Papers with medical diagnosis
LLMs on interactive feature collections with implicit dynamic decision strategy (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) struggle to efficiently narrow down the search space . external engineered systems may not fully utilize the inherent problem-solving capabilities of LLMs . |
| Approach: | They propose to implicitly guide Large Language Models to enhance their interactive feature collection abilities within a single prompt. |
| Outcome: | The proposed approach improves the performance of large language models in real-world scenarios. |
DIRECT: Directional Relevance in Conversational Trajectories (2026.eacl-industry)
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| Challenge: | Conversational Agents (Agents) often fail to understand how to start a conversation or what to ask next . a novel approach to recommending highly relevant follow-up question suggestions is proposed . |
| Approach: | They propose a method to recommend highly relevant follow-up question suggestions . they use offline QBs to fetch the most-relevant candidate questions . |
| Outcome: | The proposed system produces a ranked list of highly relevant follow-up question recommendations within 1 sec. |
AD-LLM: Benchmarking Large Language Models for Anomaly Detection (2025.findings-acl)
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Tiankai Yang, Yi Nian, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xia Hu, Yue Zhao
| Challenge: | Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. |
| Approach: | They propose a benchmark that evaluates how large language models (LLMs) can help with NLP anomaly detection. |
| Outcome: | The proposed model can perform zero-shot detection without tasks-specific training, data augmentation and model selection, and it can suggest unsupervised AD models. |
RuCCoD: Towards Automated ICD Coding in Russian (2025.emnlp-main)
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Alexandr Nesterov, Andrey Sakhovskiy, Ivan Sviridov, Airat Valiev, Vladimir Makharev, Petr Anokhin, Galina Zubkova, Elena Tutubalina
| Challenge: | a new dataset for clinical coding in Russian is available for download . human coders must navigate a wide array of medical terminology and time pressures . |
| Approach: | They present a new dataset for ICD coding in Russian, a language with limited biomedical resources. |
| Outcome: | The proposed model improves accuracy on an in-house EHR dataset from 2017 to 2021. |
Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings (2025.findings-acl)
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| Challenge: | Existing methods provide probabilistic guarantees over a reference set of queries and answers, but they fail to identify when the answers to a query are uncertain. |
| Approach: | They propose a method that approximates predicate-conditional coverage guarantees while maintaining compact prediction sets. |
| Outcome: | The proposed method provides predicate-conditional coverage guarantees while maintaining compact prediction sets. |
Reasoning with Language Model Prompting: A Survey (2023.acl-long)
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Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen
| Challenge: | Reasoning is an essential ability for complex problem-solving and can provide back-end support for various real-world applications. |
| Approach: | They present cutting-edge research on reasoning with language model prompting and provide systematic resources to help beginners. |
| Outcome: | The proposed approaches have not been systematically reviewed and analyzed. |
Cross-modal Memory Networks for Radiology Report Generation (2021.acl-long)
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| Challenge: | Medical imaging reports are essential in clinical practice, and generating the reports is beneficial to reduce the burden of radiologists. |
| Approach: | They propose to use a shared memory to enhance the encoder-decoder framework for radiology report generation. |
| Outcome: | The proposed model can generate more accurate reports on two widely used datasets. |
CARER - ClinicAl Reasoning-Enhanced Representation for Temporal Health Risk Prediction (2024.emnlp-main)
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| Challenge: | Existing deep learning methods require large datasets to achieve high generalizability. |
| Approach: | They propose a framework that enhances deep learning models with clinical rationales derived from medically proficient Large Language Models. |
| Outcome: | The proposed framework outperforms state-of-the-art models on two tasks using two popular EHR datasets by up to 11.2%. |
MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation (2025.findings-emnlp)
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| Challenge: | MEXA is a training-free framework that performs modality- and task-aware aggregation of multiple expert models to enable effective multimodal reasoning across diverse domains. |
| Approach: | MEXA is a training-free framework that performs modality- and task-aware aggregation of multiple expert models. |
| Outcome: | MEXA performs modality- and task-aware aggregation of multiple expert models . it generates interpretable textual reasoning outputs and reasons over them using a Large Reasoning Model (LRM) MEX A consistently delivers performance improvements over strong multimodal benchmarks . |
Value of Information: A Framework for Human–Agent Communication (2026.acl-long)
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| Challenge: | Existing approaches to large language model (LLM) agents fail to account for stakes of different decisions. |
| Approach: | They propose a framework that balances task risk, query ambiguity, user effort . they use a value-of-information framework to dynamically weigh the expected utility gain . |
| Outcome: | The proposed model matches or exceeds the best manually-tuned baselines in four domains . it explicitly balances task risk, query ambiguity, and user effort . |