OUP accepted manuscript

Revolutionizing Cardiology With Words: Unveiling the Impact of Large Language Models in Medical Science Writing

Large language models (LLMs) are a unique form of machine learning that facilitates inputs of unstructured text/numerical information for meaningful interpretation and prediction. Recently, LLMs have become commercialized, allowing the average person to access these incredibly powerful tools. Early adopters focused on LLM use in performing logical tasks, including-but not limited to-generating titles, identifying key words, summarizing text, initial editing of scientific work, improving statistical protocols, and performing statistical analysis. More recently, LLMs have been expanded to clinical practice and academia to perform higher cognitive and creative tasks. LLMs provide personalized assistance in learning, facilitate the management of electronic medical records, and offer valuable insights into clinical decision making in cardiology. They enhance patient education by explaining intricate medical conditions in lay terms, have a vast library of knowledge to help clinicians expedite administrative tasks, provide useful feedback regarding content of scientific writing, and assist in the peer-review process. Despite their impressive capabilities, LLMs are not without limitations. They are susceptible to generating incorrect or plagiarized content, face challenges in handling tasks without detailed prompts, and lack originality. These limitations underscore the importance of human overview in using LLMs in medical science and clinical practice. As LLMs continue to evolve, addressing these challenges will be crucial in maximizing their potential benefits while mitigating risks. This review explores the functions, opportunities, and constraints of LLMs, with a focus on their impact on cardiology, illustrating both the transformative power and the boundaries of current technology in medicine.

PET Radiotracers in Atherosclerosis: A Review

Traditional atherosclerosis imaging modalities are limited to late stages of disease, prior to which patients are frequently asymptomatic. Positron emission tomography (PET) imaging allows for the visualization of metabolic processes underscoring disease progression via radioactive tracer, allowing earlier-stage disease to be identified. 2-deoxy-2-[fluorine-18]fluoro-D-glucose (18F-FDG) uptake largely reflects the metabolic activity of macrophages, but is unspecific and limited in its utility. By detecting areas of microcalcification, 18F-Sodium Fluoride (18F-NaF) uptake also provides insight into atherosclerosis pathogenesis. Gallium-68 DOTA-0-Tyr3-Octreotate (68Ga-DOTATATE) PET has also shown potential in identifying vulnerable atherosclerotic plaques with high somatostatin receptor expression. Finally, 11-carbon (11C)-choline and 18F-fluoromethylcholine (FMCH) tracers may identify high-risk atherosclerotic plaques by detecting increased choline metabolism. Together, these radiotracers quantify disease burden, assess treatment efficacy, and stratify risk for adverse cardiac events.

The utility of beta-hydroxybutyrate in detecting myocardial glucose uptake suppression in patients undergoing inflammatory [18F]-FDG PET studies

PURPOSE

We evaluated whether serum beta-hydroxybutyrate (BHB) can identify adequate suppression of the left ventricle (LV) among patients undergoing [18F]-fluorodeoxyglucose positron emission tomography ([18F]-FDG PET) for cardiac inflammatory/infectious studies.

METHODS

Consecutive patients who underwent [18F]-FDG PET imaging were included. Serum BHB levels were measured in all patients on the day of imaging prior to injecting [18F]-FDG. Myocardial [18F]-FDG suppression was defined if [18F]-FDG uptake in the walls of myocardium, measured using standardized uptake values (SUV), was lower than the blood pool. The optimal threshold of BHB to identify myocardial suppression was based on receiver operating characteristics (ROC) in a random 30% sample of the study population (derivation cohort) and tested in the remaining 70% of sample (validation cohort).

RESULTS

A total of 256 images from 220 patients were included. Patients with sufficient LV suppression had significantly higher BHB levels compared to those with non-suppressed myocardium (median (IQR) BHB 0.6 (0.3-0.8) vs. 0.2 (0.2-0.3) mmol/l, p < 0.001, respectively). BHB level ≥ 0.335 mmol/l had a sensitivity of 84.90% and a specificity of 92.60% to identify adequate LV suppression in the validation cohort. All patients (100%) with BHB ≥ 0.41 mmol/l had adequate myocardial suppression compared to 29.63% of patients with BHB ≤ 0.20 mmol/l.

CONCLUSION

Serum BHB level can be used at the point of care to identify sufficient LV suppression in patients undergoing [18F]-FDG PET cardiac inflammatory/infectious studies. Central illustration (image to the right) shows representative cases of patient images and BHB and, in the image to the left, shows the sensitivity and specificity to identify left myocardial suppression using BHB in validation group.