Garces-Delgado, Estefany

Abstract Introduction Large language models (LLMs), such as ChatGPT-4, have demonstrated promising potential as diagnostic tools across various medical disciplines. In sleep medicine, artificial intelligence tools are already used to analyze physiological sleep data and are being studied for their potential in phenotyping, endotyping, and predicting treatment responses. However, the effectiveness of LLMs in accurately diagnosing sleep disorders based on clinical history has not yet been studied. This study evaluates ChatGPT-4’s diagnostic performance using clinical vignettes. Methods Nineteen clinical cases were selected from the Case Book of Sleep Medicine, Third Edition (AASM, 2019). Each case included patient history, examination findings, and test results. The vignettes were input into ChatGPT-4 using the following standardized prompt: “Based on the provided case details, generate the top 5 differential diagnoses and identify the most likely final diagnosis: (copy and paste clinical vignette).” The model was tasked with generating both differential diagnoses and a final diagnosis for each case. The AI’s results were compared to reference diagnoses from the case book. Differential diagnoses were measured as the number of matches, and accuracy was reported as a percentage. Final diagnoses were scored as 0 (no match), 1 (partial match), or 2 (full match). Results The mean number of AI-generated differential diagnoses matching the AASM case differential diagnoses was 2.79 ± 0.71 (95% CI: 2.45–3.13). The mean accuracy percentage for differential diagnoses was 63.27% ± 15.61% (95% CI: 55.75%–70.79%), with scores ranging from 33.33% to 100%. For final diagnoses, ChatGPT-4 scored a total of 30 out of a possible 38, with a mean score was 1.58 ± 0.61 (95% CI: 1.29–1.87) out of 2, with 74% of cases achieving a full match. Performance was higher in cases with fewer differential diagnoses, whereas accuracy decreased in more complex cases. Conclusion ChatGPT-4 showed moderate to high accuracy in generating both differential and final diagnoses for sleep disorders. These findings suggest that AI could become a valuable clinical decision-support tool in sleep medicine. However, its inconsistent performance in complex cases highlights the need for further refinement and clinical testing. Support (if any)

The introduction of robotics in living donor liver transplantation has been revolutionary. We aimed to examine the safety of robotic living donor right hepatectomy (RLDRH) compared to open (ODRH) and laparoscopic (LADRH) approaches. A systematic review was carried out in Medline and six additional databases following PRISMA guidelines. Data on morbidity, postoperative liver function, and pain in donors and recipients were extracted from studies comparing RLDRH, ODRH, and LADRH published up to September 2020; PROSPERO (CRD42020214313). Dichotomous variables were pooled as risk ratios and continuous variables as weighted mean differences. Four studies with a total of 517 patients were included. In living donors, the postoperative total bilirubin level (MD: −0.7 95%CI −1.0, −0.4), length of hospital stay (MD: −0.8 95%CI −1.4, −0.3), Clavien–Dindo complications I–II (RR: 0.5 95%CI 0.2, 0.9), and pain score at day > 3 (MD: −0.6 95%CI −1.6, 0.4) were lower following RLDRH compared to ODRH. Furthermore, the pain score at day > 3 (MD: −0.4 95%CI −0.8, −0.09) was lower after RLDRH when compared to LADRH. In recipients, the postoperative AST level was lower (MD: −0.5 95%CI −0.9, −0.1) following RLDRH compared to ODRH. Moreover, the length of stay (MD: −6.4 95%CI −11.3, −1.5) was lower after RLDRH when compared to LADRH. In summary, we identified low- to unclear-quality evidence that RLDRH seems to be safe and feasible for adult living donor liver transplantation compared to the conventional approaches. No postoperative deaths were reported.