Overall, SimpliMed—and particularly its efficient hybrid re-ranking methodology— shows meaningful progress toward producing accurate, readable, and clinically reliable medical text simplifications.
Abstract
Barriers to communication and understanding across health domains remain a critical issue, particularly when complex medical terminology is involved. Advances in large language models offer opportunities to improve accessibility through automatic text simplification. However, challenges persist regarding readability, accuracy, and semantic fidelity—limitations that traditional ATS methods have struggled to overcome. This paper presents SimpliMed, a multi-phase medical text-simplification framework built using the Simple Digestive Cancer corpus. The study evaluates baseline model performance using sequencing and causal architectures, applies supervised fine-tuning to BART-base and T5-small, and experiments with various prompting strategies. Building on these insights, we design a hybrid construction pipeline inspired by Reinforcement Learning from AI Feedback (RAHF). The hybrid system employs an 8-bit quantized BART-Large-CNN model, enhanced by dictionary-based preprocessing and candidate re-ranking guided by a heuristic reward function that balances semantic fidelity (BERTScore), readability (FKGL), and structural overlap (ROUGE-L). Automated evaluations and re-ranking experiments demonstrate the effectiveness of this hybrid approach, achieving strong performance (SARI: 61.63, BERT-F1: 0.95, ROUGE-L: 0.67) while maintaining high readability (FKGL: 6.47).Additional confirmation of this is provided by 31 participants, with 45.2% of subjects clearly preferring the hybrid output over the baseline and 41.9% finding the hybrid output to be equally suited to the human reference. In addition, four consulting physicians confirmed the clinical suitability of the simplified texts. Overall, SimpliMed—and particularly its efficient hybrid re-ranking methodology—shows meaningful progress toward producing accurate, readable, and clinically reliable medical text simplifications.
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.
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
The proposed framework offers a practical and scalable approach to mitigating hallucinations without requiring task-specific fine-tuning, highlighting the potential of retrieval-augmented approaches for trustworthy artificial intelligence (AI)-assisted healthcare applications.
Pathology Foundation Models (PFMs) offer powerful Whole Slide Image (WSI) representations but suffer from massive computational costs. While Knowledge Distillation (KD) can create efficient student models, existing multi-teacher methods often use suboptimal uniform weighting that ignores tissue heterogeneity. We propose LaGuadia (Language-Guided Adaptive DistillAtion), a framework that develops a compact pathology image encoder by dynamically integrating expertise from multiple PFMs under clinical linguistic guidance. Our approach utilizes a multi-stage pipeline: first, extracting visually observable clinical keywords from pathology reports; second, aligning visual features with these keywords via a Vision-Language meta-teacher (MedSigLIP) to provide dense semantic guidance; and finally, performing adaptive KD where teacher contributions are weighted based on their semantic alignment with the clinical narrative. Experiments on WSI captioning, visual question answering, and slide-level classification tasks demonstrate that an 87M parameter LaGuadia student model matches or exceeds foundation-scale models such as GigaPath and UNI, achieving strong factual consistency and robust generalization. These results highlight clinical language as an effective semantic anchor for building efficient and reliable digital pathology systems. Code is available at https://github.com/hvcl/LaGuadia.
While vision-language models dominate medical representation learning, unstructured text lacks the dense, quantitative diagnostic phenotypes inherent in structured clinical tables. However, existing multimodal pre-training methods underutilize this potential due to semantic-agnostic designs that treat tabular inputs as flat vectors and employ unstable continuous regression objectives. To overcome this, we propose a novel semantic-aware framework explicitly modeling the intrinsic two-dimensional structure of tabular data. First, addressing the inter-feature hierarchy of varying diagnostic importance, we introduce Importance-Aware Adaptive Masking to construct a label-free curriculum prioritizing salient features. Second, addressing the intra-feature continuity-discreteness duality, we propose a Soft-Label Discretized Module that replaces unstable numerical regression with stable distribution matching, thereby mathematically preserving ordinal relationships. Extensive experiments across large-scale dermatology (SLICE-3D, HOP) and ophthalmology (EyePACS) datasets establish a new state-of-the-art (SOTA), demonstrating exceptional robustness and cross-domain generalizability.
Yingsheng Liu, Haiming Li, Jing Zhu et al.· 0 citations
Named Entity Recognition (NER) in specialized domains like biomedicine suffers from acute data scarcity, requiring expensive expert annotations. While data augmentation offers a promising solution, it inevitably introduces noisy and mislabeled samples that can degrade model performance. This problem is amplified in few-shot scenarios where every training example matters. We introduce PALAUNER (Policy-based Active Learning to Augment Named Entity Recognition), a reinforcement learning framework that learns to select high-quality samples from augmented data pools. Using a deep Q-network, our agent evaluates samples based on content features and model predictions, deciding which examples will improve NER performance. Experiments across five BioNER benchmarks demonstrate that PALAUNER consistently enhances diverse augmentation methods, from simple perturbations to GPT-based generation. Average F1 improvements are of 0.5−7.1 points in few-shot settings. PALAUNER’s modular design enables seamless integration with emerging augmentation techniques, providing a generalizable solution for training data quality enhancement. We publicly release our code on GitHub: (https://github.com/picuslab/palauner).
M. Postiglione, Andrea Vignali, Giancarlo Sperlí et al.· Data mining and knowledge di...· 0 citations