Challenge: Existing medical dialogue systems have the problems of weak scalability, insufficient knowledge, and poor controllability.
Approach: They propose a medical conversational question-answering system based on the knowledge graph to improve scalability and controllability.
Outcome: The proposed system can conduct knowledge-grounded dialogues with users, using a Chinese medical knowledge graph and a large-scale dataset.

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MedDialog: Large-scale Medical Dialogue Datasets (2020.emnlp-main)

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Challenge: telemedicine is a medical practice that provides patient care remotely using video conferencing tools.
Approach: They build large-scale medical dialogue datasets to facilitate research . they pretrain several models on the Chinese MedDialog dataset and compare their performance .
Outcome: The proposed datasets show that models trained on MedDialog can generate doctor-like medical dialogues.
Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge (2025.findings-emnlp)

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Challenge: Large Language Models have greatly advanced medical Question Answering (QA) however, the rapid evolution of medical knowledge and manual updating of domain-specific resources can undermine reliability of these systems.
Approach: AMG-RAG automates the construction and continuous updating of Medical Knowledge Graph (MKG) . afriq: rapid evolution of medical knowledge and manual updating can undermine reliability of LLMs .
Outcome: AMG-RAG achieves an F1 score of 74.1% on MEDQA and an accuracy of 66.34% on medMCQA.
Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges (2024.findings-acl)

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Challenge: Existing medical dialogue systems have significant potential to simplify diagnostic procedure and reduce the cost of collecting information from patients.
Approach: They analyze 325 papers from well-known computer science, natural language processing conferences and journals to find out the major challenges of medical dialog systems.
Outcome: The proposed systems have been surveyed in the medical community but have not been evaluated from a technical perspective.
Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment (2025.findings-acl)

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Challenge: Medical dialogue systems (MDS) struggle to identify relevant medical knowledge and generate accurate responses.
Approach: They propose a medical dialogue system that integrates knowledge refining and dynamic prompt adjustment to improve medical knowledge and accuracy.
Outcome: The proposed system outperforms state-of-the-art systems in both generation quality and medical entity accuracy.
MedThink: A Rationale-Guided Framework for Explaining Medical Visual Question Answering (2025.findings-naacl)

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Challenge: Existing models for medical visual question answering are limited in their interpretation and interpretation . a semi-automated annotation process is used to streamline data preparation and build new benchmark datasets .
Approach: They propose a semi-automated annotation process to streamline data preparation and build new benchmark Med-VQA datasets.
Outcome: The proposed method achieves an accuracy of 83.5% on R-RAD, 86.3% on RSLAKE and 87.2% on RPath.
MedREQAL: Examining Medical Knowledge Recall of Large Language Models via Question Answering (2024.findings-acl)

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Challenge: Large language models can encode knowledge during pre-training on large text corpora, enabling downstream tasks like question answering (QA).
Approach: They construct a dataset derived from systematic reviews to examine their ability to encode medical knowledge and their recall.
Outcome: The proposed model performs well on the biomedical QA dataset.
CAMEC: Complexity-Aware Multi-Expert Collaboration for Reliable Chinese Medical Question Answering (2026.acl-long)

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Challenge: Large language models are promising for medical question answering in china, but remain unreliable due to hallucinations, weak factual grounding and difficulty handling clinically complex cases.
Approach: They propose a framework that combines hierarchical medical adaptation with complexity-aware expert routing for reliable Chinese medical QA.
Outcome: The proposed framework outperforms strong general and medical LLM baselines on four Chinese medical benchmarks.
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 Is Believing! towards Knowledge-Infused Multi-modal Medical Dialogue Generation (2024.lrec-main)

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Challenge: Existing models of disease diagnosis using AI do not use knowledge infusion.
Approach: They propose a transformer-based, knowledge-infused multi-modal medical dialogue generation framework . they propose 'discourse-aware' image identifier that recognizes signs and their severity .
Outcome: The proposed model outperforms state-of-the-art models by 7.84% in the english language.
MMCoQA: Conversational Question Answering over Text, Tables, and Images (2022.acl-long)

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Challenge: Existing conversational QA systems only use a single knowledge source, e.g., paragraphs or knowledge graph, and assume it contains enough evidence to extract answers to users' questions.
Approach: They propose a task to answer users' questions with multimodal knowledge sources via multi-turn conversations using a multimodal dataset.
Outcome: The proposed task brings a series of research challenges, including but not limited to priority, consistency, and complementarity of multimodal knowledge.

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