arXiv:2606.19174v1 Announce Type: cross Abstract: Clinician-centered evaluation is critical for validating medical AI systems, especially in ultrasound imaging where quantitative metrics do not always capture clinical usability. Existing medical image platforms primarily focus on dataset labeling. They lack integrated support for blinded model comparison and reproducible evaluation workflows. We present a clinician-centered pipeline for remote annotation and evaluation in ultrasound AI studies. The proposed pipeline uses a centralized server and lightweight browser interfaces to enable clinici
The proliferation of AI in medical imaging necessitates robust, clinician-centric evaluation tools to ensure practical applicability and safety as AI systems move from research to clinical deployment.
This development is crucial for bridging the gap between theoretical AI performance and real-world clinical usability, enabling faster, safer, and more effective integration of AI into healthcare.
The focus shifts from mere dataset labeling to integrated, blinded model comparison and reproducible evaluation workflows, accelerating the validation and adoption of medical AI.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · Clinical research organizations
- · AI models lacking clinical utility
- · Traditional, siloed evaluation methods
Clinicians gain better tools to evaluate medical AI, accelerating its adoption into practice.
Improved clinical AI validation leads to more trustworthy and effective AI systems in healthcare.
The development of standardized, clinician-centered evaluation platforms becomes a critical component of medical AI regulation and market success.
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Read at arXiv cs.AI