arXiv (All)
open access
BiomedXPro: Prompt Optimization for Explainable Diagnosis with Biomedical Vision Language Models
The clinical adoption of biomedical vision-language models is hindered by prompt optimization techniques that produce either uninterpretable latent vectors or single textual prompts. This lack of transparency and failure to capture the multi-faceted nature of clinical diagnosis, which relies on inte
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Toward Guarantees for Clinical Reasoning in Vision Language Models via Formal Verification#1343513
- Ranking-Aware Prompt Optimization for Multimodal Clinical Diagnosis#1154620
- Ranking-Aware Prompt Optimization for Multimodal Clinical Diagnosis#1136805
- Characterizing Text Branch Sensitivity in Medical Vision-Language Segmentation via Evidence Decoupling#987839
- Med-AR: Autoregressive Vision-Language Pretraining for Long-Tailed Chest X-Ray Classification and Uncertainty-Aware Evaluation#1077085
Record · ID 1247301
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive