A Dataset of Robot-Patient and Doctor-Patient Medical Dialogues for Spoken Language Processing Tasks
arXiv:2605.26747v1 Announce Type: new Abstract: Large Language Models (LLMs) have brought huge improvements to Artificial Intelligence (AI), which can be applied to general-purpose tasks. However, their application to textual or spoken medical consultations is still an open research problem. This paper proposes MeDial-Speech, a novel speech dataset for training and evaluating Med-AIs that can carry out consultations with patients. It was collected in realistic environments from robot-patient and doctor-patient dialogues, contains 111+ hours of speech data (without data augmentation), and cover
The increasing capabilities of LLMs and the recognition of their limitations in specialized domains like medicine are driving the creation of domain-specific datasets.
This development is crucial for advancing AI's practical application in healthcare, potentially transforming patient consultations and clinical workflows.
The availability of MeDial-Speech facilitates the training of Med-AIs capable of more realistic and effective spoken medical consultations, reducing the gap between general AI and specialized medical AI.
- · AI healthcare developers
- · Patients (improved access/efficiency)
- · Hospitals and clinics
- · Speech recognition companies
- · Companies relying on general-purpose LLMs for medical AI without fine-tuning
- · Providers resistant to AI integration
Med-AIs will become more accurate and contextually aware in handling medical dialogues.
Increased adoption of AI for initial patient screening, triage, and information dissemination, freeing up human doctors for complex cases.
The development of truly autonomous AI agents capable of end-to-end medical consultations, potentially reshaping healthcare delivery models.
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Read at arXiv cs.AI