Papers with medical applications
Comparing the Intrinsic Performance of Clinical Concept Embeddings by Their Field of Medicine (D19-62)
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| Challenge: | Existing work has trained medical embeddings to rep-resent medical concepts using specific medical data. |
| Approach: | They use intrinsic methods to evaluate pre-trained word embeddings from the various fields of medicine as defined by their ICD-9 systems. |
| Outcome: | The results show that the embeddings perform better in one field of medicine than in other fields. |
MedOdyssey: A Medical Domain Benchmark for Long Context Evaluation Up to 200K Tokens (2025.findings-naacl)
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| Challenge: | Existing benchmarks in the generic domain have evaluated long-context capabilities for LLMs. |
| Approach: | They propose a medical long-context benchmark with seven length levels ranging from 4K to 200K tokens. |
| Outcome: | The proposed benchmarks have seven length levels ranging from 4K to 200K tokens. |
An Industry Evaluation of Embedding-based Entity Alignment (2020.coling-industry)
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| Challenge: | Knowledge graphs (KGs) are increasingly important in various applications such as question answering and search engines. |
| Approach: | They propose to use a supervised learning environment with unbiased seed mappings for training and validation to evaluate alignment methods in an industrial context. |
| Outcome: | The proposed methods are evaluated in an industrial context and are compared with DBpedia and Wikidata benchmarks. |
Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study (2024.eacl-short)
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| Challenge: | pharmacovigilance event extraction is a key field of healthcare that involves identifying, evaluating, understanding, and preventing adverse effects. |
| Approach: | They investigate the ability of large language models (LLMs) to extract adverse events from medical text. |
| Outcome: | The proposed model performs reasonably well with demonstration selection strategies, but falls short compared to fully fine-tuned small models. |
MedEthicEval: Evaluating Large Language Models Based on Chinese Medical Ethics (2025.naacl-industry)
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| Challenge: | Large language models (LLMs) have been used in clinical decision support, medical education and patient communication. |
| Approach: | They propose a benchmark to evaluate large language models in the domain of medical ethics and assess their grasp of medical ethical principles and their application across diverse scenarios. |
| Outcome: | The proposed framework assesses the models’ grasp of medical ethics principles and their ability to apply them across diverse scenarios. |
MedRiskEval: Medical Risk Evaluation Benchmark of Language Models, On the Importance of User Perspectives in Healthcare Settings (2026.eacl-industry)
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Jean-Philippe Corbeil, Minseon Kim, Maxime Griot, Sheela Agarwal, Alessandro Sordoni, Francois Beaulieu, Paul Vozila
| Challenge: | Existing risk evaluations focused on general safety benchmarks, resulting in role-dependent vulnerabilities in real-world medical and clinical deployments. |
| Approach: | They propose a patient-oriented dataset called PatientSafetyBench that evaluates a variety of open- and closed-source LLMs. |
| Outcome: | The proposed benchmark examines medical risks from 466 open- and closed-source LLMs across 5 risk categories. |
Eliciting Medical Reasoning with Knowledge-enhanced Data Synthesis: A Semi-Supervised Reinforcement Learning Approach (2026.findings-acl)
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| Challenge: | Existing methods to enhance medical reasoning lack high-quality data. |
| Approach: | They propose a medical knowledge-enhanced data Synthesis and Semi-supervised Reinforcement learning framework that uses rare disease knowledge to synthesize distribution-controllable reasoning questions. |
| Outcome: | The proposed method outperforms existing methods across ten medical benchmarks and achieves up to 5.93% gain on rare diseases tasks. |
LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation (2025.findings-emnlp)
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Ming Zhang, Yujiong Shen, Zelin Li, Huayu Sha, Binze Hu, Yuhui Wang, Chenhao Huang, Shichun Liu, Jingqi Tong, Changhao Jiang, Mingxu Chai, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Current medical benchmarks have limitations in question design, data sources and evaluation methods. |
| Approach: | They propose a new benchmark covering five core medical areas . it includes 2,996 questions created from real-world electronic health records . |
| Outcome: | The proposed model covers five core medical areas and includes 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios. |
