RESUMO
Background Clear communication of radiological findings is crucial for effective healthcare decision-making. However, radiological reports are often complex with technical terminology, making them challenging for non-radiology healthcare professionals and patients to comprehend. Large language models like ChatGPT (Chat Generative Pre-trained Transformer, by OpenAI, San Francisco, CA) offer a potential solution by translating intricate reports into simplified language. This study aimed to assess the capability of ChatGPT-3.5 in simplifying radiological reports to facilitate improved understanding by healthcare professionals and patients. Materials and methods Nine radiological reports were taken for this study spanning various imaging modalities and medical conditions. These reports were used to ask ChatGPT a set of seven questions (describe the procedure, mention the key findings, express in a simple language, suggestions for further investigation, need of further investigation, grammatical or typing errors, and translation into Hindi). A total of eight radiologists rated the generated content in detailing, summarizing, simplifying content and language, factual correctness, further investigation, grammatical errors, and translation to Hindi. Results The highest score was obtained for detailing the report (94.17% accuracy) and the lowest score was for drawing conclusions for the patient (85% accuracy); case-wise scores were similar (p-value = 0.97). The Hindi translation by ChatGPT was not suitable for patient communication. Conclusion The current free version of ChatGPT-3.5 was able to simplify radiological reports effectively, removing technical jargon while preserving essential diagnostic information. The free version adeptly simplifies radiological reports, enhancing accessibility for healthcare professionals and patients. Hence, it has the potential to enhance medical communication, facilitating informed decision-making by healthcare professionals and patients.
RESUMO
Spectrum of disease involves nasal passage and paranasal sinuses termed as sinonasal disease. CT is superior to other imaging modalities in the assessment of the paranasal sinuses (PNS) pathology. Functional Endoscopic Sinus Surgery (FESS) is the common modality of treatment for diseases of nose and PNS. Coronal CT images closely correlates with the surgical approach. Therefore, CT is the preferred study before FESS. The lateral lamella of cribriform plate (LLCP) is the thinnest bone in the anterior skull base and most vulnerable parts of the skull base for iatrogenic complication during FESS. Therefore, preoperative evaluation of LLCP is importance in a successful FESS. The study focused on the vertical height of the LLCP and the angulation of LLCP with CP. This study was performed retrospectively on CT images of 600 adult subjects. Chi square test and pearson correlation were used for data analysis. Type II is most prevalent type of Kero's type. There is a significant correlation between Kero's classification and Gera's classification. A positive correlation found between the vertical height of LLCP with its angle formed by CP and the correlation was found to be significant. Assessment on LLCP in its 2 aspect both vertical height of LLCP and its sloping with CP certainly gives a map to the surgeon during FESS and to improve the safety profile of the procedure.