arXiv:2510. 21084v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown strong potential for clinical decision support through their advanced language understanding and reasoning capabilities.
arXiv:2606. 28692v1 Announce Type: new Abstract: Treatment reasoning underpins every therapeutic decision, integrating disease context, comorbidities, medications, contraindications, and evolving biomedical knowledge to select an appropriate therapy.
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...
DeepTCM1.0 is a multi‑expert AI agent built on the DeepSeek V3.2 large language model, designed to integrate classical traditional Chinese medicine (TCM) theory with modern life sciences for mechanistic analysis of TCM compound formulas. The framework employs a three‑tier collaborative architecture and a three‑round iterative quality‑control workflow, simulating 11 interdisciplinary intelligent agents. It was validated on the Guizhi Decoction, with performance evaluated through double‑blind five‑dimensional scoring, ICC reliability testing, Mann‑Whitney U tests, and effect size analysis across 100 independent assessments by four large language models.
arXiv:2603.08166v2 Announce Type: replace Abstract: Automated Drug Combination Extraction (DCE) from large-scale biomedical literature is crucial for advancing precision medicine and pharmacological...
arXiv:2610.14474v1 Announce Type: cross Abstract: Medication recommendations seek to deliver personalized drug combinations based on patients' clinical information, where ensuring transparent decisio...
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:2608.22622v1 Announce Type: cross Abstract: Clinical decision-making relies on identifying relevant patient information to guide diagnosis and treatment, a challenge that is especially difficul...
The paper introduces a four‑stage framework—SFT, PG‑CoT, Dynamic, and K‑RL—to improve large language models for Traditional Chinese Medicine prescription generation. It addresses three key gaps: lack of auditable reasoning (SR Gap), failure to adjust prescriptions over time (LA Gap), and non‑enforcement of absolute contraindication rules (SC Gap). Experiments on 12 fine‑tuned models and 6 zero‑shot baselines show that the framework, particularly a 7B Mistral model, outperforms zero‑shot GPT‑5 on all three TCM evaluation metrics.
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:2609.06779v1 Announce Type: cross Abstract: Drug repurposing aims to identify new therapeutic uses for existing compounds and, compared with de novo drug discovery, offers a faster and more cos...
CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models proposes a new framework to improve medical reasoning in LLMs. It introduces two key conditions—Causal Sufficiency and Proximal Learnability—to curate high-quality training trajectories, using agreement-based self-verification and dynamic entropy bounds. Experiments on medical multimodal and text-only benchmarks show that CARE outperforms competitors, reducing incorrect reasoning and enhancing training stability.