@inproceedings{zechariah-etal-2025-patient,
    title = "Patient-Centric Question Answering- Overview of the Shared Task at the Second Workshop on {NLP} and {AI} for Multilingual and Healthcare Communication",
    author = "Zechariah, Arun  and
      Krishna, Balu  and
      Mary Thomas, Hannah  and
      Mammen, Joy  and
      Misra Sharma, Dipti  and
      Krishnamurthy, Parameswari  and
      Mujadia, Vandan  and
      Dasari, Priyanka  and
      Arjunaswamy, Vishnuraj",
    editor = "Krishnamurthy, Parameswari  and
      Mujadia, Vandan  and
      Misra Sharma, Dipti  and
      Mary Thomas, Hannah",
    booktitle = "NLP-AI4Health",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.nlpai4health-main.5/",
    pages = "55--68",
    ISBN = "979-8-89176-315-9",
    abstract = "This paper presents an overview of the Shared Task on Patient-Centric Question Answering, organized as part of the NLP-AI4Health workshop at IJCNLP. The task aims to bridge the digital divide in healthcare by developing inclusive systems for two critical domains: Head and Neck Cancer (HNC) and Cystic Fibrosis (CF). We introduce the NLP4Health-2025 Dataset, a novel, large-scale multilingual corpus consisting of more than 45,000 validated multi-turn dialogues between patients and healthcare providers across 10 languages: Assamese, Bangla, Dogri, English, Gujarati, Hindi, Kannada, Marathi, Tamil, and Telugu. Participants were challenged to develop lightweight models ($<$ 3 billion parameters) to perform two core activities: (1) Clinical Summarization, encompassing both abstractive summaries and structured clinical extraction (SCE), and (2) Patient-Centric QA, generating empathetic, factually accurate answers in the dialogue native language. This paper details the hybrid human-agent dataset construction pipeline, task definitions, evaluation metrics, and analyzes the performance of 9 submissions from 6 teams. The results demonstrate the viability of small language models (SLMs) in low-resource medical settings when optimized via techniques like LoRA and RAG."
}