Karin Bergling

Medical Research Scientist

Karin Bergling

Karin earned her medical degree from Lund University, Sweden, and holds a PhD focused on peritoneal glucose transport and treatment optimization. Additionally, Karin has an MSc in Engineering Nanoscience from Lund University, Sweden, specializing in nanobiomedicine. Prior to joining the RRI, Karin spent five years working as a physician, including three years within nephrology and dialysis.

As a Research Scientist at RRI, Karin is dedicated to advance the application of computational statistics and artificial intelligence in healthcare, with a particular emphasis on enhancing clinical outcomes and quality of life for dialysis patients.” 

Recent Articles by Karin Bergling

  • Clinical kidney journal
    March 17, 2025
    From bytes to bites: application of large language models to enhance nutritional recommendations
    Karin Bergling, Lin-Chun Wang, Oshini Shivakumar, Andrea Nandorine Ban, Linda W Moore, Nancy Ginsberg, Jeroen Kooman, Neill Duncan, Peter Kotanko, Hanjie Zhang
    Large language models (LLMs) such as ChatGPT are increasingly positioned to be integrated into various aspects of daily life, with promising applications in healthcare, including personalized nutritional guidance for patients with chronic kidney disease (CKD). However, for LLM-powered nutrition support tools to reach their full potential, active collaboration of healthcare professionals, patients, caregivers and LLM experts is crucial. We conducted a comprehensive review of the literature on the use of LLMs as tools to enhance nutrition recommendations for patients with CKD, curated by our expertise in the field. Additionally, we considered relevant findings from adjacent fields, including diabetes and obesity management. Currently, the application of LLMs for CKD-specific nutrition support remains limited and has room for improvement. Although LLMs can generate recipe ideas, their nutritional analyses often underestimate critical food components such as electrolytes and calories. Anticipated advancements in LLMs and other generative artificial intelligence (AI) technologies are expected to enhance these capabilities, potentially enabling accurate nutritional analysis, the generation of visual aids for cooking and identification of kidney-healthy options in restaurants. While LLM-based nutritional support for patients with CKD is still in its early stages, rapid advancements are expected in the near future. Engagement from the CKD community, including healthcare professionals, patients and caregivers, will be essential to harness AI-driven improvements in nutritional care with a balanced perspective that is both critical and optimistic.