A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding
arXiv:2512.21414v2 Announce Type: replace-cross Abstract: Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tools. Broadly, these frameworks leverage VLMs and a pre-specified toolbox to decompose the prediction task into multiple tool calls (often deep learning models) which are composed to make a prediction. The dominant approach to composing tools is using text, via function calls embedded in VLM-generated code or natural language. However, these methods often perform poorly on medical image underst
The proliferation of vision-language models and the increasing demand for interpretable AI in sensitive domains like medicine are driving research into more sophisticated tool-use frameworks.
Improving the interpretability and reliability of AI in medical image understanding can accelerate diagnosis, enhance treatment planning, and build greater trust in AI-driven healthcare solutions.
The development of 'tool bottleneck frameworks' aims to make complex AI decisions in medical imaging more transparent and clinically actionable, moving beyond opaque black-box models.
- · Healthcare sector
- · Patients
- · AI ethicists
- · Medical diagnostic companies
- · Black-box AI developers
- · Traditional diagnostic methods
More accurate and explainable AI diagnostics become widespread in clinical settings.
Increased adoption of AI in medicine reduces diagnostic errors and improves patient outcomes, leading to healthcare cost efficiencies.
The success of interpretable AI in medicine sets a precedent for other mission-critical applications, influencing regulatory frameworks and public acceptance across industries.
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Read at arXiv cs.LG