ArticleJournal of imaging informatics in medicine2024
Personalized Impression Generation for PET Reports Using Large Language Models.
Article in Journal of imaging informatics in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Testing and evaluation of generative large language models in electronic health record applications: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Precision Grounding: augmenting large language models with evidence-based databases for trustworthy genetic variant summarization.International journal of medical informatics · 2026Article
- Evaluating Large Language Models for Turkish Emergency CT Impression Drafting: Quality, Critical Omissions, and Readability.Journal of imaging informatics in medicine · 2026Article
- Staging Prostate Cancer with AI: A Comparative Study of Large Language Models and Expert Interpretation on PSMA PET-CT Reports.Molecular imaging and biology · 2026Article
- Conceptual proposal for LLM-generated FDG PET/CT follow-up reports in melanoma: a pilot study on model stability and blinded expert evaluation.Frontiers in nuclear medicine · 2026Article
- Case-matched retrieval improves textual alignment of LLM-generated radiology impressions.PloS one · 2026Article
- Diagnostic and interpretive gains from reasoning over conclusions with a large reasoning model in radiology.NPJ digital medicine · 2025Article
- Trends and Trajectories in the Rise of Large Language Models in Radiology: Scoping Review.JMIR medical informatics · 2025Article
- Automation ofEJNMMI research · 2025Article
- Human evaluation of large language models in healthcare: gaps, challenges, and the need for standardization.npj health systems · 2025Article
- Utility of Multimodal Large Language Models in Analyzing Chest X-Rays with Incomplete Contextual Information.Healthcare informatics research · 2025Article
- Development and evaluation of large-language models (LLMs) for oncology: A scoping review.PLOS digital health · 2025Article
- Artificial Intelligence for Teaching Case Curation: Evaluating Model Performance on Imaging Report Discrepancies.Academic radiology · 2025Article
- Empowering PET imaging reporting with retrieval-augmented large language models and reading reports database: a pilot single center study.European journal of nuclear medicine and molecular imaging · 2025Article
- Assessing large language models for Lugano classification of malignant lymphoma in Japanese FDG-PET reports.EJNMMI reports · 2025Article
- Large Language Models and Large Multimodal Models in Medical Imaging: A Primer for Physicians.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Review
- Radiology Report Annotation Using Generative Large Language Models: Comparative Analysis.International journal of biomedical imaging · 2025Article
- Generative AI and large language models in nuclear medicine: current status and future prospects.Annals of nuclear medicine · 2024Review
- Clinical Meaningfulness of an Algorithm-Based Service for Analyzing Treatment Response in Patients with Metastatic Cancer Using FDG PET/CT.Journal of clinical medicine · 2024Article
- A framework for human evaluation of large language models in healthcare derived from literature review.NPJ digital medicine · 2024Article
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11 authors at 3 institutions in 1 country.
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Abstract
Large language models (LLMs) have shown promise in accelerating radiology reporting by summarizing clinical findings into impressions. However, automatic impression generation for whole-body PET reports presents unique challenges and has received little attention. Our study aimed to evaluate whether LLMs can create clinically useful impressions for PET reporting. To this end, we fine-tuned twelve open-source language models on a corpus of 37,370 retrospective PET reports collected from our institution. All models were trained using the teacher-forcing algorithm, with the report findings and patient information as input and the original clinical impressions as reference. An extra input token encoded the reading physician's identity, allowing models to learn physician-specific reporting styles. To compare the performances of different models, we computed various automatic evaluation metrics and benchmarked them against physician preferences, ultimately selecting PEGASUS as the top LLM. To evaluate its clinical utility, three nuclear medicine physicians assessed the PEGASUS-generated impressions and original clinical impressions across 6 quality dimensions (3-point scales) and an overall utility score (5-point scale). Each physician reviewed 12 of their own reports and 12 reports from other physicians. When physicians assessed LLM impressions generated in their own style, 89% were considered clinically acceptable, with a mean utility score of 4.08/5. On average, physicians rated these personalized impressions as comparable in overall utility to the impressions dictated by other physicians (4.03, P = 0.41). In summary, our study demonstrated that personalized impressions generated by PEGASUS were clinically useful in most cases, highlighting its potential to expedite PET reporting by automatically drafting impressions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.