Dr. Google to Dr. ChatGPT: assessing the content and quality of artificial intelligence-generated medical information on appendicitis.

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.

Can Artificial Intelligence Improve the Readability of Patient Education Materials on Aortic Stenosis? A Pilot Study.

INTRODUCTION: The advent of generative artificial intelligence (AI) dialogue platforms and large language models (LLMs) may help facilitate ongoing efforts to improve health literacy. Additionally, recent studies have highlighted inadequate health literacy among patients with cardiac disease. The aim of the present study was to ascertain whether two freely available generative AI dialogue platforms could rewrite online aortic stenosis (AS) patient education materials (PEMs) to meet recommended reading skill levels for the public. METHODS: Online PEMs were gathered from a professional cardiothoracic surgical society and academic institutions in the USA. PEMs were then inputted into two AI-powered LLMs, ChatGPT-3.5 and Bard, with the prompt "translate to 5th-grade reading level". Readability of PEMs before and after AI conversion was measured using the validated Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook Index (SMOGI), and Gunning-Fog Index (GFI) scores. RESULTS: Overall, 21 PEMs on AS were gathered. Original readability measures indicated difficult readability at the 10th-12th grade reading level. ChatGPT-3.5 successfully improved readability across all four measures (p < 0.001) to the approximately 6th-7th grade reading level. Bard successfully improved readability across all measures (p < 0.001) except for SMOGI (p = 0.729) to the approximately 8th-9th grade level. Neither platform generated PEMs written below the recommended 6th-grade reading level. ChatGPT-3.5 demonstrated significantly more favorable post-conversion readability scores, percentage change in readability scores, and conversion time compared to Bard (all p < 0.001). CONCLUSION: AI dialogue platforms can enhance the readability of PEMs for patients with AS but may not fully meet recommended reading skill levels, highlighting potential tools to help strengthen cardiac health literacy in the future.

Effect of preoperative radiation on free flap outcomes for head and neck reconstruction: An updated systematic review and meta-analysis.

BACKGROUND: There is an ongoing debate about whether neoadjuvant radiation therapy is associated with higher rates of postoperative complications after head and neck reconstruction. Herle et al. conducted a systematic review in 2014 of 24 studies, finding higher complication rates in irradiated fields. We sought to perform an exhaustive updated systematic review and meta-analysis. METHODS: We conducted an updated systematic review of the literature, as outlined in our protocol, which was registered on PROSPERO. Databases included Medline, Embase, Cochrane Central, and Web of Science. There were no limits placed on the date range, place of publication, or origin. Exclusion criteria included patients less than 18 years of age, studies with less than 20 participants (n < 20), case studies, skull base reconstructions, and local tissue rearrangements. The combined results of the studies and relative risks (RR) were calculated. RESULTS: 53 studies were included for analysis, including 5,086 free flaps in an irradiated field, and 9,110 free flaps in a non-irradiated field. Of the 53 studies, 21 studies overlapped with those discussed in Herle et al.'s study, with a total of 32 additional studies. Neoadjuvant radiation was found to be a statistically significant risk factor for postoperative complications (RR 1.579, P < 0.001), total flap failure (RR, 1.565; P < 0.001), and fistula (RR, 1.810; P < 0.001). Our work reaffirmed the findings of the Herle et al. CONCLUSION: Preoperative radiation was associated with a statistically significant increase in the risk of total flap failure, fistula, and total complications but not partial flap failure. These high-morbidity complications must be taken into consideration when determining which patients should receive neoadjuvant radiation therapy.

Binding, sliding, and function of cohesin during transcriptional activation.

The ring-shaped cohesin complex orchestrates long-range DNA interactions to mediate sister chromatid cohesion and other aspects of chromosome structure and function. In the yeast Saccharomyces cerevisiae, the complex binds discrete sites along chromosomes, including positions within and around genes. Transcriptional activity redistributes the complex to the 3' ends of convergently oriented gene pairs. Despite the wealth of information about where cohesin binds, little is known about cohesion at individual chromosomal binding sites and how transcription affects cohesion when cohesin complexes redistribute. In this study, we generated extrachromosomal DNA circles to study cohesion in response to transcriptional induction of a model gene, URA3. Functional cohesin complexes loaded onto the locus via a poly(dA:dT) tract in the gene promoter and mediated cohesion before induction. Upon transcription, the fate of these complexes depended on whether the DNA was circular or not. When gene activation occurred before DNA circularization, cohesion was lost. When activation occurred after DNA circularization, cohesion persisted. The presence of a convergently oriented gene also prevented transcription-driven loss of functional cohesin complexes, at least in M phase-arrested cells. The results are consistent with cohesin binding chromatin in a topological embrace and with transcription mobilizing functional complexes by sliding them along DNA.