Figueiredo, Clariele de Almeida Pereira et al.· Cadernos de Saúde Pública· 1 citation