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OBJECTIVE: Radiology reporting is an essential component of clinical diagnosis and decision-making. With the advent of advanced artificial intelligence (AI) models like GPT-4 (Generative Pre-trained Transformer 4), there is growing interest in evaluating their potential for optimizing or generating radiology reports. This study aimed to compare the quality and content of radiologist-generated and GPT-4 AI-generated radiology reports. METHODS: A comparative study design was employed in the study, where a total of 100 anonymized radiology reports were randomly selected and analyzed. Each report was processed by GPT-4, resulting in the generation of a corresponding AI-generated report. Quantitative and qualitative analysis techniques were utilized to assess similarities and differences between the two sets of reports. RESULTS: The AI-generated reports showed comparable quality to radiologist-generated reports in most categories. Significant differences were observed in clarity (p = 0.027), ease of understanding (p = 0.023), and structure (p = 0.050), favoring the AI-generated reports. AI-generated reports were more concise, with 34.53 fewer words and 174.22 fewer characters on average, but had greater variability in sentence length. Content similarity was high, with an average Cosine Similarity of 0.85, Sequence Matcher Similarity of 0.52, BLEU Score of 0.5008, and BERTScore F1 of 0.8775. CONCLUSION: The results of this proof-of-concept study suggest that GPT-4 can be a reliable tool for generating standardized radiology reports, offering potential benefits such as improved efficiency, better communication, and simplified data extraction and analysis. However, limitations and ethical implications must be addressed to ensure the safe and effective implementation of this technology in clinical practice. CLINICAL RELEVANCE STATEMENT: The findings of this study suggest that GPT-4 (Generative Pre-trained Transformer 4), an advanced AI model, has the potential to significantly contribute to the standardization and optimization of radiology reporting, offering improved efficiency and communication in clinical practice. KEY POINTS: ⢠Large language model-generated radiology reports exhibited high content similarity and moderate structural resemblance to radiologist-generated reports. ⢠Performance metrics highlighted the strong matching of word selection and order, as well as high semantic similarity between AI and radiologist-generated reports. ⢠Large language model demonstrated potential for generating standardized radiology reports, improving efficiency and communication in clinical settings.
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INTRODUCTION AND OBJECTIVE: Von Hippel-Lindau (VHL) is a hereditary cancer syndrome characterized by bilateral, multifocal renal masses. The cumulative impact of extirpative surgery can depreciate renal function and render patients anephric. In the larger end-stage renal disease population, renal transplant offers both excellent quality of life and functional renal replacement. This case control study aims to examine and compare oncologic and functional outcomes of patients who have undergone renal transplant as renal replacement therapy (RRT) to those who remain anephric. METHODS: Patient charts were retrospectively reviewed of patients with germline testing confirmed VHL between 1980 and 2022 for transplant, all prior surgical history (within and outside the NCI), renal function and graft outcomes. Overall survival (OS) was determined from years after radical nephrectomy, and graft time was defined as years of graft function from initial transplant until failure or patient death. Graft survival was determined as time between transplant(s) to last follow up. Kaplan-Meier analysis was conducted to compare graft times of anephric VHL patients to those with transplanted kidneys. RESULTS: A total of 23 VHLD patients were identified as either anephric or candidates for transplant. Out of this cohort, 11 total VHLD received 12 total kidney grafts. Median wait time from nephrectomy to transplant was 22.6 months (IQR: 1.02-40.25 months). Median age at transplant was 32 years (IQR: 23-54 years). OS at 5 and 10 years of anephric patients who did not receive a transplant was 33% and 16.7%, respectively. OS rates of the transplant cohort at 10, 15, and 20 years were 91%, 78%, and 58% years, respectively. Median graft time was 161 months (IQR: 56-214 months). Graft survival at 10, 15, and 20 years was 69.8%, 69.8%, and 26.2%, respectively. CONCLUSIONS: We demonstrate that transplant recipients have decreased mortality with no difference in cancer recurrence compared to those who do not receive renal transplant for RRT. This data can aid in informing providers of the optimal window for early RRT planning in VHL, while also improving patient counseling.
