7 papers
Unlocking the Power of Medical Tabular Data via Semantic-Aware Multimodal Pre-training
Yingsheng Liu, Haiming Li, Jingmin Zhu +6
While vision-language models dominate medical representation learning, unstructured text lacks the dense, quantitative diagnostic phenotypes inherent in structured clinical tables.…
A Vision-Language Foundation Model for Zero-shot Clinical Collaboration and Automated Concept Discovery in Dermatology
Siyuan Yan, Xieji Li, Dan Mo +28
Medical foundation models have shown promise in controlled benchmarks, yet widespread deployment remains hindered by reliance on task-specific fine-tuning. Here, we introduce DermF…
Multi-Aspect Knowledge-Enhanced Medical Vision-Language Pretraining with Multi-Agent Data Generation
Xieji Li, Siyuan Yan, Yingsheng Liu +4
Vision-language pretraining (VLP) has emerged as a powerful paradigm in medical image analysis, enabling representation learning from large-scale image-text pairs without relying o…
From Evidence to Decision: Exploring Evaluative AI
Thao Le, Tim Miller, Liz Sonenberg +2
This paper presents a hypothesis-driven approach to improve AI-supported decision-making that is based on the Evaluative AI paradigm - a conceptual framework that proposes providin…
Supporting Data-Frame Dynamics in AI-assisted Decision Making
Chengbo Zheng, Tim Miller, Alina Bialkowski +2
High stakes decision-making often requires a continuous interplay between evolving evidence and shifting hypotheses, a dynamic that is not well supported by current AI decision sup…
A Multimodal Vision Foundation Model for Clinical Dermatology
Siyuan Yan, Zhen Yu, Clare Primiero +22
Diagnosing and treating skin diseases require advanced visual skills across domains and the ability to synthesize information from multiple imaging modalities. While current deep l…