BACKGROUND: Although uncertainties exist regarding implementation, artificial intelligence-driven generative language models (GLMs) have enormous potential in medicine. Deployment of GLMs could improve patient comprehension of clinical texts and improve low health literacy. OBJECTIVE: The goal of this study is to evaluate the potential of ChatGPT-3.5 and GPT-4 to tailor the complexity of medical information to patient-specific input education level, which is crucial if it is to serve as a tool in addressing low health literacy. METHODS: Input templates related to 2 prevalent chronic diseases-type II diabetes and hypertension-were designed. Each clinical vignette was adjusted for hypothetical patient education levels to evaluate output personalization. To assess the success of a GLM (GPT-3.5 and GPT-4) in tailoring output writing, the readability of pre- and posttransformation outputs were quantified using the Flesch reading ease score (FKRE) and the Flesch-Kincaid grade level (FKGL). RESULTS: Responses (n=80) were generated using GPT-3.5 and GPT-4 across 2 clinical vignettes. For GPT-3.5, FKRE means were 57.75 (SD 4.75), 51.28 (SD 5.14), 32.28 (SD 4.52), and 28.31 (SD 5.22) for 6th grade, 8th grade, high school, and bachelors, respectively; FKGL mean scores were 9.08 (SD 0.90), 10.27 (SD 1.06), 13.4 (SD 0.80), and 13.74 (SD 1.18). GPT-3.5 only aligned with the prespecified education levels at the bachelors degree. Conversely, GPT-4s FKRE mean scores were 74.54 (SD 2.6), 71.25 (SD 4.96), 47.61 (SD 6.13), and 13.71 (SD 5.77), with FKGL mean scores of 6.3 (SD 0.73), 6.7 (SD 1.11), 11.09 (SD 1.26), and 17.03 (SD 1.11) for the same respective education levels. GPT-4 met the target readability for all groups except the 6th-grade FKRE average. Both GLMs produced outputs with statistically significant differences (P<.001; 8th grade P<.001; high school P<.001; bachelors P=.003; FKGL: 6th grade P=.001; 8th grade P<.001; high school P<.001; bachelors P<.001) between mean FKRE and FKGL across input education levels. CONCLUSIONS: GLMs can change the structure and readability of medical text outputs according to input-specified education. However, GLMs categorize input education designation into 3 broad tiers of output readability: easy (6th and 8th grade), medium (high school), and difficult (bachelors degree). This is the first result to suggest that there are broader boundaries in the success of GLMs in output text simplification. Future research must establish how GLMs can reliably personalize medical texts to prespecified education levels to enable a broader impact on health care literacy.

OBJECTIVE: We aimed to elucidate associations between geographic location, size, and ranking of medical schools that orthopaedic surgery residents graduate from and the residencies that they match both pre-COVID-19 and post-COVID-19 pandemic by examining the 2017 to 2022 orthopaedic surgery residency cohorts. METHODS: Demographics were extracted using Doximity Residency Navigator platform, the 2021 US News and World Report, and program websites. Medical schools were classified as large if they had >613 medical students. Postgraduate year 1 (PGY-1) (2021 match) and PGY-2 (2022 match) residents were classified as the COVID-19 cohort. Location was categorized as Northeast, Midwest, South, and West. Chi-square tests, Cohens H value, and descriptive statistics were used for analysis with statistical significance set at p <0.05. RESULTS: Four thousand two hundred forty-three residents from 160 accredited US orthopaedic residency programs (78.4%) were included. Northeastern applicants were most likely to match in the same region (p <0.01), and southern applicants were most likely to match at their home program (p <0.001). Applicants affected by the COVID-19 pandemic did not differ from their predecessors with regards to matching to the same region (p = 0.637) or home program (p = 0.489). Applicants from public medical schools were more likely to match in the same region and at their home program (p <0.001), whereas those from private medical schools were more likely to match at top-ranked residencies (p <0.001). Students from both top 25- and top 50-ranked medical schools were more likely to match at their home program (p <0.01) and attend top 20-ranked residency programs (p <0.0001). CONCLUSION: These results demonstrate significant associations between matched residencies and attended medical schools geographic location, school type, and ranking. During the pandemic, geographic trends were overall unchanged, whereas residents from large or lower-ranked schools were more likely to match at home programs, and those from private or top-ranked schools were less likely to attend top residencies.