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JOON PARK

As seen in: Medium, Nature, Wiley Online Library, Business Wire, MDPI, CSIRO Publishing, National Bureau of Economic Research, Dialnet - Universidad de La Rioja, Optica, American Journal of Managed Care and

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Articles

Human-in-the-loop validation of a sequential multi-LLM medical education pipeline

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By Yoojin Nam, Taein An, Sung Il Hwang, Ji Won Jeong, Dong Yeong Kim, Pyeong Hwa Kim, So Yeon Kim, Sungkwan Kim, Taehee Lee, JOON PARK, Seunghyeon Shin, Yongsik Sim, Seunghyun Song, Namkug Kim, Chawoong Jeon, Juhyun Jeong, Yangwon Kim, Kwang Hwi Lee, Jeong Min Song
| Nature Verified
Abstract Large language models (LLMs) can generate medical educational content at scale, but multi-dimensional human validation of multi-LLM pipelines has not been performed at scale. We evaluated a 7-stage sequential multi-LLM pipeline implemented exclusively with the Gemini family (Gemini 3 Flash and Pro for text; Gemini 3 Pro Image, the Nano Banana Pro preview, for images), producing 6000 flashcards and 833 infographics for radiology board preparation.
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Tracking digital payments skids

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By JOON PARK
| Payments Dive Verified
Joon Park is the chief legal officer at Extend Enterprises, an expense management company. He is based in Cambridge, Massachusetts. For most people, the move from paper checks to electronic payments sounds like obvious progress. It’s faster, cleaner, and easier to scale. For decades, a paper check did more than just move money. It was numbered, it could be annotated with a client matter, filing date or trust account, and critically, USCIS only cashed it when an application was accepted.
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Adnexal torsion diagnosis framework with CT-based adaptive preprocessing and deep neural networks

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By Sang Min Kim, Jong Kim, Yoon Hee Lee, Jisun Lumiere Lee, JOON PARK, Hyun Lee, Juhun Lee, Ho Kyung Shin, Woo-Jeoung Nam
| Nature Verified
Abstract Adnexal cystic torsion is a gynecological emergency that requires prompt and accurate diagnosis followed by immediate surgical intervention to preserve ovarian function. Although ultrasound or computed tomography (CT) are commonly employed, their diagnostic accuracy is often limited by image quality and interobserver variability. This study aimed to evaluate the feasibility of applying deep learning models for the automated diagnosis of adnexal torsion using abdominopelvic CT.
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