Accuracy, readability, and understandability of large language models for prostate cancer information to the public.

BACKGROUND: Generative Pretrained Model (GPT) chatbots have gained popularity since the public release of ChatGPT. Studies have evaluated the ability of different GPT models to provide information about medical conditions. To date, no study has assessed the quality of ChatGPT outputs to prostate cancer related questions from both the physician and public perspective while optimizing outputs for patient consumption. METHODS: Nine prostate cancer-related questions, identified through Google Trends (Global), were categorized into diagnosis, treatment, and postoperative follow-up. These questions were processed using ChatGPT 3.5, and the responses were recorded. Subsequently, these responses were re-inputted into ChatGPT to create simplified summaries understandable at a sixth-grade level. Readability of both the original ChatGPT responses and the layperson summaries was evaluated using validated readability tools. A survey was conducted among urology providers (urologists and urologists in training) to rate the original ChatGPT responses for accuracy, completeness, and clarity using a 5-point Likert scale. Furthermore, two independent reviewers evaluated the layperson summaries on correctness trifecta: accuracy, completeness, and decision-making sufficiency. Public assessment of the simplified summaries' clarity and understandability was carried out through Amazon Mechanical Turk (MTurk). Participants rated the clarity and demonstrated their understanding through a multiple-choice question. RESULTS: GPT-generated output was deemed correct by 71.7% to 94.3% of raters (36 urologists, 17 urology residents) across 9 scenarios. GPT-generated simplified layperson summaries of this output was rated as accurate in 8 of 9 (88.9%) scenarios and sufficient for a patient to make a decision in 8 of 9 (88.9%) scenarios. Mean readability of layperson summaries was higher than original GPT outputs ([original ChatGPT v. simplified ChatGPT, mean (SD), p-value] Flesch Reading Ease: 36.5(9.1) v. 70.2(11.2), <0.0001; Gunning Fog: 15.8(1.7) v. 9.5(2.0), p < 0.0001; Flesch Grade Level: 12.8(1.2) v. 7.4(1.7), p < 0.0001; Coleman Liau: 13.7(2.1) v. 8.6(2.4), 0.0002; Smog index: 11.8(1.2) v. 6.7(1.8), <0.0001; Automated Readability Index: 13.1(1.4) v. 7.5(2.1), p < 0.0001). MTurk workers (n = 514) rated the layperson summaries as correct (89.5-95.7%) and correctly understood the content (63.0-87.4%). CONCLUSION: GPT shows promise for correct patient education for prostate cancer-related contents, but the technology is not designed for delivering patients information. Prompting the model to respond with accuracy, completeness, clarity and readability may enhance its utility when used for GPT-powered medical chatbots.

Open versus Robot-Assisted Radical Cystectomy for the Treatment of pT4a Bladder Cancer: Comparison of Perioperative Outcomes.

We compared the perioperative outcomes of open (ORC) vs. robot-assisted (RARC) radical cystectomy in the treatment of pT4a MIBC. In total, 212 patients underwent ORC (102 patients, Group A) vs. RARC (110 patients, Group B) for pT4a bladder cancer. Patients were prospectively followed and retrospectively reviewed. We assessed operative time, estimated blood loss (EBL), intraoperative and postoperative complications, length of stay, transfusion rate, and oncological outcomes. Preoperative features were comparable. The mean operative time was 232.8 vs. 189.2 min (p = 0.04), and mean EBL was 832.8 vs. 523.7 mL in Group A vs. B (p = 0.04). An intraoperative transfusion was performed in 32 (31.4%) vs. 11 (10.0%) cases during ORC vs. RARC (p = 0.03). The intraoperative complications rate was comparable. The mean length of stay was shorter after RARC (12.6 vs. 7.2 days, p = 0.02). Postoperative transfusions were performed in 36 (35.3%) vs. 13 (11.8%) cases (p = 0.03), and postoperative complications occurred in 37 (36.3%) vs. 29 (26.4%) patients in Groups A vs. B (p = 0.05). The positive surgical margin (PSM) rate was lower after RARC. No differences were recorded according to the oncological outcomes. ORC and RARC are feasible treatments for the management of pT4a bladder tumors. Minimally invasive surgery provides shorter operative time, bleeding, transfusion rate, postoperative complications, length of stay, and PSM rate.

Study protocol for the Intraoperative Complications Assessment and Reporting with Universal Standards (ICARUS) global cross-specialty surveys and consensus.

Annually, about 300 million surgeries lead to significant intraoperative adverse events (iAEs), impacting patients and surgeons. Their full extent is underestimated due to flawed assessment and reporting methods. Inconsistent adoption of new grading systems and a lack of standardization, along with litigation concerns, contribute to underreporting. Only half of relevant journals provide guidelines on reporting these events, with a lack of standards in surgical literature. To address these issues, the Intraoperative Complications Assessment and Reporting with Universal Standard (ICARUS) Global Surgical Collaboration was established in 2022. The initiative involves conducting global surveys and a Delphi consensus to understand the barriers for poor reporting of iAEs, validate shared criteria for reporting, define iAEs according to surgical procedures, evaluate the existing grading systems' reliability, and identify strategies for enhancing the collection, reporting, and management of iAEs. Invitation to participate are extended to all the surgical specialties, interventional cardiology, interventional radiology, OR Staffs and anesthesiology. This effort represents an essential step towards improved patient safety and the well-being of healthcare professionals in the surgical field.

Generative artificial intelligence in surgery.

Generative artificial intelligence is able to collect, extract, digest, and generate information in an understandable way for humans. As the first surgical applications of generative artificial intelligence are applied, this perspective paper aims to provide a comprehensive overview of current applications and future perspectives for the application of generative artificial intelligence in surgery, from preoperative planning to training. Generative artificial intelligence can be used before surgery for planning and decision support by extracting patient information and providing patients with information and simulation regarding the procedure. Intraoperatively, generative artificial intelligence can document data that is normally not captured as intraoperative adverse events or provide information to help decision-making. Postoperatively, GAIs can help with patient discharge and follow-up. The ability to provide real-time feedback and store it for later review is an important capability of GAIs. GAI applications are emerging as highly specialized, task-specific tools for tasks such as data extraction, synthesis, presentation, and communication within the realm of surgery. GAIs have the potential to play a pivotal role in facilitating interaction between surgeons and artificial intelligence.

Publishers' and journals' instructions to authors on use of generative artificial intelligence in academic and scientific publishing: bibliometric analysis.

OBJECTIVES: To determine the extent and content of academic publishers' and scientific journals' guidance for authors on the use of generative artificial intelligence (GAI). DESIGN: Cross sectional, bibliometric study. SETTING: Websites of academic publishers and scientific journals, screened on 19-20 May 2023, with the search updated on 8-9 October 2023. PARTICIPANTS: Top 100 largest academic publishers and top 100 highly ranked scientific journals, regardless of subject, language, or country of origin. Publishers were identified by the total number of journals in their portfolio, and journals were identified through the Scimago journal rank using the Hirsch index (H index) as an indicator of journal productivity and impact. MAIN OUTCOME MEASURES: The primary outcomes were the content of GAI guidelines listed on the websites of the top 100 academic publishers and scientific journals, and the consistency of guidance between the publishers and their affiliated journals. RESULTS: Among the top 100 largest publishers, 24% provided guidance on the use of GAI, of which 15 (63%) were among the top 25 publishers. Among the top 100 highly ranked journals, 87% provided guidance on GAI. Of the publishers and journals with guidelines, the inclusion of GAI as an author was prohibited in 96% and 98%, respectively. Only one journal (1%) explicitly prohibited the use of GAI in the generation of a manuscript, and two (8%) publishers and 19 (22%) journals indicated that their guidelines exclusively applied to the writing process. When disclosing the use of GAI, 75% of publishers and 43% of journals included specific disclosure criteria. Where to disclose the use of GAI varied, including in the methods or acknowledgments, in the cover letter, or in a new section. Variability was also found in how to access GAI guidelines shared between journals and publishers. GAI guidelines in 12 journals directly conflicted with those developed by the publishers. The guidelines developed by top medical journals were broadly similar to those of academic journals. CONCLUSIONS: Guidelines by some top publishers and journals on the use of GAI by authors are lacking. Among those that provided guidelines, the allowable uses of GAI and how it should be disclosed varied substantially, with this heterogeneity persisting in some instances among affiliated publishers and journals. Lack of standardization places a burden on authors and could limit the effectiveness of the regulations. As GAI continues to grow in popularity, standardized guidelines to protect the integrity of scientific output are needed.

