Papers with diagnosis
Task-oriented Dialogue System for Automatic Diagnosis (P18-2)
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Zhongyu Wei, Qianlong Liu, Baolin Peng, Huaixiao Tou, Ting Chen, Xuanjing Huang, Kam-fai Wong, Xiangying Dai
| Challenge: | Existing methods to identify phenotypes using electronic health records (EHRs) are expensive and difficult to transfer models from one disease to another. |
| Approach: | They propose a task-oriented dialogue system framework to make diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports. |
| Outcome: | The proposed system can collect additional symptoms from conversation and improve disease identification accuracy. |
MedDecXtract: A Clinician-Support System for Extracting, Visualizing, and Annotating Medical Decisions in Clinical Narratives (2025.acl-demo)
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| Challenge: | Clinical notes contain important information about medical decisions embedded within unstructured text. |
| Approach: | They propose an open-source interactive system that automatically extracts medical decisions from clinical text. |
| Outcome: | The open-source system extracts and visualizes medical decisions from clinical text. |
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning. (2022.coling-1)
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| Challenge: | Reasoning and knowledge-related skills are considered as fundamental skills for natural language understanding (NLU) tasks. |
| Approach: | They propose a method to diagnose correlations between an NLU dataset and a specific skill. |
| Outcome: | The proposed method is able to diagnose correlations between dataset and logical reasoning skill on 8 MRC and 3 NLI datasets. |
Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network (2021.naacl-main)
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| Challenge: | Existing methods provide explanations based on a precise medical knowledge base, which is disease-specific and difficult to obtain for experts in reality. |
| Approach: | They propose a method to extract supporting facts from irregular EMR without external knowledge bases by constructing a hierarchical graph network and using it to obtain causal relationship between multi-granularity features and diagnosis results. |
| Outcome: | The proposed method diagnoses four types of EMR correctly and provides accurate supporting facts for the results. |
D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat (2022.emnlp-main)
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| Challenge: | Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness. |
| Approach: | They propose to construct a Chinese Dialogue Dataset for depression-diagnosis-oriented chat based on clinical depression diagnostic criteria. |
| Outcome: | The proposed system can be used to diagnose depression using a Chinese Dialogue Dataset. |
Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers (2023.emnlp-main)
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| Challenge: | valence variability was significantly lower in the control group compared to ADHD, depression, bipolar disorder, MDD, PTSD, and OCD but not PPD. |
| Approach: | They study the relationship between tweet emotion dynamics and mental health disorders by using a user-disclosed diagnosis. |
| Outcome: | The results show that the measures varied by the user's self-disclosed diagnosis. |
On the Automatic Generation of Medical Imaging Reports (P18-1)
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| Challenge: | a complete medical imaging report contains multiple heterogeneous forms of information, including findings and tags . abnormal regions in medical images are difficult to identify and the reports are typically long, containing multiple sentences. |
| Approach: | They propose a multi-task learning framework which predicts tags and generates paragraphs for abnormal regions in medical images. |
| Outcome: | The proposed framework can generate long paragraphs on two publicly available datasets. |
Towards Reducing Diagnostic Errors with Interpretable Risk Prediction (2024.naacl-long)
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Denis McInerney, William Dickinson, Lucy Flynn, Andrea Young, Geoffrey Young, Jan-Willem van de Meent, Byron Wallace
| Challenge: | Diagnostic errors occur because clinicians cannot easily access relevant information in EHRs. |
| Approach: | They propose a method to use LLMs to identify pieces of evidence that indicate increased or decreased risk of specific diagnoses in patient EHRs. |
| Outcome: | The proposed method reduces diagnostic errors by identifying evidence in patient EHRs . it uses a Neural Additive Model to make predictions backed by evidence at time-points where clinicians are uncertain . |
MidMed: Towards Mixed-Type Dialogues for Medical Consultation (2023.acl-long)
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| Challenge: | Current medical dialogue systems assume that patients have explicit goals but are often unavailable in real-world situations due to the lack of medical knowledge. |
| Approach: | They propose a human-to-human mixed-type medical consultation dialogue corpus . they build benchmarking baselines on MidMed and propose an instruction-guiding framework . Experimental results show the effectiveness of InsMed . |
| Outcome: | The proposed system can help patients clarify their goals in real-world situations . it covers four departments with 8,309 dialogues and provides benchmarking baselines . |
The Tembusu Treebank: An English Learner Treebank (2022.lrec-1)
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| Challenge: | a new treebank is created to help diagnose ungrammatical sentences using mal-rules . the Tembusu Learner Treebank is an open treebank created from the corpus of Learner English . |
| Approach: | They propose to use the Tembusu Learner Treebank to train a new parse-ranking model for the English Resource Grammar . the model incorporates mal-rules in the annotation of ungrammatical sentences . |
| Outcome: | The Tembusu Learner Treebank is an open treebank created from the NTU Corpus of Learner English . the treebank is unique for incorporating mal-rules in the annotation of ungrammatical sentences . |
Zero-Shot Entailment Learning for Ontology-Based Biomedical Annotation Without Explicit Mentions (2025.coling-main)
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Rumana Ferdous Munne, Noriki Nishida, Shanshan Liu, Narumi Tokunaga, Yuki Yamagata, Kouji Kozaki, Yuji Matsumoto
| Challenge: | Automated biomedical annotation presents significant challenges when entities are not explicitly mentioned in the text. |
| Approach: | They propose an entailment-based zero-shot text classification approach to annotate biomedical text passages using the Homeostasis Imbalance Process (HOIP) ontology. |
| Outcome: | The proposed method performs well when processes are not explicitly mentioned . it is time-consuming and expensive to annotate biomedical texts with a specific ontology . |