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications? (2024.findings-emnlp)
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Yanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach, Timothy Miller, Danielle Bitterman, Matthew Churpek, Majid Afshar
| Challenge: | Numerical data is pivotal for medical questions and answers, but tabular data is not fully integrated into LLMs. |
| Approach: | They examine the effectiveness of vector representations from last hidden states of LLMs for medical diagnostics and prognostics using electronic health record data. |
| Outcome: | The proposed representations outperform those using raw numerical EHR data in medical diagnostics and prognostics. |
MMXU: A Multi-Modal and Multi-X-ray Understanding Dataset for Disease Progression (2025.findings-acl)
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| Challenge: | Existing datasets and models fail to consider critical aspects of medical diagnostics, authors argue . MMXU enables multi-image questions incorporating both current and historical patient data. |
| Approach: | They propose a dataset for MedVQA that focuses on identifying changes in specific regions between two patient visits. |
| Outcome: | The proposed dataset improves diagnostic accuracy by 20% by integrating historical data. |
VPL: Visual Proxy Learning Framework for Zero-Shot Medical Image Diagnosis (2024.findings-emnlp)
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| Challenge: | Insufficient medical text precision and the modal disparity between text and vision spaces pose challenges for vision-language models like CLIP. |
| Approach: | They propose a visual proxy learning framework that combines a text refinement module and a stable Sinkhorn algorithm to enhance the diagnostic performance. |
| Outcome: | The proposed model outperforms the state-of-the-art CLIP inference by 1.69% to 15.31% on five datasets covering various diseases. |
Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? (2024.emnlp-main)
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| Challenge: | Several studies claim that domain-adaptive pretraining improves performance on downstream medical tasks. |
| Approach: | They compare medical LLMs and VLMs against their corresponding base models . they find that medical Lms outperform their base models in 12.1% of cases . |
| Outcome: | The proposed models outperform their base models on medical questions and tasks in 12.1% of cases and reach a tie in 49.8% of cases. |
Scaling is Not All You Need: Clinical-Oriented Reinforcement Learning Makes Parameter-Efficient Clinical Reasoning (2026.findings-acl)
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| Challenge: | Large language models are increasingly used in medicine, but expert-level clinical reasoning remains a high-complexity, high-stakes frontier. |
| Approach: | They propose to train clinical reasoning models using a Reasoning-Oriented Data Strategy based on topological synthesis and CoT cold-start. |
| Outcome: | The proposed pipeline outperforms existing models and outperformed the strongest open-source alternatives up to 671B in MedXpertQA. |
M3Retrieve: Benchmarking Multimodal Retrieval for Medicine (2025.emnlp-main)
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| Challenge: | Strong retrieval models are increasingly important in knowledge-intensive domains. |
| Approach: | They propose a benchmark to evaluate multimodal retrieval models in medical settings . they examine 1.2 million text documents and 164K multimodal queries . |
| Outcome: | The proposed model spans 5 domains,16 medical fields, and 4 distinct tasks with over 1.2 Million text documents and 164K multimodal queries. |
Italian Word Embeddings for the Medical Domain (2024.lrec-main)
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| Challenge: | Neural word embeddings have proven valuable in the development of medical applications, but for the Italian language, there are no publicly available corpora, embedds, or evaluation resources tailored to this domain. |
| Approach: | They propose to use a corpus of medical texts to generate neural word embeddings in Italian using Metathesaurus concept graphs. |
| Outcome: | The results show that the new embeddings correlate well with human judgments regarding similarity and relatedness of medical concepts. |
Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation (2026.acl-long)
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Peiru Yang, Haoran Zheng, Tong Ju, Shiting Wang, Wanchun Ni, Jiajun Liu, Shangguang Wang, Yongfeng Huang, Tao Qi
| Challenge: | Existing studies have investigated knowledge poisoning attacks in medical RAG systems . knowledge poison attacks can disrupt model outputs and undermine system reliability . |
| Approach: | They propose a knowledge poisoning framework that injects misinformation into textual data . they propose to use paired visual data as a query-agnostic trigger to promote retrieval . |
