arXiv AI June 18, 2026 By Soheyl Bateni, Maryam Abdolali

Language Models as Interfaces, Not Oracles: A Hybrid LLM-ML System for Pediatric Appendicitis

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 2

LLMs for Cardiovascular Risk Prediction from Structured Clinical Data

arXiv:2606. 00031v1 Announce Type: cross Abstract: Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to support early diagnosis and risk assessment.

By Jeba Maliha, Md Rafiul Kabir

llms

arXiv AI

2d ago

Domain-Adapted Small Language Models for Reliable Clinical Triage

arXiv:2604.26766v2 Announce Type: replace-cross Abstract: Accurate and consistent Emergency Severity Index (ESI) assignment remains a persistent challenge in emergency departments, where highly varia...

By Manar Aljohani, Brandon Ho, Kenneth McKinley, Dennis Ren, Xuan Wang

llms fine-tuning safety

arXiv Computation and Language

Aug 27

MTDiag: A Multi-Turn Diagnostic Dataset Towards Clinically Meaningful LLM Evaluation

MTDiag is a newly released multi-turn diagnostic dialogue dataset designed to evaluate large language models (LLMs) in clinically meaningful ways. It is built from DDXPlus, MIMIC-IV, and AJCR case reports, covering both common emergency department presentations and rare conditions, and normalizes cases into a canonical schema using UMLS concept identifiers and ICD-10 codes. The dataset includes a UserLM‑8B utterance‑generation pipeline and physician‑validated natural‑language utterances, and introduces clinical knowledge‑grounded metrics that go beyond simple diagnostic accuracy for multi‑turn differential diagnosis tasks.

By Pia Chouayfati, Alexander M. Fichtl, Miriam Ansch\"utz, George Doumat, Georg Groh

llms agents benchmarks

arXiv Machine Learning

Sep 15

Knowledge-Enriched Structured EHR Features for 30-Day Hospital Readmission Prediction on MIMIC-IV

arXiv:2609.15713v1 Announce Type: new Abstract: Recent approaches to 30-day hospital readmission prediction rely on pre-trained language models applied to discharge summaries. Although these methods...

By Mohamad Najafi, Hongyun Fu, Mathias Brochhausen, Jian Wu, Yaohang Li

llms rag safety

arXiv AI

Jul 20

Large Language Models as Unified Multimodal Learners for Clinical Prediction

arXiv:2607. 15380v1 Announce Type: cross Abstract: Electronic health records combine free-text clinical narratives with structured measurements such as vital signs, laboratory values, and comorbidities.

By Ajay Madhavan Ravichandran, Bilgin Osmandoja, Klemens Budde, Klaus Netter, Tobias Strapatsas, Aljoscha Burchardt, Sebastian M\"oller, Roland Roller

llms fine-tuning multimodal

arXiv AI

Jun 30

Primary ICD Category Prediction using LLM-based Probing

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.

By Chengyuan Liu, Xinyue Zhang, Yao Li, Guanting Chen

llms rag multimodal

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Related stories

LLMs for Cardiovascular Risk Prediction from Structured Clinical Data

Domain-Adapted Small Language Models for Reliable Clinical Triage

MTDiag: A Multi-Turn Diagnostic Dataset Towards Clinically Meaningful LLM Evaluation

Knowledge-Enriched Structured EHR Features for 30-Day Hospital Readmission Prediction on MIMIC-IV

Large Language Models as Unified Multimodal Learners for Clinical Prediction

Primary ICD Category Prediction using LLM-based Probing