mlsanityData to 7 Oct 2026

DDXPlus: A New Dataset For Automatic Medical Diagnosis

arXiv 2205.09148 ↗First posted 18 May 2022cs.CL cs.AI cs.LGcited by 87

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Abstract (arXiv)

There has been a rapidly growing interest in Automatic Symptom Detection (ASD) and Automatic Diagnosis (AD) systems in the machine learning research literature, aiming to assist doctors in telemedicine services. These systems are designed to interact with patients, collect evidence about their symptoms and relevant antecedents, and possibly make predictions about the underlying diseases. Doctors would review the interactions, including the evidence and the predictions, collect if necessary additional information from patients, before deciding on next steps. Despite recent progress in this area, an important piece of doctors' interactions with patients is missing in the design of these systems, namely the differential diagnosis. Its absence is largely due to the lack of datasets that include such information for models to train on. In this work, we present a large-scale synthetic dataset of roughly 1.3 million patients that includes a differential diagnosis, along with the ground truth pathology, symptoms and antecedents for each patient. Unlike existing datasets which only contain binary symptoms and antecedents, this dataset also contains categorical and multi-choice symptoms and antecedents useful for efficient data collection. Moreover, some symptoms are organized in a hierarchy, making it possible to design systems able to interact with patients in a logical way. As a proof-of-concept, we extend two existing AD and ASD systems to incorporate the differential diagnosis, and provide empirical evidence that using differentials as training signals is essential for the efficiency of such systems or for helping doctors better understand the reasoning of those systems.

Key facts. Cited by 87 papers in the map (data through 7 Oct 2026). Builds on 6 of its 59 references that are in the map. First posted on arXiv 18 May 2022.

Builds on

6 of 59 references.

References · 6

  1. 2019End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosiscited by 70
  2. 2021Diaformer: Automatic Diagnosis via Symptoms Sequence Generationcited by 19
  3. 2021Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and Classificationcited by 12
  4. 2022"My nose is running.""Are you also coughing?": Building A Medical Diagnosis Agent with Interpretable Inquiry Logicscited by 9
  5. 2021Knowledge Grounded Conversational Symptom Detection with Graph Memory Networkscited by 6
  6. 2021A Bayesian Approach for Medical Inquiry and Disease Inference in Automated Differential Diagnosiscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 21; 2025: 22; 2026: 36.

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  1. 2023A Survey on Evaluation of Large Language Modelscited by 1,227
  2. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 409
  3. 2025Agentic Context Engineering: Evolving Contexts for Self-Improving Language Modelscited by 349
  4. 2023On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspectivecited by 208
  5. 2024MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoningcited by 145
  6. 2024Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Modelscited by 90
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  8. 2024Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Applicationcited by 55
  9. 2024RareBench: Can LLMs Serve as Rare Diseases Specialists?cited by 45
  10. 2024StreamBench: Towards Benchmarking Continuous Improvement of Language Agentscited by 44
  11. 2024CoD, Towards an Interpretable Medical Agent using Chain of Diagnosiscited by 43
  12. 2024Towards Evaluating and Building Versatile Large Language Models for Medicinecited by 38
38 more
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  3. 2023TABLET: Learning From Instructions For Tabular Datacited by 28
  4. 2024PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restorationcited by 27
  5. 2025MEDDxAgent: A Unified Modular Agent Framework for Explainable Automatic Differential Diagnosiscited by 23
  6. 2024Assessing Adversarial Robustness of Large Language Models: An Empirical Studycited by 21
  7. 2024MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotationscited by 21
  8. 2025Automating Expert-Level Medical Reasoning Evaluation of Large Language Modelscited by 21
  9. 2023Large Language Models Perform Diagnostic Reasoningcited by 18
  10. 2024ToolScan: A Benchmark for Characterizing Errors in Tool-Use LLMscited by 17
  11. 2025Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applicationscited by 13
  12. 2026EvoRoute: Experience-Driven Self-Routing LLM Agent Systemscited by 13
  13. 2025Follow-up Question Generation For Enhanced Patient-Provider Conversationscited by 9
  14. 2024Fill In The Gaps: Model Calibration and Generalization with Synthetic Datacited by 8
  15. 2025Adjust for Trust: Mitigating Trust-Induced Inappropriate Reliance on AI Assistancecited by 6
  16. 2025Human-AI Collaborative Uncertainty Quantificationcited by 6
  17. 2026Reaching Beyond the Mode: RL for Distributional Reasoning in Language Modelscited by 6
  18. 2022Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learningcited by 5
  19. 2024PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language Modelscited by 5
  20. 2025Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluationcited by 5
  21. 2026Wiring the 'Why': A Unified Taxonomy and Survey of Abductive Reasoning in LLMscited by 5
  22. 2024Interpretable Differential Diagnosis with Dual-Inference Large Language Modelscited by 4
  23. 2025DoctorRAG: Medical RAG Fusing Knowledge with Patient Analogy through Textual Gradientscited by 4
  24. 2026MedEinst: Benchmarking the Einstellung Effect in Medical LLMs through Counterfactual Differential Diagnosiscited by 4
  25. 2026LLMs Can't Play Hangman: On the Necessity of a Private Working Memory for Language Agentscited by 4
  26. 2023DDxT: Deep Generative Transformer Models for Differential Diagnosiscited by 3
  27. 2024An Empirical Study on Large Language Models in Accuracy and Robustness under Chinese Industrial Scenarioscited by 3
  28. 2025Improving Interactive Diagnostic Ability of a Large Language Model Agent Through Clinical Experience Learningcited by 3
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  30. 2024On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problemcited by 2
  31. 2024E-Bench: Towards Evaluating the Ease-of-Use of Large Language Modelscited by 2
  32. 2024Automatic Differential Diagnosis using Transformer-Based Multi-Label Sequence Classificationcited by 2
  33. 2024Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Modelscited by 2
  34. 2026Strong Reasoning Isn't Enough: Evaluating Evidence Elicitation in Interactive Diagnosiscited by 2
  35. 2026Small Agent Group is the Future of Digital Healthcited by 2
  36. 2026EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agentscited by 2
  37. 2026MamaBench: Benchmarking LLM Robustness in Maternal and Child Health Diagnosis through Counterfactual Clinical Perturbationcited by 2
  38. 2025Second Opinion Matters: Towards Adaptive Clinical AI via the Consensus of Expert Model Ensemblecited by 1

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