| Outcome: | The proposed framework produces clinically plausible but incorrect generations on five LLMs and datasets. |
FactKG: Fact Verification via Reasoning on Knowledge Graphs (2023.acl-long)
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| Challenge: | knowledge graphs (KGs) have not been fully utilized as a knowledge source for fact verification. |
| Approach: | They propose a dataset to enable the community to better use knowledge graphs . they propose 108k natural language claims with five types of reasoning . |
| Outcome: | The proposed dataset consists of 108k natural language claims with five types of reasoning . authors believe the proposed method can advance reliability and practicality . |
Medical Vision-Language Pre-Training for Brain Abnormalities (2024.lrec-main)
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| Challenge: | Existing vision-language models lack expertise for medical applications due to the scarcity and complexity of data. |
| Approach: | They propose a pipeline to collect medical image-text aligned data for pretraining from public resources such as PubMed and build a high-performance vision-language model tailored to specific medical tasks. |
| Outcome: | The proposed model is based on a large brain image-text dataset and will be released to the public. |
MedMT5: An Open-Source Multilingual Text-to-Text LLM for the Medical Domain (2024.lrec-main)
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Iker García-Ferrero, Rodrigo Agerri, Aitziber Atutxa Salazar, Elena Cabrio, Iker de la Iglesia, Alberto Lavelli, Bernardo Magnini, Benjamin Molinet, Johana Ramirez-Romero, German Rigau, Jose Maria Villa-Gonzalez, Serena Villata, Andrea Zaninello
| Challenge: | Existing studies on large language models for medical applications have focused on a single language . medical mT5 outperforms both encoders and similar sized text-to-text models in English, French, and Italian benchmarks . |
| Approach: | They propose to train Medical mT5, the first open-source text-to-text multilingual model for the medical domain. |
| Outcome: | The proposed model outperforms encoders and similar sized models on the Spanish, French, and Italian benchmarks while being competitive with current state-of-the-art models in English. |
MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models (2026.findings-acl)
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Boqi Chen, Xudong Liu, Jiachuan Peng, Marianne Frey-Marti, Kyle Lam, Bang Zheng, Lin Li, Jianing Qiu
| Challenge: | Existing benchmarks for multimodal large language models do not capture real-world clinical complexity. |
| Approach: | They evaluate multilingual, multimodal multimodal models of clinical cases with up to 7 distinct visual clinical evidence types per case. |
| Outcome: | The proposed model outperforms human models on differential diagnosis (DDx) generation and final diagnosis (FDx) selection. |
Medical Graph RAG: Evidence-based Medical Large Language Model via Graph Retrieval-Augmented Generation (2025.acl-long)
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Junde Wu, Jiayuan Zhu, Yunli Qi, Jingkun Chen, Min Xu, Filippo Menolascina, Yueming Jin, Vicente Grau
| Challenge: | GraphRAG framework is designed to enhance LLMs in generating evidence-based medical responses. |
| Approach: | They propose a graph-based Retrieval-augmented generation framework to enhance LLMs in generating evidence-based medical responses. |
| Outcome: | The proposed framework outperforms state-of-the-art models on 9 medical Q&A benchmarks, 2 health fact-checking datasets, and a long-form generation test set. |
Not All Citations Are Equal:Entropy-Guided Citation Selection for Noise-Resistant Medical LLM (2026.findings-acl)
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| Challenge: | Large language models have demonstrated extensive potential in medical applications . however, their practical deployment in healthcare faces significant challenges . |
| Approach: | They propose a training-free multi-turn reasoning framework and a post-training methodology that provides external knowledge support for large language models. |
| Outcome: | The proposed framework elicits internal thought, external thought, and fusion thought, with an entropy-based reward that encourages selective citation of beneficial external knowledge while penalizing noisy citations. |
CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding (2026.findings-acl)
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| Challenge: | Multimodal large language models generate medical hallucinations due to over-sensitivity to clinical sections. |
| Approach: | They propose a framework that integrates structured clinical signals from task-specific radiology expert models. |
| Outcome: | The proposed framework improves overall performance on radiology report generation (RRG) on the MIMIC-CXR dataset, it yields up to 17% improvement in RadGraph-F1. |