arXiv Artificial Intelligence

Timely Clinical Diagnosis through Active Test Selection

3 hours ago 12.09.2026 07:00 👁 2 views
Timely Clinical Diagnosis through Active Test Selection

Quick summary

arXiv:2510.18988v5 Announce Type: replace Abstract: There is growing interest in using machine learning (ML) to support clinical diagnosis, but most approaches rely on static, fully observed datasets and fail to reflect the sequential, resource-aware reasoning clinicians use in practice. Diagnosis remains complex and error prone, especially in high-pressure or resource-limited settings, underscoring the need for frameworks that help clinicians make timely and cost-effective decisions. We propose ACTMED (Adaptive Clinical Test selection via Model-based Experimental Design), a diagnostic framewo

Key takeaways

  • arXiv:2510.18988v5 Announce Type: replace Abstract: There is growing interest in using machine learning (ML) to support clinical diagnosis, but most approaches rely on static, fully observed datasets and fail to reflect the sequential, resource-aware reasoning clinicians use in practice.
  • Diagnosis remains complex and error prone, especially in high-pressure or resource-limited settings, underscoring the need for frameworks that help clinicians make timely and cost-effective decisions.
  • We propose ACTMED (Adaptive Clinical Test selection via Model-based Experimental Design), a diagnostic framewo

Why it matters

This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