Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Ian Pan
Emacs Appearances in Pop Culture
As an Emacs user, few things are as delightful as catching my favorite text editor out in the wild. It doesn’t happen often though – Emacs is niche, and pop culture rarely gives it a nod. This post tracks down every one I know of (as of June 2026), and I’ll keep adding to it as I stumble across more. Here you go, in no particular order: The Social Network is a biographical drama film portraying the founding of Facebook.
Patient-friendly simplification and translation of neuroradiology impressions using artificial intelligence
Abstract Healthcare systems in the United States are now mandated to provide patients with immediate access to medical results. Access to complex results prior to appropriate clinical follow-up may cause confusion and fear, especially in populations with limited health literacy and English language proficiency. In this study, we demonstrate a use case of large language models (LLMs) to simplify and translate complex medical information for patients.
External Validation of a Winning Artificial Intelligence Algorithm from the RSNA 2022 Cervical Spine Fracture Detection Challenge
Abstract BACKGROUND AND PURPOSE: The Radiological Society of North America has actively promoted artificial intelligence (AI) challenges since 2017. Algorithms emerging from the recent RSNA 2022 Cervical Spine Fracture Detection Challenge demonstrated state-of-the-art performance in the competition’s data set, surpassing results from prior publications. However, their performance in real-world clinical practice is not known.
Building an Intelligent Emacs
This post introduces the combination of Emacs and LSP, and how you can make your own editor “smarter” by using the same idea of communications between an editor client and multiple language servers.
Deep Learning: An Update for Radiologists
SA-CME LEARNING OBJECTIVES After completing this journal-based SA-CME activity, participants will be able to: ■ Differentiate among four computer vision tasks using deep learning techniques on radiologic images: classification, detection, semantic segmentation, and instance segmentation. ■ Identify building blocks that constitute components of more complex neural network architectures. ■ Discuss neural network architectures adapted to different computer vision tasks.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Ian
Contact Ian, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack