arXiv:2605.23118v1 Announce Type: cross Abstract: Tracking tumor lesions across serial CT scans is essential for oncological response assessment. Existing automated methods face a fundamental trade-off: end-to-end trackers achieve high automation but offer no opportunity to correct silent tracking failures, while decoupled registration-segmentation pipelines permit user verification yet discard the lesion's prior appearance, limiting accuracy in ambiguous cases. In this work, we propose a Verified Tracking paradigm: a clinician verifies a registration-proposed prompt, which the model leverages
This development emerges as AI models become sophisticated enough to handle complex image analysis, yet still benefit significantly from human oversight in critical applications like medical diagnostics.
It demonstrates a practical solution to the 'last mile' problem in AI adoption for medicine, by integrating human expertise directly into automated workflows to enhance accuracy and trust.
The paradigm shifts from purely automated or purely manual lesion tracking to a verified, interactive process that combines AI's efficiency with clinical verification for improved diagnostic outcomes.
- · Medical AI companies
- · Oncologists
- · Patients
- · Healthcare providers
- · Companies offering only fully automated, unverified diagnostic AI
- · Manual image analysis software
Increased adoption of AI tools in oncology due to enhanced trust and accuracy.
Reduced misdiagnosis rates and more personalized treatment plans for cancer patients.
The 'Verified Tracking' paradigm could become a standard for critical AI applications beyond medicine, demanding human-in-the-loop validation.
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Read at arXiv cs.LG