# MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotations

arXiv: https://arxiv.org/abs/2410.14204 · HTML: https://mlsanity.com/paper/2410.14204/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 79 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 22 of 22)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://mlsanity.com/paper/1909.05358/) (2019)
- [Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System](https://mlsanity.com/paper/2109.14739/) (2021)
- [Dialogue State Tracking with a Language Model using Schema-Driven Prompting](https://mlsanity.com/paper/2109.07506/) (2021)
- [DDXPlus: A New Dataset For Automatic Medical Diagnosis](https://mlsanity.com/paper/2205.09148/) (2022)
- [Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems](https://mlsanity.com/paper/1704.00057/) (2017)
- [Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization](https://mlsanity.com/paper/2110.07356/) (2021)
- [Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques](https://mlsanity.com/paper/2005.01795/) (2020)
- [Description-Driven Task-Oriented Dialog Modeling](https://mlsanity.com/paper/2201.08904/) (2022)
- [Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures](https://mlsanity.com/paper/2009.08666/) (2020)
- [NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes](https://mlsanity.com/paper/2310.15959/) (2023)
- [A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets](https://mlsanity.com/paper/2204.08997/) (2022)
- [Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System](https://mlsanity.com/paper/2305.02468/) (2023)
- [Extracting Symptoms and their Status from Clinical Conversations](https://mlsanity.com/paper/1906.02239/) (2019)
- [ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues](https://mlsanity.com/paper/2109.00430/) (2021)
- [Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog](https://mlsanity.com/paper/2210.08917/) (2022)

## Built on by (most cited first, 22 of 22)

- [TAB-PO: Preference Optimization with a Token-Level Adaptive Barrier for Token-Critical Structured Generation](https://mlsanity.com/paper/2603.00025/) (2026)
- [Retrieval-Augmented Agentic Rubric Generation for Reliable Medical Response Evaluation](https://mlsanity.com/paper/2601.15161/) (2026)
- [EPPCMinerBen: A Novel Benchmark for Evaluating Large Language Models on Electronic Patient-Provider Communication via the Patient Portal](https://mlsanity.com/paper/2603.00028/) (2026)
- [Beyond Idealized Patients: Evaluating LLMs under Challenging Patient Behaviors in Medical Consultations](https://mlsanity.com/paper/2603.29373/) (2026)
- [PVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language Models](https://mlsanity.com/paper/2603.05776/) (2026)
- [EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents](https://mlsanity.com/paper/2604.07549/) (2026)
- [STaR-DRO: Stateful Tsallis Reweighting for Group-Robust Structured Prediction](https://mlsanity.com/paper/2604.09737/) (2026)
- [All Required, In Order: Phase-Level Evaluation for AI-Human Dialogue in Healthcare and Beyond](https://mlsanity.com/paper/2601.08690/) (2026)
- [A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agents](https://mlsanity.com/paper/2601.10809/) (2026)
- [Note2Chat: Improving LLMs for Multi-Turn Clinical History Taking Using Medical Notes](https://mlsanity.com/paper/2601.21551/) (2026)
- [MedAidDialog: A Multilingual Multi-Turn Medical Dialogue Dataset for Accessible Healthcare](https://mlsanity.com/paper/2603.24132/) (2026)
- ["Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborations](https://mlsanity.com/paper/2604.15588/) (2026)
- [Active Evidence-Seeking and Diagnostic Reasoning in Large Language Models for Clinical Decision Support](https://mlsanity.com/paper/2605.22047/) (2026)
- ["Where does it hurt?" -- Dataset and Study on Physician Intent Trajectories in Doctor Patient Dialogues](https://mlsanity.com/paper/2508.19077/) (2025)
- [Fine-Tuning DialoGPT on Common Diseases in Rural Nepal for Medical Conversations](https://mlsanity.com/paper/2511.00514/) (2025)
- [Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation](https://mlsanity.com/paper/2601.15645/) (2026)
- [IndicMedDialog: A Parallel Multi-Turn Medical Dialogue Dataset for Accessible Healthcare in Indic Languages](https://mlsanity.com/paper/2605.13292/) (2026)
- [Localizing Input Uncertainty Quantification for Large Language Models via Shapley Values](https://mlsanity.com/paper/2605.28170/) (2026)
- [MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation](https://mlsanity.com/paper/2607.09142/) (2026)
- [ELICITED: EHR-grounded Longitudinal Interactive Conversations for Information-seeking Triage Evaluation and Decision-making](https://mlsanity.com/paper/2608.09024/) (2026)
- [Beyond Semantic Accuracy: Consequence-Aware Evaluation for Safety-Critical Language Understanding](https://mlsanity.com/paper/2608.24621/) (2026)
- [DocTalkBN: A Novel Dataset of Expert Telemedicine Conversations in Bengali](https://mlsanity.com/paper/2608.27110/) (2026)

## Abstract (arXiv)

Medical task-oriented dialogue systems can assist doctors by collecting patient medical history, aiding in diagnosis, or guiding treatment selection, thereby reducing doctor burnout and expanding access to medical services. However, doctor-patient dialogue datasets are not readily available, primarily due to privacy regulations. Moreover, existing datasets lack comprehensive annotations involving medical slots and their different attributes, such as symptoms and their onset, progression, and severity. These comprehensive annotations are crucial for accurate diagnosis. Finally, most existing datasets are non-English, limiting their utility for the larger research community. In response, we introduce MediTOD, a new dataset of doctor-patient dialogues in English for the medical history-taking task. Collaborating with doctors, we devise a questionnaire-based labeling scheme tailored to the medical domain. Then, medical professionals create the dataset with high-quality comprehensive annotations, capturing medical slots and their attributes. We establish benchmarks in supervised and few-shot settings on MediTOD for natural language understanding, policy learning, and natural language generation subtasks, evaluating models from both TOD and biomedical domains. We make MediTOD publicly available for future research.