Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach.

Despite the importance of informed consent in healthcare, the readability and specificity of consent forms often impede patients' comprehension. This study investigates the use of GPT-4 to simplify surgical consent forms and introduces an AI-human expert collaborative approach to validate content appropriateness. Consent forms from multiple institutions were assessed for readability and simplified using GPT-4, with pre- and post-simplification readability metrics compared using nonparametric tests. Independent reviews by medical authors and a malpractice defense attorney were conducted. Finally, GPT-4's potential for generating de novo procedure-specific consent forms was assessed, with forms evaluated using a validated 8-item rubric and expert subspecialty surgeon review. Analysis of 15 academic medical centers' consent forms revealed significant reductions in average reading time, word rarity, and passive sentence frequency (all P < 0.05) following GPT-4-faciliated simplification. Readability improved from an average college freshman to an 8th-grade level (P = 0.004), matching the average American's reading level. Medical and legal sufficiency consistency was confirmed. GPT-4 generated procedure-specific consent forms for five varied surgical procedures at an average 6th-grade reading level. These forms received perfect scores on a standardized consent form rubric and withstood scrutiny upon expert subspeciality surgeon review. This study demonstrates the first AI-human expert collaboration to enhance surgical consent forms, significantly improving readability without sacrificing clinical detail. Our framework could be extended to other patient communication materials, emphasizing clear communication and mitigating disparities related to health literacy barriers.

Demographic Representation in 3 Leading Artificial Intelligence Text-to-Image Generators.

Importance: The progression of artificial intelligence (AI) text-to-image generators raises concerns of perpetuating societal biases, including profession-based stereotypes. Objective: To gauge the demographic accuracy of surgeon representation by 3 prominent AI text-to-image models compared to real-world attending surgeons and trainees. Design, Setting, and Participants: The study used a cross-sectional design, assessing the latest release of 3 leading publicly available AI text-to-image generators. Seven independent reviewers categorized AI-produced images. A total of 2400 images were analyzed, generated across 8 surgical specialties within each model. An additional 1200 images were evaluated based on geographic prompts for 3 countries. The study was conducted in May 2023. The 3 AI text-to-image generators were chosen due to their popularity at the time of this study. The measure of demographic characteristics was provided by the Association of American Medical Colleges subspecialty report, which references the American Medical Association master file for physician demographic characteristics across 50 states. Given changing demographic characteristics in trainees compared to attending surgeons, the decision was made to look into both groups separately. Race (non-White, defined as any race other than non-Hispanic White, and White) and gender (female and male) were assessed to evaluate known societal biases. Exposures: Images were generated using a prompt template, "a photo of the face of a [blank]", with the blank replaced by a surgical specialty. Geographic-based prompting was evaluated by specifying the most populous countries on 3 continents (the US, Nigeria, and China). Main Outcomes and Measures: The study compared representation of female and non-White surgeons in each model with real demographic data using χ2, Fisher exact, and proportion tests. Results: There was a significantly higher mean representation of female (35.8% vs 14.7%; P < .001) and non-White (37.4% vs 22.8%; P < .001) surgeons among trainees than attending surgeons. DALL-E 2 reflected attending surgeons' true demographic data for female surgeons (15.9% vs 14.7%; P = .39) and non-White surgeons (22.6% vs 22.8%; P = .92) but underestimated trainees' representation for both female (15.9% vs 35.8%; P < .001) and non-White (22.6% vs 37.4%; P < .001) surgeons. In contrast, Midjourney and Stable Diffusion had significantly lower representation of images of female (0% and 1.8%, respectively; P < .001) and non-White (0.5% and 0.6%, respectively; P < .001) surgeons than DALL-E 2 or true demographic data. Geographic-based prompting increased non-White surgeon representation but did not alter female representation for all models in prompts specifying Nigeria and China. Conclusion and Relevance: In this study, 2 leading publicly available text-to-image generators amplified societal biases, depicting over 98% surgeons as White and male. While 1 of the models depicted comparable demographic characteristics to real attending surgeons, all 3 models underestimated trainee representation. The study suggests the need for guardrails and robust feedback systems to minimize AI text-to-image generators magnifying stereotypes in professions such as surgery.

Histiocytoid Sweet syndrome harboring an isocitrate dehydrogenase 1 mutation: A case report and retrospective analysis of 29 cases of histiocytoid Sweet syndrome.

Histiocytoid Sweet syndrome (HSS) is a rare histopathologic variant of Sweet syndrome that demonstrates dermal and/or subcutaneous infiltrate with a prominent component of myeloid cells resembling histiocytes. It has been known to occur in association with hematologic neoplasms, including myelodysplastic syndrome (MDS) and acute myelogenous leukemia, but whether it confers an increased risk of such neoplasms is controversial. Here, we describe a case of a HSS that led to the diagnosis of MDS with an isocitrate dehydrogenase 1 (IDH-1) mutation and a corresponding study looking for additional cases of IDH-1 mutations in biopsies of histiocytoid and conventional Sweet syndrome.

Mixing of Injectable Fillers: A National Survey.

BACKGROUND: The mixing of hyaluronic acid or calcium hydroxylapatite fillers with normal saline, plain lidocaine, or lidocaine with epinephrine before injection is a familiar practice among dermatologists. However, the frequency of this practice and rationale behind it has not been well studied. OBJECTIVE: To better elucidate the clinical practice of mixing fillers with other solutions before injecting among dermatologists. METHODS: A survey was electronically distributed to members of the American Society for Dermatologic Surgery. RESULTS: Four hundred seventy-five dermatologists responded to the survey. Thirty-five percent of respondents mix fillers before injection. Solutions used were as follows: plain lidocaine (44%), lidocaine with epinephrine (36%), normal saline (30%), and sterile water (7%). Respondents mix filler for the following reasons: to decrease viscosity (40%), increase anesthesia (30%), decrease swelling (17%), and increase volume (13%). CONCLUSION: Despite the lack of evidence, more than one-third of dermatologists surveyed mix fillers with other solutions before injection. Plain lidocaine is most commonly used. The top reason for mixing fillers is to decrease viscosity and facilitate ease of injection. More scientific data are needed to support this practice and better understand the biophysical changes that occur when mixing fillers with other solutions.