Xiang Li

Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success

2018 · 695 Zit. · Journal of the American College of Radiology

Summary of ChatGPT-Related research and perspective towards the future of large language models

2023 · 686 Zit. · Meta-Radiology

Federated learning for predicting clinical outcomes in patients with COVID-19

2021 · 657 Zit. · Nature Medicine

Evaluating large language models on a highly-specialized topic, radiation oncology physics

2023 · 133 Zit. · Frontiers in Oncology

Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models

2023 · 123 Zit. · arXiv (Cornell University)

DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4

2023 · 89 Zit. · arXiv (Cornell University)

Differentiating ChatGPT-Generated and Human-Written Medical Texts: Quantitative Study

2023 · 66 Zit. · JMIR Medical Education

BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks

2023 · 50 Zit. · arXiv (Cornell University)

Federated Learning used for predicting outcomes in SARS-COV-2 patients

2021 · 44 Zit.

Artificial Intelligence in Nephrology: How Can Artificial Intelligence Augment Nephrologists’ Intelligence?

2019 · 43 Zit. · Kidney Diseases

Tailoring Large Language Models to Radiology: A Preliminary Approach to LLM Adaptation for a Highly Specialized Domain

2023 · 36 Zit. · Lecture notes in computer science

Radiology-Llama2: Best-in-Class Large Language Model for Radiology

2023 · 30 Zit. · arXiv (Cornell University)

How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information

2024 · 29 Zit. · Clinical Orthopaedics and Related Research

Surviving ChatGPT in healthcare

2024 · 29 Zit. · Frontiers in Radiology

Differentiate ChatGPT-generated and Human-written Medical Texts

2023 · 24 Zit. · arXiv (Cornell University)