NLM DIR Seminar Schedule
UPCOMING SEMINARS
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July 1, 2025 Yoshitaka Inoue
Graph-Aware Interpretable Drug Response Prediction and LLM-Driven Multi-Agent Drug-Target Interaction Prediction -
July 3, 2025 Matthew Diller
Using Ontologies to Make Knowledge Computable -
July 8, 2025 Noam Rotenberg
TBD
RECENT SEMINARS
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July 1, 2025 Yoshitaka Inoue
Graph-Aware Interpretable Drug Response Prediction and LLM-Driven Multi-Agent Drug-Target Interaction Prediction -
June 10, 2025 Aleksandra Foerster
Interactions at pre-bonding distances and bond formation for open p-shell atoms: a step toward biomolecular interaction modeling using electrostatics -
June 3, 2025 MG Hirsch
Interactions among subclones and immunity controls melanoma progression -
May 29, 2025 Harutyun Sahakyan
In silico evolution of globular protein folds from random sequences -
May 20, 2025 Ajith Pankajam
A roadmap from single cell to knowledge graph
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.