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
BACKGROUND: Patients undergoing hemodialysis are at high risk for Hepatitis C virus infection. Anti-HCV antibody detection is widely used for screening this infection but is not sensitive for window period detection. An ELISA to detect the HCV Core Antigen has recently become available. OBJECTIVES: To investigate the utility of the HCV core Antigen ELISA in the detection of HCV infection in hemodialysis patients and to compare with 3rd generation ELISA validated by real-time PCR. METHODS: Two hundred fifty hemodialysis patients were included in the study. Anti-HCV antibodies and Total HCVcAg was determined by third generation ELISA kits. HCV RNA was determined using Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) and sensitivity of the two assays was confirmed by estimating viral load using real-time PCR. RESULTS: Forty-three out of 250 (17.2%) patients were positive for anti-HCV antibodies and HCVcAg. 13/250 (5.2%) were positive for HCVcAg but anti-HCV negative, which is statistically significant (P<0.05). All 13 were confirmed viremic by in-house nested RT-PCR leading to specificity of 100%. Viral load of 49,258+/-28,682 copies/mL were detected in HCVcAg positive cases in comparison to 239,383+/-107,805 copies/mL in the only anti-HCV positive group (P<0.001). False negative cases for HCVcAg assay accounted for 2/250 (0.8%) in which the viral load was 306+/-461 copies/mL which was significantly lower in comparison to HCVcAg positive group (P<0.001, t-test=9.982). CONCLUSIONS: Total HCVcAg ELISA is an accurate serological marker for early identification of HCV infection, than is possible by currently used serological assay. It will be useful for patients undergoing hemodialysis who have a longer window period due to immunosuppressed state. It is both a cost-effective and a less labor-intensive alternative to PCR, enhancing its clinical utility.