arXiv:2603.26738v3 Announce Type: replace-cross Abstract: While automated sleep staging has achieved expert-level accuracy, its clinical adoption is hindered by a lack of auditable reasoning. We introduce SleepVLM, a rule-grounded vision-language model (VLM) that stages sleep from multi-channel polysomnography (PSG) waveform images and generates clinician-readable rationales based on American Academy of Sleep Medicine (AASM) scoring criteria. Utilizing waveform-perceptual pre-training and rule-grounded supervised fine-tuning, SleepVLM achieved Cohen's kappa of 0.767 on a held-out test set (MAS
The convergence of advanced AI, particularly vision-language models, and the increasing demand for auditable AI reasoning is enabling new applications in medical diagnostics.
This development represents a significant step towards practical, transparent AI integration in sensitive fields like healthcare, addressing a key barrier to adoption.
AI models for medical diagnosis can now offer not just predictions but also human-readable explanations grounded in established medical criteria, enhancing trust and clinical utility.
- · AI healthcare developers
- · Sleep diagnostic clinics
- · Patients needing sleep disorder diagnosis
- · Medical technology companies
- · Traditional manual sleep staging methods
- · AI models lacking explainability
Automated sleep staging becomes more widely adopted in clinical settings due to increased trust and regulatory acceptance.
The explainable AI paradigm extends to other medical diagnostic fields, accelerating the integration of AI more broadly in healthcare.
Predictive and preventative healthcare gains significant momentum as AI-driven diagnostics become more accessible and transparent, leading to improved patient outcomes and reduced healthcare costs.
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