Artificial intelligence and urology: ethical considerations for urologists and patients.

The use of artificial intelligence (AI) in medicine and in urology specifically has increased over the past few years, during which time it has enabled optimization of patient workflow, increased diagnostic accuracy and enhanced computer analysis of radiological and pathological images. However, before further use of AI is undertaken, possible ethical issues need to be evaluated to improve understanding of this technology and to protect patients and providers. Possible ethical issues that require consideration when applying AI in clinical practice include patient safety, cybersecurity, transparency and interpretability of the data, inclusivity and equity, fostering responsibility and accountability, and the preservation of providers' decision-making and autonomy. Ethical principles for the application of AI to health care and in urology are proposed to guide urologists, patients and regulators to improve use of AI technologies and guide policy-making.

A nomogram to predict the absence of clinically significant prostate cancer in males with negative MRI

PURPOSE: To create a nomogram to predict the absence of clinically significant prostate cancer (CSPCa) in males with non-suspicion multiparametric magnetic resonance imaging (mpMRI) undergoing prostate biopsy (PBx). MATERIALS AND METHODS: We identified consecutive patients who underwent 3T mpMRI followed by PBx for suspicion of PCa or surveillance follow-up. All patients had Prostate Imaging Reporting and Data System score 1-2 (negative mpMRI). CSPCa was defined as Grade Group ≥2. Multivariate logistic regression analysis was performed via backward elimination. Discrimination was evaluated with area under the receiver operating characteristic (AUROC). Internal validation with 1,000x bootstrapping for estimating the optimism corrected AUROC. RESULTS: Total 327 patients met inclusion criteria. The median (IQR) age and PSA density (PSAD) were 64 years (58-70) and 0.10 ng/mL2 (0.07-0.15), respectively. Biopsy history was as follows: 117 (36%) males were PBx-naive, 130 (40%) had previous negative PBx and 80 (24%) had previous positive PBx. The majority were White (65%); 6% of males self-reported Black. Overall, 44 (13%) patients were diagnosed with CSPCa on PBx. Black race, history of previous negative PBx and PSAD ≥0.15ng/mL2 were independent predictors for CSPCa on PBx and were included in the nomogram. The AUROC of the nomogram was 0.78 and the optimism corrected AUROC was 0.75. CONCLUSIONS: Our nomogram facilitates evaluating individual probability of CSPCa on PBx in males with PIRADS 1-2 mpMRI and may be used to identify those in whom PBx may be safely avoided. Black males have increased risk of CSPCa on PBx, even in the setting of PIRADS 1-2 mpMRI.

Awareness and Use of ChatGPT and Large Language Models: A Prospective Cross-sectional Global Survey in Urology.

