mlsanityData to 14 Oct 2026

PaniniQA: Enhancing Patient Education Through Interactive Question Answering

arXiv 2308.03253 ↗First posted 7 Aug 2023cs.CL cs.AIcited by 21

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Abstract (arXiv)

Patient portal allows discharged patients to access their personalized discharge instructions in electronic health records (EHRs). However, many patients have difficulty understanding or memorizing their discharge instructions. In this paper, we present PaniniQA, a patient-centric interactive question answering system designed to help patients understand their discharge instructions. PaniniQA first identifies important clinical content from patients' discharge instructions and then formulates patient-specific educational questions. In addition, PaniniQA is also equipped with answer verification functionality to provide timely feedback to correct patients' misunderstandings. Our comprehensive automatic and human evaluation results demonstrate our PaniniQA is capable of improving patients' mastery of their medical instructions through effective interactions

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 38 of its 75 references that are in the map. First posted on arXiv 7 Aug 2023.

Builds on

38 of 75 references.

References · 38

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26 more
  1. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 900
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  5. 2020Unsupervised Commonsense Question Answering with Self-Talkcited by 161
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  7. 2021Can Language Models be Biomedical Knowledge Bases?cited by 83
  8. 2022Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehensioncited by 80
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 6; 2025: 7; 2026: 3.

21 papers.

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  1. 2023NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notescited by 48
  2. 2023README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLPcited by 17
  3. 2024MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedbackcited by 14
  4. 2023Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospectscited by 8
  5. 2024SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMscited by 8
  6. 2023Improving Summarization with Human Editscited by 7
  7. 2025DischargeSim: A Simulation Benchmark for Educational Doctor-Patient Communication at Dischargecited by 7
  8. 2025MedReadCtrl: Personalizing medical text generation with readability-controlled instruction learningcited by 5
  9. 2023EHRTutor: Enhancing Patient Understanding of Discharge Instructionscited by 4
  10. 2025ChatThero: An LLM-Supported Chatbot for Behavior Change and Therapeutic Support in Addiction Recoverycited by 4
  11. 2025Chatbot To Help Patients Understand Their Healthcited by 4
  12. 2024Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Studycited by 3
9 more
  1. 2024A Neuro-Symbolic Approach to Monitoring Salt Content in Foodcited by 1
  2. 2024QOG:Question and Options Generation based on Language Modelcited by 1
  3. 2025Conversational Assistants to support Heart Failure Patients: comparing a Neurosymbolic Architecture with ChatGPTcited by 1
  4. 2025ChatCLIDS: Simulating Persuasive AI Dialogues to Promote Closed-Loop Insulin Adoption in Type 1 Diabetes Carecited by 1
  5. 2024Chatting Up Attachment: Using LLMs to Predict Adult Bondscited by 0
  6. 2025Towards conversational assistants for health applications: using ChatGPT to generate conversations about heart failurecited by 0
  7. 2026Rethinking Patient Education as Multi-turn Multi-modal Interactioncited by 0
  8. 2026MedGame: Storytelling Gamification Empowered by Large Language Models for Medical Educationcited by 0
  9. 2026Clinical Intent Extraction: A FHIR-Aligned Representation and the CIRCA Benchmarkcited by 0