Pai 的 AI 雷达

生信医疗

今天:EMR(Agent 原型),K-Bench(arXiv:2609)。并行还有 KREL、Route, Don't Fix、Same Patient。

日期
2026/09/17
今日条目
101
当前类目
5
#01

EMR: Self-Evolving Medical Multi-Agent System via Experience Mining and Reuse

arXiv:2609.15161v1 Announce Type: cross Abstract: Large language model (LLM) driven multi-agent systems have shown promise in complex clinical reasoning, yet existing approaches rely on static strategies and lack persistent clinical memory, preventing self-evolving from prior dia

推荐理由EMR: Self-Evolving Medical Multi-Agent:arXiv:2609.15161v1 Announce Type: cross Abstract: Large language model (LLM) driven。
模型与评测生信医疗arXiv cs.AI 官方
#02

K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations

arXiv:2609.15855v2 Announce Type: cross Abstract: People increasingly use large language models (LLMs) for mental health support, yet their safety in evolving, high-risk conversations remains poorly characterised. We developed K-Bench, a clinician-calibrated, protected benchmark

推荐理由K-Bench: a clinically calibrated:arXiv:2609.15855v2 Announce Type: cross Abstract: People increasingly use large。
模型与评测生信医疗arXiv cs.AI 官方
#03

KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs

arXiv:2608.20887v2 Announce Type: replace-cross Abstract: Automatic Medical Coding (AMC), which assigns standardized International Classification of Diseases (ICD) codes to clinical notes, is essential for medical reimbursement, quality reporting, and clinical research. Existing

推荐理由KREL: Automatic Medical Coding via:arXiv:2608.20887v2 Announce Type: replace-cross Abstract: Automatic Medical Coding。
模型与评测生信医疗arXiv cs.AI 官方
#04

Route, Don't Fix: Regime-Dependent Decoding Correction and a Trajectory-Gated Router for Reliable Clinical LLM Answer Selection

arXiv:2609.14825v1 Announce Type: cross Abstract: Large language models (LLMs) are often deemed unsafe for clinical question answering because of their tendency to hallucinate. Retrieval augmentation, fine-tuning, and external verifiers require new infrastructure that clinical go

推荐理由Route, Don't Fix: Regime-Dependent:arXiv:2609.14825v1 Announce Type: cross Abstract: Large language models (LLMs) are。
模型与评测生信医疗arXiv cs.AI 官方
#05

Same Patient, Different Order: Action-Level Reliability of Clinical LLM Agents Under Repeated Runs

arXiv:2609.13582v1 Announce Type: cross Abstract: A clinical agent benchmark can report the same verdict on identical inputs while the agent files a materially different order on each run. Such agents order tests, request medications and place referrals, yet benchmarks typically

推荐理由Same Patient, Different Order:arXiv:2609.13582v1 Announce Type: cross Abstract: A clinical agent benchmark can report。
模型与评测生信医疗arXiv cs.AI 官方