BACKGROUND: Since its release in November 2022, ChatGPT has captivated society and shown potential for various aspects of health care. OBJECTIVE: To investigate potential use of ChatGPT, a large language model (LLM), in urology by gathering opinions from urologists worldwide. DESIGN, SETTING, AND PARTICIPANTS: An open web-based survey was distributed via social media and e-mail chains to urologists between April 20, 2023 and May 5, 2023. Participants were asked to answer questions related to their knowledge and experience with artificial intelligence, as well as their opinions of potential use of ChatGPT/LLMs in research and clinical practice. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Data are reported as the mean and standard deviation for continuous variables, and the frequency and percentage for categorical variables. Charts and tables are used as appropriate, with descriptions of the chart types and the measures used. The data are reported in accordance with the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). RESULTS AND LIMITATIONS: A total of 456 individuals completed the survey (64% completion rate). Nearly half (47.7%) reported that they use ChatGPT/LLMs in their academic practice, with fewer using the technology in clinical practice (19.8%). More than half (62.2%) believe there are potential ethical concerns when using ChatGPT for scientific or academic writing, and 53% reported that they have experienced limitations when using ChatGPT in academic practice. CONCLUSIONS: Urologists recognise the potential of ChatGPT/LLMs in research but have concerns regarding ethics and patient acceptance. There is a desire for regulations and guidelines to ensure appropriate use. In addition, measures should be taken to establish rules and guidelines to maximise safety and efficiency when using this novel technology. PATIENT SUMMARY: A survey asked 456 urologists from around the world about using an artificial intelligence tool called ChatGPT in their work. Almost half of them use ChatGPT for research, but not many use it for patients care. The resonders think ChatGPT could be helpful, but they worry about problems like ethics and want rules to make sure it's used safely.

Gender disparities among publications within international sexual medicine urology journals and the impact of blinding in the review process.

BACKGROUND: While female urologists are known to publish at less frequency than their male peers, The Journal of Sexual Medicine was reported to have among the highest growth in female authorship from 2002 to 2020 in urology journals. AIM: We sought to assess the frequency of female authorship in sexual medicine journals worldwide and the factors that affect this, including the blinded/unblinded review process. METHODS: Eleven sexual medicine journals were assessed for geographic location, peer review method, and SCImago Journal Rank citation index (a metric of citation frequency and prestige). Journals were grouped into top, middle, and bottom quartiles based on metric score. Web of Science was used to access the publications' first, second, last, and corresponding authors from the past 5 years. An internet search or Gender-API.com was used to determine the gender identities of authors. Univariate and multivariable logistic regression models were performed. OUTCOMES: Outcomes included the likelihood of female authorship (first, second, last, and corresponding) based on journal location and ranking, the clustering of female authors, the journal's peer review process, and the frequency of female editorial board members. RESULTS: Overall, 8938 publications were identified. Women represented 30.7%, 31.3%, 21.3%, and 18.7% of the first, second, last, and corresponding authors, respectively; gender was unable to be assessed for 2.6%, 17.2%, 7.3%, and 2.7%. On univariate analysis, journals from North America, in the top quartile, and with a double-blind review process were more likely to have female authors (P < .001). On multivariate analysis, articles were more likely to have a female first author if they had a double-blind peer review process (odds ratio [OR], 1.20; 95% CI, 1.02-1.40), a female second author (OR, 2.54; 95% CI, 2.26-2.85), or a female corresponding author (OR, 7.80; 95% CI, 6.69-9.10). CLINICAL IMPLICATIONS: Gender-concordant mentoring and universal double-blind manuscript review processes may minimize the impact of gender bias and increase female authorship rates, in turn producing more diverse research. STRENGTHS AND LIMITATIONS: This is the first study assessing female authorship in sexual medicine journals. Limitations include not assessing every author listed on articles and being unable to determine gender identities for some authors. CONCLUSION: Female authorship rates are higher than reported rates of practicing female urologists but still lower than their male peers. Female authors were more likely to be published in journals with double-blind peer review processes and when publishing with additional female authors.

Framework for a living systematic review and meta-analysis for the surgical treatment of bladder cancer: introducing EVIglance to urology.

