1 citations · 2 across the 5 of their papers we have counts for
5 papers
MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models
Hyunjae Kim, Dain Kim, Pan Xiao +25
Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams. Yet the development of multimodal foundation models is constrained by…
Are Multimodal LLMs Ready for Clinical Dermatology? A Real-World Evaluation in Dermatology
Roy Jiang, Hyunjae Kim, Zhenyue Qin +8
Multimodal large language models (MLLMs) have demonstrated promise on publicly available dermatology benchmarks. However, benchmark performance may not generalize to real-world der…
Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insights
Hyunjae Kim, Jiwoong Sohn, Aidan Gilson +24
Large language models (LLMs) are transforming the landscape of medicine, yet two fundamental challenges persist: keeping up with rapidly evolving medical knowledge and providing ve…
ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs
Pengrui Han, Rafal Kocielnik, Adhithya Saravanan +3
Large Language models (LLMs), while powerful, exhibit harmful social biases. Debiasing is often challenging due to computational costs, data constraints, and potential degradation…
Exploring Social Bias in Downstream Applications of Text-to-Image Foundation Models
Adhithya Prakash Saravanan, Rafal Kocielnik, Roy Jiang +2
Text-to-image diffusion models have been adopted into key commercial workflows, such as art generation and image editing. Characterising the implicit social biases they exhibit, su…