Academic Appointments

  • Clinical Scholar, Radiology

All Publications

  • Reply. Journal of the American College of Radiology : JACR Herwald, S. E., Shah, P., Johnston, A., Olsen, C., Delbrouck, J., Langlotz, C. P. 2025

    View details for DOI 10.1016/j.jacr.2025.08.004

    View details for PubMedID 40812729

  • RadGPT: A system based on a large language model that generates sets of patient-centered materials to explain radiology report information. Journal of the American College of Radiology : JACR Herwald, S. E., Shah, P., Johnston, A., Olsen, C., Delbrouck, J., Langlotz, C. P. 2025

    Abstract

    OBJECTIVE: The Cures Act Final Rule requires that patients have real-time access to their radiology reports, which contain technical language. Our objective to was to use a novel system called RadGPT, which integrates concept extraction and a large language model (LLM), to help patients understand their radiology reports.METHODS: RadGPT generated 150 concept explanations and 390 question-and-answer pairs from 30 radiology report impressions from between 2012 and 2020. The extracted concepts were used to create concept-based explanations, as well as concept-based question-and-answer pairs where questions were generated using either a fixed template or an LLM. Additionally, report-based question-and-answer pairs were generated directly from the impression using an LLM without concept extraction. One board-certified radiologist and 4 radiology residents rated the material quality using a standardized rubric.RESULTS: Concept-based LLM-generated questions were significantly higher quality than concept-based template-generated questions (p < 0.001). Excluding those template-based question-and-answer pairs from further analysis, nearly all (> 95%) of RadGPT-generated materials were rated highly, with at least 50% receiving the highest possible ranking from all 5 raters. No answers or explanations were rated as likely to affect the safety or effectiveness of patient care. Report-level LLM-based questions and answers were rated particularly highly, with 92% of report-level LLM-based questions and 61% of the corresponding report-level answers receiving the highest rating from all raters.DISCUSSION: The educational tool RadGPT generated high-quality explanations and question-and-answer pairs that were personalized for each radiology report, unlikely to produce harmful explanations and likely to enhance patient understanding of radiology information.

    View details for DOI 10.1016/j.jacr.2025.06.013

    View details for PubMedID 40505763

  • Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis Prakash, E., Valanarasu, J., Chen, Z., Reis, E., Johnston, A., Pareek, A., Bluethgen, C., Gatidis, S., Olsen, C., Chaudhari, A., Ng, A., Langlotz, C., IEEE COMPUTER SOC IEEE COMPUTER SOC. 2025: 4472-4480

    View details for DOI 10.1109/ICCVW69036.2025.00463

    View details for Web of Science ID 001740020100463