Background: Knowledge of current and ongoing studies is critical for identifying research gaps and enabling evidence-based decisions for individualized treatment. However, the increasing number of scientific publications poses challenges for healthcare providers and patients in all medical fields to stay updated with the latest evidence. To overcome these barriers, we aim to develop a living systematic review and open-access online evidence map of surgical therapy for bladder cancer (BC), including meta-analyses. Methods: Following the guidelines provided in the Cochrane Handbook for Systematic Reviews of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement, a systematic literature search on uro-oncological therapy in BC will be performed across various literature databases. Within the scope of a meta-analysis and living systematic review, relevant randomized controlled trials will be identified. Data extraction and quantitative analysis will be conducted, along with a critical appraisal of the quality and risk of bias of each study. The available research evidence will be entered into an open-access framework (www.evidencemap.surgery) and will also be accessible via the EVIglance app. Regular semi-automatic updates will enable the implementation of a real-living review concept and facilitate resource-efficient screening. Discussion: A regularly updated evidence map provides professionals and patients with an open-access knowledge base on the current state of research, allowing for decision-making based on recent evidence. It will help identify an oversupply of evidence, thus avoiding redundant work. Furthermore, by identifying research gaps, new hypotheses can be formulated more precisely, enabling planning, determination of sample size, and definition of endpoints for future trials.

The Impact of Telemedicine in Reducing the Carbon Footprint in Health Care: A Systematic Review and Cumulative Analysis of 68 Million Clinical Consultations.

CONTEXT: Carbon footprint (CF) has emerged as an important factor when assessing health care interventions. OBJECTIVE: To investigate the reduction in CF for patients utilizing telemedicine. EVIDENCE ACQUISITION: The PubMed, Scopus, and Web of Science databases were queried for studies describing telemedicine consultation and reporting on carbon emissions saved and the carbon emissions of telemedicine devices as primary outcomes, and travel distance and time and cost savings and safety as secondary outcomes. Outcomes were tabulated and calculated per consultation. Carbon emissions and travel distances were also calculated for each total study cohort. Risk of bias was assessed using the Newcastle-Ottawa scale, and the Oxford level of evidence was determined. EVIDENCE SYNTHESIS: A total of 48 studies met the inclusion criteria, covering 68 465 481 telemedicine consultations and savings of 691 825 tons of CO2 emissions and 3 318 464 047 km of travel distance. Carbon assessment was mostly reported as the estimated distance saved using a conversion factor. Medical specialties used telemedicine to connect specialists with patients at home (n = 25) or at a local center (n = 6). Surgical specialties used telemedicine for virtual preoperative assessment (n = 9), follow-up (n = 4), and general consultation (n = 4). The savings per consultation were 21.9-632.17 min and $1.85-$325. More studies focused on the COVID-19 time frame (n = 33) than before the pandemic (n = 15). The studies are limited by calculations, mostly for the travel distance for carbon savings, and appropriate follow-up to analyze the real impact on travel and appointments. CONCLUSIONS: Telemedicine reduces the CF of the health care sector. Expanding the use of telemedicine and educating providers and patients could further decrease CO2 emissions and save both money and time. PATIENT SUMMARY: We reviewed 48 studies on the use of telemedicine. We found that people used their cars less and saved time and money, as well as CO2 emissions, if they used teleconsultations. Some studies only looked at how much CO2 from driving was saved, so there might be more to learn about the benefits of teleconsultations. The use of online doctor appointments is not only good for our planet but also helps patients in saving time and money. This review is registered on the PROSPERO database for systematic reviews (CRD42023456839).

Patients' Trust in Artificial Intelligence-based Decision-making for Localized Prostate Cancer: Results from a Prospective Trial.

