The study demonstrates that instruction‑tuning open‑weight large language models (LLMs) with over 500,000 public question‑answer pairs derived from ICD‑10‑GM, ICD‑O‑3, and OPS catalogues markedly improves ICD coding accuracy for German tumor diagnoses. After fine‑tuning, ICD‑10‑GM exact accuracy rose from 1.4–24 % to 41–58 %, and partial accuracy from 31–74 % to 73–83 %. The approach also eliminated malformed code outputs and achieved 99 % tumor‑diagnosis recognition, with performance scaling positively with model size but narrowing the gap between small and large models.
arXiv:2608. 03854v1 Announce Type: new Abstract: When decoder language models are used as classifiers, predicted class probabilities depend on implementation choices, including the prompt template, verbalizer (label-to-token mapping), and scoring rule, that are rarely treated as experimental variables.
arXiv:2608. 02615v1 Announce Type: cross Abstract: Cancer diagnosis and characterization require integrating complementary evidence from radiology, pathology, genomics, and clinical metadata.
arXiv:2608.30021v1 Announce Type: cross Abstract: Errors in radiology reports can adversely affect patient treatment, yet automated report quality assurance remains challenging because errors are oft...
The paper evaluates whether a 9‑billion parameter decision foundation model, Clef‑Flash, is necessary for symptom‑to‑disease classification. Fine‑tuning the model on the 24‑class Symptom2Disease benchmark yields 95.42% accuracy, but simple TF‑IDF linear classifiers achieve higher accuracy (98.75%) with far faster inference. Ablation studies show that predictions can hinge on single descriptive clauses, indicating that the benchmark may not fully capture the model’s capabilities.
arXiv:2610.03829v1 Announce Type: cross Abstract: Real-world outpatient oncology notes contain specialised terminology, tumour staging expressions, treatment names, toxicity descriptions, and institu...
arXiv:2606. 19183v1 Announce Type: cross Abstract: Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to prompts, information order, and plausible but incorrect outputs.
arXiv:2607. 08602v1 Announce Type: new Abstract: Hepatocellular carcinoma (HCC) is a common malignancy and a leading cause of cancer-related mortality.
arXiv:2606. 28798v1 Announce Type: new Abstract: Objective: ICD codes are central to reimbursement, research, and population health surveillance, yet automated coding systems often struggle to integrate diagnostic signals from both clinical narratives and structured electronic health record (EHR) variables.
arXiv:2608.03854v4 Announce Type: replace Abstract: Quantized large language models can run on consumer hardware, which motivates interest in on-premises processing of sensitive data. The reliability...
This study benchmarks transformer models for Bangla medical named entity recognition (NER), comparing BanglaBERT, multilingual BERT (mBERT), XLM‑RoBERTa, and GPT‑4o mini under zero‑shot and few‑shot prompting. Across a full test set of 3,179 samples, fine‑tuned XLM‑RoBERTa achieves a new state‑of‑the‑art F1‑score of 0.5959, while BanglaBERT lags with 0.4937, suggesting that domain diversity outweighs language specificity. The analysis shows high performance on Medicine and Specialist entities (F1 > 0.83) but lower accuracy on Symptoms (F1 0.4367), and demonstrates that fine‑tuned transformers outperform prompt‑only approaches by a factor of 3.76.
The paper introduces Labadain-MedQA, a Tetun medical question‑answering dataset created by translating MedAlpaca’s Medical Flashcards and WikiDoc Patient Information into Tetun with Gemini 2.5 Flash, yielding 39,077 QA pairs. Translation quality was evaluated on 50 random pairs, showing human translations scored higher than LLM‑generated ones for medical accuracy and fluency (4.43/5 vs. 4.02/5, p < 0.001). Fine‑tuning Gemma and Llama models with parameter‑efficient methods on this dataset improved performance on Tetun‑translated USMLE multiple‑choice questions by up to 10.79% relative accuracy.
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...