Gaokerena is a family of compact Persian medical language models designed for consumer‑grade hardware. The first variant, Gaokerena‑V, was trained on a 90‑million‑token Persian medical corpus and 20,000 physician Q&A pairs, raising a translated medical MMLU benchmark score from 46.28% to 49.31%. A second variant, Gaokerena‑R, adds a Chain‑of‑Thought approach and two Reinforcement Learning with AI Feedback frameworks, achieving a higher benchmark score of 52.98% while also providing uncertainty estimates based on internal hidden states.
arXiv:2609.24480v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic reasoning: the conv...
arXiv:2505. 14107v5 Announce Type: replace-cross Abstract: The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing various scientific challenges, including those arising in complex clinical scenarios.
MMTClinic is a new benchmark that tests large language models on complex reasoning and question‑answering tasks involving clinical time‑series data. It combines text, medical images, and multivariate physiological signals to create 30,000 QA pairs—including 15,000 multiple‑choice and 15,000 open‑ended questions—in five languages (English, Hindi, Bengali, Marathi, and Tamil). The benchmark covers mortality prediction, heart‑rate forecasting, and SOFA score estimation, and evaluates 13 state‑of‑the‑art LLMs across zero‑shot, few‑shot, and chain‑of‑thought settings, revealing significant performance gaps across tasks, languages, and modalities.
The paper introduces AyurParam-2.9B, a bilingual language model fine‑tuned for Ayurveda using a curated dataset of classical texts and clinical guidance in English and Hindi. It outperforms other open‑source instruction‑tuned models of similar size on the BhashaBench‑Ayur benchmark and rivals larger models in performance. The work demonstrates that authentic domain adaptation and high‑quality supervision are essential for reliable, culturally congruent AI in specialized medical fields.
arXiv:2507. 02983v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) hold significant promise for transforming digital health by enabling automated medical question answering.
arXiv:2606. 13572v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown promising reasoning capabilities in general domains, yet their performance remains limited in specialized settings such as healthcare, especially in multilingual and low-resource scenarios.
Multimodal Large Language Models (MLLMs) have shown promising reasoning capabilities in general domains, yet their performance remains limited in specialized settings such as healthcare, especially in multilingual and low-resource scenarios. This gap is critical in regions like rural India, where patients often express complex medical queries in native Indic languages and rely on multimodal inputs such as medical images.
arXiv:2610.34871v1 Announce Type: new Abstract: Objective: The rapid advancement of large language models (LLMs) in healthcare has opened new possibilities for AI-assisted clinical decision-making; h...
PetQA is a Korean long‑form question‑answering benchmark designed to assess veterinary knowledge and clinical reasoning in large language and vision‑language models. It comprises 10,076 text‑only and 8,751 multimodal QA pairs about dogs and cats, with expert veterinarian answers, and a test split called PetQA‑Bench that includes question type and clinical condition annotations. The study evaluates 18 models across zero‑shot, retrieval‑augmented generation, and supervised fine‑tuning settings using ROUGE, BERTScore, and LLM‑as‑a‑judge metrics, revealing current models’ strengths and limitations and underscoring the need for better adaptation methods for clinically reliable veterinary AI; translated versions in five languages are also provided.
arXiv:2608. 12138v1 Announce Type: cross 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.
arXiv:2610.14705v1 Announce Type: cross Abstract: Large language models have advanced medical reasoning, but clinically grounded reasoning in traditional Chinese medicine (TCM) remains challenging be...
arXiv:2512. 01241v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized.