activity
20192024
most citedCapabilities of Gemini Models in Medicine

96 citations · 213 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2024★ 7 cited

AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset

Tobi Olatunji, Charles Nimo, Abraham Owodunni +23

Recent advancements in large language model(LLM) performance on medical multiple choice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients gl…

cs.AI2024★ 96 cited

Capabilities of Gemini Models in Medicine

Khaled Saab, Tao Tu, Wei-Hung Weng +64

Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of…

cs.LG2022★ 1 cited

Instability in clinical risk stratification models using deep learning

Daniel Lopez-Martinez, Alex Yakubovich, Martin Seneviratne +9

While it has been well known in the ML community that deep learning models suffer from instability, the consequences for healthcare deployments are under characterised. We study th…

cs.LG2022★ 1 cited

Boosting the interpretability of clinical risk scores with intervention predictions

Eric Loreaux, Ke Yu, Jonas Kemp +8

Machine learning systems show significant promise for forecasting patient adverse events via risk scores. However, these risk scores implicitly encode assumptions about future inte…

cs.LG2019★ 39 cited

Learning an Adaptive Learning Rate Schedule

Zhen Xu, Andrew M. Dai, Jonas Kemp +1

The learning rate is one of the most important hyper-parameters for model training and generalization. However, current hand-designed parametric learning rate schedules offer limit…

cs.LG2019★ 12 cited

Improved Hierarchical Patient Classification with Language Model Pretraining over Clinical Notes

Jonas Kemp, Alvin Rajkomar, Andrew M. Dai

Clinical notes in electronic health records contain highly heterogeneous writing styles, including non-standard terminology or abbreviations. Using these notes in predictive modeli…