PRO-READ IR:Enhanced PROcedural Information READability for Patient-Centered Care in Interventional Radiology with Large Language Models.

PURPOSE: To evaluate the extent to which GPT-4 can educate patients by generating easily understandable information about the most common Interventional Radiology(IR) procedures. MATERIALS AND METHODS: We reviewed 10 IR procedures and prepared prompts for GPT-4 to provide patient educational instructions about each procedure in layman's terms. The instructions were then evaluated by 4-clinical physicians and 9-nonclinical assessors to determine their clinical appropriateness, understandability and clarity utilizing a survey. A grade-level readability assessment was performed using validated metrics to evaluate accessibility to a wide patient population. The same procedures were also evaluated from the patient instructions available at radiologyinfo.org and compared to GPT-generated instructions utilizing a paired t-test. RESULTS: Evaluation by 4-clinical physicians shows that 9 GPT-generated instructions were fully appropriate, whereas arterial embolization instructions was somewhat appropriate. Evaluation by 9-nonclinical assessors shows that paracentesis, dialysis-catheter-placement, thrombectomy, ultrasound-guided-biopsy, and nephrostomy-tube instructions, were rated excellent by 57%, and good by 43%. The arterial-embolization and biliary-drain instructions were rated excellent by 28.6% and good by 71.4%. In contrast, thoracentesis, port-placement, and CT-guided-biopsy instructions received 43% excellent, 43% good, and 14% fair. The readability assessment across all procedural instructions showed a better Flesch-Kincaid mean grade of GPT-4 instructions compared to radiologyinfo.org(7.8±0.87 vs 9.6±0.83,p=0.007) indicating excellent readability at 7-8th grade level compared to 9-10th grade. Additionally there was a lower Gunning-Fog mean Index(10.4±1.2 vs. 12.7±0.93,p=0.006), and higher Flesch Reading Ease mean score (69.4±4.8 vs 51.3±3.9,p=0.0001) indicating better readability. CONCLUSION: IR procedural instructions generated by GPT-4 can aid in improving health literacy and patient-centered care in IR by generating easily understandable explanations.

Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions.

CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.

Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions.

Artificial Intelligence (AI) is a branch of computer science that utilizes optimization, probabilistic and statistical approaches to analyze and make predictions based on a vast amount of data. In recent years, AI has revolutionized the field of oncology and spearheaded novel approaches in the management of various cancers, including colorectal cancer (CRC). Notably, the applications of AI to diagnose, prognosticate, and predict response to therapy in CRC, is gaining traction and proving to be promising. There have also been several advancements in AI technologies to help predict metastases in CRC and in Computer-Aided Detection (CAD) Systems to improve miss rates for colorectal neoplasia. This article provides a comprehensive review of the role of AI in predicting risk, prognosis, and response to therapies among patients with CRC.

Spontaneous pneumomediastinum, pneumothorax and subcutaneous emphysema in COVID-19 pneumonia: a rare case and literature review.

Spontaneous pneumomediastinum (SPM) and pneumothorax (PNX) unrelated to positive pressure ventilation has been recently reported as an unusual complication in cases of severe COVID-19 pneumonia. The presumed pathophysiological mechanism is diffuse alveolar injury leading to alveolar rupture and air leak. We present a case of COVID-19 pneumonia complicated on day 13 post admission by SPM, PNX and subcutaneous emphysema in a patient with no identifiable risk factors for such complication. The patient received medical treatment for his COVID-19 infection without the use of an invasive or non-invasive ventilator. Moreover, he is a non-smoker with no lung comorbidities and never reported a cough. He was eventually discharged home in stable condition. A comprehensive literature review revealed 15 cases of SPM developing in patients with COVID-19 pneumonia.