Apolipoprotein L1 (ApoL1) predictive genetic testing for kidney disease, and its emerging role in transplantation, remains controversial as it may exacerbate underlying disparities among African Americans (AAs) at increased risk. We conducted an online simulation among AAs (N = 585) about interest in ApoL1 testing and its cofactors, under 2 scenarios: as a potential living donor (PLD), and as a patient awaiting transplantation. Most respondents (61%) expressed high interest in genetic testing as a PLD: age ≥35 years (adjusted odds ratio [aOR], 1.75; 95% confidence interval [CI], 1.18, 2.60, P = .01), AA identity (aOR, 1.67; 95% CI, 1.02, 2.72, P = .04), perceived kidney disease risk following donation (aOR, 1.68; 95% CI, 1.03, 2.73, P = .03), interest in genetics (aOR, 2.89; 95% CI, 1.95, 4.29, P = .001), and genetics self‐efficacy (aOR, 2.38; 95% CI, 1.54, 3.67, P = .001) were positively associated with ApoL1 test interest. If awaiting transplantation, most (89%) believed that ApoL1 testing should be done on AA deceased donors, and older age (aOR, 1.85; 95% CI, 1.03, 3.32, P = .04) and greater interest in genetics (aOR, 2.61; 95% CI, 1.41, 4.81, P = .002) were associated with interest in testing deceased donors. Findings highlight strong support for ApoL1 testing in AAs and the need to examine such opinions among PLDs and transplant patients to enhance patient education efforts.

Background: Artificial intelligence (AI) has the potential to dramatically alter healthcare by enhancing how we diagnosis and treat disease. One promising AI model is ChatGPT, a large general-purpose language model trained by OpenAI. The chat interface has shown robust, human-level performance on several professional and academic benchmarks. We sought to probe its performance and stability over time on surgical case questions. Methods: We evaluated the performance of ChatGPT-4 on two surgical knowledge assessments: the Surgical Council on Resident Education (SCORE) and a second commonly used knowledge assessment, referred to as Data-B. Questions were entered in two formats: open-ended and multiple choice. ChatGPT output were assessed for accuracy and insights by surgeon evaluators. We categorized reasons for model errors and the stability of performance on repeat encounters. Results: A total of 167 SCORE and 112 Data-B questions were presented to the ChatGPT interface. ChatGPT correctly answered 71% and 68% of multiple-choice SCORE and Data-B questions, respectively. For both open-ended and multiple-choice questions, approximately two-thirds of ChatGPT responses contained non-obvious insights. Common reasons for inaccurate responses included: inaccurate information in a complex question (n=16, 36.4%); inaccurate information in fact-based question (n=11, 25.0%); and accurate information with circumstantial discrepancy (n=6, 13.6%). Upon repeat query, the answer selected by ChatGPT varied for 36.4% of inaccurate questions; the response accuracy changed for 6/16 questions. Conclusion: Consistent with prior findings, we demonstrate robust near or above human-level performance of ChatGPT within the surgical domain. Unique to this study, we demonstrate a substantial inconsistency in ChatGPT responses with repeat query. This finding warrants future consideration and presents an opportunity to further train these models to provide safe and consistent responses. Without mental and/or conceptual models, it is unclear whether language models such as ChatGPT would be able to safely assist clinicians in providing care.

BACKGROUND: Surgical risk prediction models traditionally use patient attributes and measures of physiology to generate predictions about postoperative outcomes. However, the surgeon’s assessment of the patient may be a valuable predictor, given the surgeon’s ability to detect and incorporate factors that existing models cannot capture. We compare the predictive utility of surgeon intuition and a risk calculator derived from the American College of Surgeons (ACS) NSQIP. STUDY DESIGN: From January 10, 2021 to January 9, 2022, surgeons were surveyed immediately before performing surgery to assess their perception of a patient’s risk of developing any postoperative complication. Clinical data were abstracted from ACS NSQIP. Both sources of data were independently used to build models to predict the likelihood of a patient experiencing any 30-day postoperative complication as defined by ACS NSQIP. RESULTS: Preoperative surgeon assessment was obtained for 216 patients. NSQIP data were available for 9,182 patients who underwent general surgery (January 1, 2017 to January 9, 2022). A binomial regression model trained on clinical data alone had an area under the receiver operating characteristic curve (AUC) of 0.83 (95% CI 0.80 to 0.85) in predicting any complication. A model trained on only preoperative surgeon intuition had an AUC of 0.70 (95% CI 0.63 to 0.78). A model trained on surgeon intuition and a subset of clinical predictors had an AUC of 0.83 (95% CI 0.77 to 0.89). CONCLUSIONS: Preoperative surgeon intuition alone is an independent predictor of patient outcomes; however, a risk calculator derived from ACS NSQIP is a more robust predictor of postoperative complication. Combining intuition and clinical data did not strengthen prediction.