---
title: "MediTOD — paper by Saley et al. (2024) — summary and ideas"
description: "Medical task-oriented dialogue (TOD) systems could help doctors by gathering a patient's history… 44 references, cited by 9 papers here."
authors: ["Vishal Vivek Saley", "Goonjan Saha", "Rocktim Jyoti Das", "Dinesh Raghu", "Mausam"]
year: 2024
venue: "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2024.emnlp-main.936/"
doi: "10.18653/v1/2024.emnlp-main.936"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/saley-2024-meditod-english-dialogue-dataset-medical/"
---
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# MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotations

**Authors:** Vishal Vivek Saley, Goonjan Saha, [Rocktim Jyoti Das](https://md.research.elamur.ai/free/en/authors/rocktim-jyoti-das/), Dinesh Raghu and [Mausam](https://md.research.elamur.ai/free/en/authors/mausam/) · **Year:** 2024 · **Venue:** [Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2024](https://md.research.elamur.ai/free/en/venues/emnlp/2024/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.emnlp-main.936/) · [PDF](https://aclanthology.org/2024.emnlp-main.936.pdf) · [DOI 10.18653/v1/2024.emnlp-main.936](https://doi.org/10.18653/v1/2024.emnlp-main.936) · [OpenAlex](https://openalex.org/W4404783120)

**Citations:** 5 citations (OpenAlex, 2026-10-14) · [9](https://md.research.elamur.ai/free/en/papers/saley-2024-meditod-english-dialogue-dataset-medical/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Medical task-oriented dialogue (TOD) systems could help doctors by gathering a patient's history before or during a consultation. Building them is hard for three reasons. Privacy rules keep real doctor-patient conversations scarce. Most annotated corpora are not in English. Existing labels store slots as flat key-value pairs, so a detail like *onset* is never tied to the symptom it belongs to. MediTOD addresses all three. The authors take staged OSCE-style interviews published by Fareez et al. (2022), in which medical professionals play both doctor and patient, from the respiratory and musculoskeletal specialties. They annotate these dialogues with a new nested schema, CMAS, where each slot carries its own attributes. Annotators work through a questionnaire portal designed together with doctors, and slot values are mapped to UMLS concepts. Benchmarks cover NLU, policy learning (POL) and NLG in supervised and few-shot settings. Results show the dataset is challenging, particularly for policy prediction and for transfer across specialties. The data and the annotation portal are both released publicly.

## Key points
- The corpus has 22,503 annotated English utterances, averaging about 95.57 per dialogue. Per the comparison table, it is the only public medical dialogue set whose labels are comprehensive (attributes linked to slots), canonicalized, and cover all TOD subtasks.
- Six intents are defined: Inquire, Inform, Diagnose, Salutations, Chit-chat, Other. The slot inventory covers Symptom, Disease, Medication, Medical Test, Habits, Exposure, Patient/Family Medical History, Occupation, Residence, Travel Information, and Basic Information. Symptoms carry attributes such as onset, duration, location, frequency, severity, and progression.
- Labeling was done by six annotators with medical or pharmacy backgrounds, supervised by a doctor. Values were canonicalized to the UMLS Metathesaurus using QuickUMLS. The source dialogues are licensed CC0.
- Benchmarked models include Flan-T5, BioGPT, PPTOD, OpenBioLLM 8B, Llama3 8B, ChatGPT and GPT-4-Turbo.
- In out-of-domain tests on musculoskeletal dialogues, NLU and NLG scores fell because the specialty uses different terminology. POL held steady at its in-domain level, but that level was already low.

## Interesting ideas
- Each attribute (for example, onset) is stored inside the slot instance it describes, so a patient reporting two symptoms with two different onsets keeps each pairing intact. Flat key-value labels would lose which onset goes with which symptom.
- Doctors write a question for every attribute of every slot type, and annotators fill in labels by answering those questions instead of marking spans freely. This makes the labeling task simpler and the resulting structure consistent.
- Lay phrasing is normalized to a standard concept, such as mapping a "sore throat" mention to pharyngitis in UMLS. Evaluation can then compare concepts rather than surface strings.
- Models are trained on one specialty and tested on another. This split separates subtasks that depend on vocabulary (NLU, NLG) from those that do not (POL).

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (frames): Attach temporal and manner modifiers to the specific frame or participant they govern, especially in coordinated clauses, rather than to the sentence as a whole.
- **I2** (learn): Query LLM teacher tiers with fixed per-role questions for each frame (e.g., who, when, where), so their silver answers can be compared field by field across witnesses.
- **I3** (world): Link entity and state values to WordNet/Wikidata identifiers so that answers phrased differently but referring to the same concept count as matches.
- **I4** (learn): Hold out whole text domains, such as fables versus maths problems, as a control to show which pipeline stages break on unfamiliar vocabulary and which generalize.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Mohamed bin Zayed University of Artificial Intelligence](https://md.research.elamur.ai/free/en/universities/mohamed-bin-zayed-university-of-artificial-intelligence/) — University, Abu Dhabi, United Arab Emirates · 138 works · 6,590 citations
- [Indian Institute of Technology Delhi](https://md.research.elamur.ai/free/en/universities/indian-institute-of-technology-delhi/) — University, New Delhi, India · 24 works · 6,933 citations
- [IBM Research - India](https://md.research.elamur.ai/free/en/labs/ibm-research-india/) — Research institute, New Delhi, India · 9 works · 749 citations

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## Cite

Cite as: Vishal Vivek Saley, Goonjan Saha, Rocktim Jyoti Das, Dinesh Raghu and Mausam. 2024. MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotations. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{saley2024meditod,
  title = {{MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotations}},
  author = {Vishal Vivek Saley, Goonjan Saha, Rocktim Jyoti Das, Dinesh Raghu and Mausam},
  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  year = {2024},
  doi = {10.18653/v1/2024.emnlp-main.936},
  url = {https://doi.org/10.18653/v1/2024.emnlp-main.936}
}
```

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## References

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*Updated 2 November 2026*