Gaokerena, a novel family of compact Persian medical language models optimized for deployment on consumer grade hardware, and Gaokerena-R, a novel family of compact Persian medical language models optimized for deployment on consumer grade hardware, are presented.
Abstract
The integration of artificial intelligence into medical question-answering systems has advanced rapidly; however, research remains predominantly focused on English, leaving low resource languages like Persian significantly underserved. To address this gap, this paper introduces Gaokerena, a novel family of compact Persian medical language models optimized for deployment on consumer grade hardware. As a foundational step toward localized digital healthcare, we first present Gaokerena-V, developed by training a baseline model on a newly curated 90-million-token Persian medical corpus and 20,000 expert-vetted physician Q&A pairs, which improved performance on a translated medical MMLU benchmark from 46.28% to 49.31%. Second, recognizing the critical demands of clinical reasoning, we developed Gaokerena-R by integrating a Chain-of-Thought approach with two novel Reinforcement Learning with AI Feedback (RLAIF) frameworks to optimize preference-based reasoning. Despite utilizing the same baseline architecture and a smaller dataset than Gaokerena-V, Gaokerena-R achieved a superior benchmark score of 52.98%. Furthermore, both models are equipped with custom-developed uncertainty heads that predict the model's confidence in its responses based solely on internal hidden states. While these results demonstrate significant progress in Persian medical language modeling and proactive safety estimation, current performance levels remain insufficient for direct clinical application, highlighting the necessity for further research into robust knowledge acquisition and rigorous safety verification prior to real world deployment.
Background: Large language models (LLMs) show considerable promise for medical question answering and reasoning. Their use in medical microbiology, however, remains constrained by limited domain-specific knowledge and the risk of hallucinated outputs. Objective: To develop and evaluate Smart Medical Microbiology (SmartMM), a specialized LLM for accurate, reliable, and context-aware responses in medical microbiology. Methods: SmartMM integrates domain-adaptive continual pretraining, supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), knowledge distillation, and retrieval-augmented generation (RAG). We constructed a high-quality microbiology corpus from textbooks, clinical guidelines, the scientific literature, case reports, and other authoritative sources. Model performance was assessed using objective examinations, subjective generation tasks, expert review, and real-world user preference evaluation. Results: SmartMM achieved accuracies of 0.897 and 0.563 on true-or-false and fill-in-the-blank questions, respectively. In subjective generation tasks, it obtained the highest ROUGE-L score (0.265) and BERTScore F1 score (0.771) among all compared models. Expert assessment showed excellent inter-rater reliability, with all ICC(C,3) values exceeding 0.970. In a user evaluation involving 20 participants and 100 real-world questions, SmartMM received the largest number of first-place rankings (33), placing it among the top-performing systems overall. Conclusions: SmartMM showed strong domain adaptability in medical microbiology knowledge organization, semantic generation, and retrieval-augmented reasoning. These findings support its potential use in educational support, infectious disease knowledge assistance, and retrieval-enhanced medical question answering.
Yongqian Gong, Ruiqiang Ma, Xicheng Wang et al.· Applied Informatics· 0 citations
Developing artificial intelligence capable of clinical language comprehension and reliable diagnostic reasoning has remained a core challenge in biomedical engineering. While Large Language Models (LLMs) demonstrate significant potential in general natural language processing tasks, their direct application in the medical domain is severely constrained by parametric hallucinations and data silos. This paper introduces an end-to-end, resource-efficient, multilingual speech-driven Question-Answering (QA) framework optimized for localized clinical support. To accommodate deployment on consumer-grade execution environments, we implement Parameter-Efficient Fine-Tuning (PEFT) using Low-Rank Adaptation (LoRA) and 4-bit Quantized LoRA (QLoRA) configurations across open-source 3B and 7B parameter architectures. Human preference alignment is enforced via a stateful Reinforcement Learning with Human Feedback (RLHF) loop applying Proximal Policy Optimization (PPO). Crucially, to mitigate the vulnerabilities of passive information retrieval, we introduce an Active Validation Loop powered by Corrective Retrieval-Augmented Generation (CRAG). This validation engine is decoupled from the model harness using the Model Context Protocol (MCP), standardizing asynchronous lookups across dense vector repositories, clinical guidelines, and real-time electronic health registries.
Misha Patel· International Journal of Sci...· 0 citations
Across medical benchmarks, MedLLM shows a pattern visible only at sub-billion scale: medical competence does not degrade uniformly under compression but splits by task type and dissociation is masked at 7B, where both capabilities are present, and surfaces only when capacity is scarce.
M. R. Rahman, Asim Ahmed, Mihan Mohagheghzadeh et al.· 0 citations
A dual-view approach that connects clinical practice with computational methods is presented, establishing a five-level competency scheme following Miller’s Pyramid and linking deductive, inductive, and abductive reasoning patterns to common medical goals and tasks.
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
An engineering-oriented, end-to-end roadmap that structures the full lifecycle of clinical language model systems—from model design and domain adaptation to optimization and real-world evaluation is introduced.