Kriti Bhattarai

Leveraging GPT-4 for identifying cancer phenotypes in electronic health records: a performance comparison between GPT-4, GPT-3.5-turbo, Flan-T5, Llama-3-8B, and spaCy’s rule-based and machine learning-based methods

2024 · 39 Zit. · JAMIA Open

Leveraging GPT-4 for Identifying Cancer Phenotypes in Electronic Health Records: A Performance Comparison between GPT-4, GPT-3.5-turbo, Flan-T5 and spaCy’s Rule-based & Machine Learning-based methods

2023 · 8 Zit. · bioRxiv (Cold Spring Harbor Laboratory)

Evaluating Generative AI’s Ability to Identify Cancer Subtypes in Publicly Available Structured Genetic Datasets

2024 · 3 Zit. · Journal of Personalized Medicine

BioPulse-QA: A Dynamic Biomedical Question-Answering Benchmark for Evaluating Factuality, Robustness, and Bias in Large Language Models

2026 · 0 Zit. · ArXiv.org

BioPulse-QA: A Dynamic Biomedical Question-Answering Benchmark for Evaluating Factuality, Robustness, and Bias in Large Language Models

2026 · 0 Zit. · arXiv (Cornell University)