BACKGROUND: Artificial intelligence (AI) has the potential to enhance diagnostic accuracy and improve treatment outcomes. However, AI integration into clinical workflows and patient perspectives remain unclear. OBJECTIVE: To determine patients' trust in AI and their perception of urologists relying on AI, and future diagnostic and therapeutic AI applications for patients. DESIGN, SETTING, AND PARTICIPANTS: A prospective trial was conducted involving patients who received diagnostic or therapeutic interventions for prostate cancer (PC). INTERVENTION: Patients were asked to complete a survey before magnetic resonance imaging, prostate biopsy, or radical prostatectomy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary outcome was patient trust in AI. Secondary outcomes were the choice of AI in treatment settings and traits attributed to AI and urologists. RESULTS AND LIMITATIONS: Data for 466 patients were analyzed. The cumulative affinity for technology was positively correlated with trust in AI (correlation coefficient 0.094; p = 0.04), whereas patient age, level of education, and subjective perception of illness were not (p > 0.05). The mean score (± standard deviation) for trust in capability was higher for physicians than for AI for responding in an individualized way when communicating a diagnosis (4.51 ± 0.76 vs 3.38 ± 1.07; mean difference [MD] 1.130, 95% confidence interval [CI] 1.010-1.250; t924 = 18.52, p < 0.001; Cohen's d = 1.040) and for explaining information in an understandable way (4.57 ± vs 3.18 ± 1.09; MD 1.392, 95% CI 1.275-1.509; t921 = 27.27, p < 0.001; Cohen's d = 1.216). Patients stated that they had higher trust in a diagnosis made by AI controlled by a physician versus AI not controlled by a physician (4.31 ± 0.88 vs 1.75 ± 0.93; MD 2.561, 95% CI 2.444-2.678; t925 = 42.89, p < 0.001; Cohen's d = 2.818). AI-assisted physicians (66.74%) were preferred over physicians alone (29.61%), physicians controlled by AI (2.36%), and AI alone (0.64%) for treatment in the current clinical scenario. CONCLUSIONS: Trust in future diagnostic and therapeutic AI-based treatment relies on optimal integration with urologists as the human-machine interface to leverage human and AI capabilities. PATIENT SUMMARY: Artificial intelligence (AI) will play a role in diagnostic decisions in prostate cancer in the future. At present, patients prefer AI-assisted urologists over urologists alone, AI alone, and AI-controlled urologists. Specific traits of AI and urologists could be used to optimize diagnosis and treatment for patients with prostate cancer.

Transperineal vs transrectal magnetic resonance and ultrasound image fusion prostate biopsy: a pair-matched comparison.

The objective of this study was to compare transperineal (TP) versus transrectal (TR) magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) fusion prostate biopsy (PBx). Consecutive men who underwent prostate MRI followed by a systematic biopsy. Additional target biopsies were performed from Prostate Imaging Reporting & Data System (PIRADS) 3-5 lesions. Men who underwent TP PBx were matched 1:2 with a synchronous cohort undergoing TR PBx by PSA, Prostate volume (PV) and PIRADS score. Endpoint of the study was the detection of clinically significant prostate cancer (CSPCa; Grade Group ≥ 2). Univariate and multivariable analyses were performed. Results were considered statistically significant if p < 0.05. Overall, 504 patients met the inclusion criteria. A total of 168 TP PBx were pair-matched to 336 TR PBx patients. Baseline demographics and imaging characteristics were similar between the groups. Per patient, the CSPCa detection was 2.1% vs 6.3% (p = 0.4) for PIRADS 1-2, and 59% vs 60% (p = 0.9) for PIRADS 3-5, on TP vs TR PBx, respectively. Per lesion, the CSPCa detection for PIRADS 3 (21% vs 16%; p = 0.4), PIRADS 4 (51% vs 44%; p = 0.8) and PIRADS 5 (76% vs 84%; p = 0.3) was similar for TP vs TR PBx, respectively. However, the TP PBx showed a longer maximum cancer core length (11 vs 9 mm; p = 0.02) and higher cancer core involvement (83% vs 65%; p < 0.001) than TR PBx. Independent predictors for CSPCa detection were age, PSA, PV, abnormal digital rectal examination findings, and PIRADS 3-5. Our study demonstrated transperineal MRI/TRUS fusion PBx provides similar CSPCa detection, with larger prostate cancer core length and percent of core involvement, than transrectal PBx.