NLM DIR Seminar Schedule
UPCOMING SEMINARS
RECENT SEMINARS
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June 30, 2026 Jaya Srivastava
Disrupted Regulation of Essential Genes Mediates Dementias and Age-Associated Disorders -
June 11, 2026 Angela Jiang
Identification and Evolutionary Analysis of Steroid-Metabolism Enzymes in Gut Microbes -
June 10, 2026 Luda Diatchenko
New Insights on Pain Biology from Human Transcriptomics: How Stimulation of Immune Response Shapes Pain Resolution -
June 9, 2026 Pascal Mutz
Characterization of covalently closed circular RNA replicators detected in (meta)transcriptomic data -
June 4, 2026 Madeleine Clore
Explaining why AlphaFold struggles to predict mutational effects
Scheduled Seminars on Jan. 16, 2025
In-person: Building 38A/B2N14 NCBI Library or Meeting Link
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
We introduce GPTRadScore, a groundbreaking evaluation framework for assessing multimodal large language models (LLMs) in CT imaging. Using GPT-4, GPTRadScore measures model performance in tasks like lesion localization, body part identification, and lesion typing. It outperforms traditional metrics such as BLEU and ROUGE, aligning closely with expert clinician assessments. Fine-tuning with specialized datasets significantly boosts performance, as demonstrated by RadFM’s notable improvements in accuracy.
To support the development of AI in CT imaging, we also present CT-Bench, a comprehensive dataset containing 20,335 annotated lesions from 7,795 patient studies. Accompanied by high-quality, GPT-4-enhanced textual descriptions and a visual question-answering (VQA) benchmark with 2,850 QA pairs, CT-Bench enables targeted training and evaluation of AI models for lesion description, localization, and diagnostic reasoning.
Together, GPTRadScore and CT-Bench provide powerful tools to advance multimodal AI, setting new standards for evaluation, training, and performance in CT imaging analysis.