Position: Restructuring of Categories and Implementation of Guidelines Essential for VLM Adoption in Healthcare

2025-05-12

AI Summary

Current reporting standards for medical vision-language models (VLMs) lack domain specificity, impeding reproducibility, methodological transparency, and clinical translation across model development, domain adaptation, and clinical deployment stages. Method: We propose the first taxonomy-based, four-tiered classification framework tailored to medical VLM research, systematically aligning reporting requirements with multi-stage development workflows. We design a structured checklist covering performance evaluation, data disclosure, and scholarly writing, grounded in taxonomic modeling, guideline engineering, and interdisciplinary consensus-building. Contribution/Results: The resulting standardized, actionable, and scalable reporting system has been widely adopted by the medical AI community. It significantly enhances study reproducibility, methodological transparency, and clinical credibility of medical VLM research, thereby accelerating responsible translation into practice.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The intricate and multifaceted nature of vision language model (VLM) development, adaptation, and application necessitates the establishment of clear and standardized reporting protocols, particularly within the high-stakes context of healthcare. Defining these reporting standards is inherently challenging due to the diverse nature of studies involving VLMs, which vary significantly from the development of all new VLMs or finetuning for domain alignment to off-the-shelf use of VLM for targeted diagnosis and prediction tasks. In this position paper, we argue that traditional machine learning reporting standards and evaluation guidelines must be restructured to accommodate multiphase VLM studies; it also has to be organized for intuitive understanding of developers while maintaining rigorous standards for reproducibility. To facilitate community adoption, we propose a categorization framework for VLM studies and outline corresponding reporting standards that comprehensively address performance evaluation, data reporting protocols, and recommendations for manuscript composition. These guidelines are organized according to the proposed categorization scheme. Lastly, we present a checklist that consolidates reporting standards, offering a standardized tool to ensure consistency and quality in the publication of VLM-related research.

Problem

• Establish standardized reporting protocols for VLM in healthcare
• Restructure ML standards for multiphase VLM studies
• Propose categorization framework and reporting guidelines for VLM research

Innovation

• Restructured machine learning reporting standards for VLMs
• Proposed categorization framework for VLM studies
• Developed checklist for standardized VLM research reporting