mlsanityData to 14 Oct 2026

Medical Dialogue: A Survey of Categories, Methods, Evaluation and Challenges

arXiv 2405.10630 ↗First posted 17 May 2024cs.CL cs.AIcited by 5

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Abstract (arXiv)

This paper surveys and organizes research works on medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical community from an application perspective, a systematic review from a rigorous technical perspective has to date remained noticeably absent. As a result, an overview of the categories, methods, and evaluation of medical dialogue systems remain limited and underspecified, hindering the further improvement of this area. To fill this gap, we investigate an initial pool of 325 papers from well-known computer science, and natural language processing conferences and journals, and make an overview. Recently, large language models have shown strong model capacity on downstream tasks, which also reshaped medical dialog systems' foundation. Despite the alluring practical application value, current medical dialogue systems still suffer from problems. To this end, this paper lists the grand challenges of medical dialog systems, especially of large language models.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 86 of its 203 references that are in the map. First posted on arXiv 17 May 2024.

Builds on

86 of 203 references.

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Cited by year

Citing papers per year: 2024: 1; 2025: 4.

5 papers.

  1. 2024RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Modelscited by 25
  2. 2025CLARITY: Clinical Assistant for Routing, Inference, and Triagecited by 2
  3. 2025HealthBranches: Synthesizing Clinically-Grounded Question Answering Datasets via Decision Pathwayscited by 1
  4. 2025How to Evaluate Medical AIcited by 1
  5. 2025GAP: Graph-Assisted Prompts for Dialogue-based Medication Recommendationcited by 0