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. |
Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging (2025.acl-long)
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Zhenyang Cai, Junying Chen, Rongsheng Wang, Weihong Wang, Yonglin Deng, Dingjie Song, Yize Chen, Zixu Zhang, Benyou Wang
| Challenge: | Current research suggests that multitask training outperforms single-task as different tasks can benefit each other, but they often overlook the internal relationships within these tasks. |
| Approach: | They employ compositional generalization (CG) to examine the generalization of multimodal large language models in medical imaging. |
| Outcome: | The proposed model can understand unseen medical images and is able to perform CG across classification and detection tasks. |
MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection (2025.coling-main)
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| Challenge: | Existing knowledge triples are ineffective in medical question-answering because of superfluous data and inability to capture complex relationships between symptoms and treatments. |
| Approach: | They propose a first-order logical reasoning model that uses First-Order Logic to model intricate relationships between diseases and treatments. |
| Outcome: | The proposed model captures the interplay of symptoms, diseases, and treatments, enhancing context comprehension. |
Seeing the Whole Elephant: A Benchmark for Failure Attribution in LLM-based Multi-Agent Systems (2026.acl-long)
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| Challenge: | Existing benchmarks rely on partially observable traces that capture only agent outputs . lack of full execution traces obscures many failure causes, authors argue . |
| Approach: | They propose a benchmark that allows attribution under full execution observability . they find full traces improve attribution accuracy by up to 76.5% over a partial-observation counterpart . |
| Outcome: | The proposed benchmark improves attribution accuracy by up to 76.5% over a partial-observation counterpart. |
MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotations (2024.emnlp-main)
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| Challenge: | Existing datasets lacking comprehensive annotations for medical history-taking are non-English . existing datasets lack comprehensive annotation for medical slots and their attributes . |
| Approach: | They propose a dataset of doctor-patient dialogues in English for medical history-taking task. |
| Outcome: | The proposed datasets are available in English and are compared with existing datasets. |
ClinicalMC: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models (2026.findings-acl)
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| Challenge: | Existing benchmarks assess LLM performance in single-course settings and lack systematic evaluation in multi-course scenarios, where a patient’s condition evolves over time. |
| Approach: | They propose to use large language models to assess their performance in multi-course clinical decision-making scenarios where a patient’s condition evolves over time. |
| Outcome: | The proposed model includes 1,275 Chinese and 5,804 English samples across four stages from admission to discharge. |
MKeCL: Medical Knowledge-Enhanced Contrastive Learning for Few-shot Disease Diagnosis (2024.lrec-main)
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| Challenge: | Existing approaches to disease classification are limited in real-world clinics due to insufficient data and inflexibility. |
| Approach: | They propose a medical knowledge-Enhanced Contrastive Learning approach to disease diagnosis . they incorporate medical knowledge graphs and medical licensing exams in modeling . |
| Outcome: | The proposed model outperforms existing models on real clinical EMRs on a single patient. |
Semantic Similarity Models for Depression Severity Estimation (2023.emnlp-main)
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| Challenge: | Public health systems have limited capacity for case detection and diagnosis due to the widespread use of social media. |
| Approach: | They propose to use social media content to generate semantic rankings for depressive symptoms and severity levels and use them to predict symptoms severity. |
| Outcome: | The proposed pipeline improves on two Reddit-based benchmarks and shows that it is more efficient than state-of-the-art in terms of measuring depression level. |
AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering (2025.findings-emnlp)
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| Challenge: | Existing Med-MLLMs fail when deployed in low-resource settings where abundant labeled data is unavailable. |
| Approach: | They propose a training-free agentic framework that performs medical knowledge augmentation via LLM agents. |
| Outcome: | The proposed framework performs medical knowledge augmentation via LLM agents. |
MedEinst: Benchmarking the Einstellung Effect in Medical LLMs through Counterfactual Differential Diagnosis (2026.acl-long)
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| Challenge: | Existing medical benchmarks fail to detect the Einstellung Effect in clinical diagnosis . Existing models exhibit the Einstellung effect, relying on statistical shortcuts rather than logical reasoning. |
| Approach: | They propose a counterfactual benchmark that uses statistical shortcuts to diagnose patients . they propose CGME-based system that iteratively refines reasoning paths . |
| Outcome: | The proposed model achieves high baseline accuracy but severe bias trap rates . iteratively refines reasoning paths in an exemplar base and consolidates disease-specific knowledge into illness graphs. |
ROSCO-Omni: Multimodal LLM-Based Communication Understanding for Non- and Minimally-Speaking Autistic Individuals (2026.findings-acl)
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| Challenge: | 30% of autistic individuals remain non- or minimally-speaking throughout their lives . however, caregivers rely on simultaneous integration of visual cues, auditory signals, and contextual understanding to infer intent. |
| Approach: | They propose a framework that fine tunes a teacher-student MLLM for domain-specialized inference. |
| Outcome: | The proposed framework achieves comparable performance to closed-source models . |
LLM-Based Multi-Agent Systems for Clinical Workflows: A Survey of AI Hospitals (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are moving from isolated text generation toward agentic work inside clinical workflows. |
| Approach: | They propose a workflow-level taxonomy for LLM-based multi-agent systems for clinical and healthcare workflows . they propose integration readiness levels, task-level instrumentation requirements and recurring workflow failure modes as a practical framework for comparing, evaluating and deploying clinical LLM agents and AI hospitals. |
| Outcome: | The proposed systems should be compared at the workflow level, rather than only by model components or end-task accuracy. |