Introduction: Generative artificial intelligence (AI) chatbots have recently been posited as potential sources of online medical information for patients making medical decisions. Existing online patient-oriented medical information has repeatedly been shown to be of variable quality and difficult readability. Therefore, we sought to evaluate the content and quality of AI-generated medical information on acute appendicitis. Methods: A modified DISCERN assessment tool, comprising 16 distinct criteria each scored on a 5-point Likert scale (score range 16–80), was used to assess AI-generated content. Readability was determined using the Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL) scores. Four popular chatbots, ChatGPT-3.5 and ChatGPT-4, Bard, and Claude-2, were prompted to generate medical information about appendicitis. Three investigators independently scored the generated texts blinded to the identity of the AI platforms. Results: ChatGPT-3.5, ChatGPT-4, Bard, and Claude-2 had overall mean (SD) quality scores of 60.7 (1.2), 62.0 (1.0), 62.3 (1.2), and 51.3 (2.3), respectively, on a scale of 16–80. Inter-rater reliability was 0.81, 0.75, 0.81, and 0.72, respectively, indicating substantial agreement. Claude-2 demonstrated a significantly lower mean quality score compared to ChatGPT-4 (p = 0.001), ChatGPT-3.5 (p = 0.005), and Bard (p = 0.001). Bard was the only AI platform that listed verifiable sources, while Claude-2 provided fabricated sources. All chatbots except for Claude-2 advised readers to consult a physician if experiencing symptoms. Regarding readability, FKGL and FRE scores of ChatGPT-3.5, ChatGPT-4, Bard, and Claude-2 were 14.6 and 23.8, 11.9 and 33.9, 8.6 and 52.8, 11.0 and 36.6, respectively, indicating difficulty readability at a college reading skill level. Conclusion: AI-generated medical information on appendicitis scored favorably upon quality assessment, but most either fabricated sources or did not provide any altogether. Additionally, overall readability far exceeded recommended levels for the public. Generative AI platforms demonstrate measured potential for patient education and engagement about appendicitis.

There is a paucity of recent literature investigating the sole effect of income level on the treatment and survival of patients with rectal cancer. We analyzed all cases of rectal cancer in the Rectal Cancer PUF of the NCDB from 2010 to 2020. We utilized the Median Income Quartiles 2016–2020 to define our income levels. The two lower quartiles were combined to create a lower income group, with the upper two quartiles creating the higher income group. The total cohort included 201,329 patients, with 116,843 and 84,486 in the higher and lower income groups, respectively. Lower income patients were more often black (17 % vs 6 %), lived farther from the nearest hospital (33.5 miles vs 25.7 miles) despite being more likely to live in urban areas (25 % vs 7 %), and had lower levels of private insurance (36 % vs 49 %). They underwent more APRs (17 % vs 14 %) and had a 13 % higher chance of undergoing an open operation (OR 1.13, CI 1.09–1.17). Higher income patients had a 12 % reduction in 90-day (OR 0.88, 95 % CI 0.82–0.96) and overall mortality (OR 0.88, 95 % CI 0.86–0.89). Clinicians should be aware that lower income patients are often faced with unique challenges that may impact care delivery.