16 papers
Multi-Task Bayesian In-Context Learning
Qingyang Zhu, Eric Karl Oermann, Kyunghyun Cho
Bayesian predictive inference provides a principled framework for uncertainty quantification, data efficiency, and robust generalization. However, exact inference is often intracta…
Paradox of De-identification: A Critique of HIPAA Safe Harbour in the Age of LLMs
Lavender Y. Jiang, Xujin Chris Liu, Kyunghyun Cho +1
Privacy is a human right that sustains patient-provider trust. Clinical notes capture a patient's private vulnerability and individuality, which are used for care coordination and…
Large Language Models Predict Functional Outcomes after Acute Ischemic Stroke
Anjali K. Kapoor, Anton Alyakin, Jin Vivian Lee +8
Accurate prediction of functional outcomes after acute ischemic stroke can inform clinical decision-making and resource allocation. Prior work on modified Rankin Scale (mRS) predic…
Large-Scale Multi-omic Biosequence Transformers for Modeling Protein-Nucleic Acid Interactions
Sully F. Chen, Robert J. Steele, Glen M. Hocky +3
The transformer architecture has revolutionized bioinformatics and driven progress in the understanding and prediction of the properties of biomolecules. To date, most biosequence…
Generalist Large Language Models Outperform Clinical Tools on Medical Benchmarks
Krithik Vishwanath, Mrigayu Ghosh, Anton Alyakin +3
Specialized clinical AI assistants are rapidly entering medical practice, often framed as safer or more reliable than general-purpose large language models (LLMs). Yet, unlike fron…
CNS-Obsidian: A Neurosurgical Vision-Language Model Built From Scientific Publications
Anton Alyakin, Jaden Stryker, Daniel Alexander Alber +29
General-purpose VLMs demonstrate impressive capabilities, but their opaque training on uncurated internet data poses critical limitations for high-stakes decision-making, such as i…