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.

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.

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.

Comprehensive Assessment of MRI-based Artificial Intelligence Frameworks Performance in the Detection, Segmentation, and Classification of Prostate Lesions Using Open-Source Databases

Numerous MRI-based artificial intelligence (AI) frameworks have been designed for prostate cancer lesion detection, segmentation, and classification via MRI as a result of intrareader and interreader variability that is inherent to traditional interpretation. Open-source data sets have been released with the intention of providing freely available MRIs for the testing of diverse AI frameworks in automated or semiautomated tasks. Here, an in-depth assessment of the performance of MRI-based AI frameworks for detecting, segmenting, and classifying prostate lesions using open-source databases was performed. Among 17 data sets, 12 were specific to prostate cancer detection/classification, with 52 studies meeting the inclusion criteria.

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.

The Novel Green Learning Artificial Intelligence for Prostate Cancer Imaging: A Balanced Alternative to Deep Learning and Radiomics

The application of artificial intelligence (AI) on prostate magnetic resonance imaging (MRI) has shown promising results. Several AI systems have been developed to automatically analyze prostate MRI for segmentation, cancer detection, and region of interest characterization, thereby assisting clinicians in their decision-making process. Deep learning, the current trend in imaging AI, has limitations including the lack of transparency "black box", large data processing, and excessive energy consumption. In this narrative review, the authors provide an overview of the recent advances in AI for prostate cancer diagnosis and introduce their next-generation AI model, Green Learning, as a promising solution.

Clinical Patient Summaries Not Fit for Purpose: A Study in Urology

We evaluated the comprehensibility of patient summaries provided by urology journals for the general public. The WebFX online tool was used to assess the readability of abstracts and patient summaries by scoring the text according to established readability indices. A total of 266 articles were included and statistical analysis was performed to compare the readability of abstracts and patient summaries, stratified by article type and text type. The results show that patient summaries consistently performed worse than abstracts for all readability metrics, and the readability levels for both abstracts and patient summaries were more advanced than the recommended guidelines on average. This study suggests that patient summaries provided by these urology journals may not be easily understood by the general population, and tools should be developed to help urological researchers improve the accessibility of their work. PATIENT SUMMARY: We checked how easy it is to read and understand patient summaries and abstracts of research articles from four urology journals. We found that the summaries and abstracts were too hard to read. This study shows that we need to make these summaries easier to read for everyone.

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).

Bridging the Gap Between Urological Research and Patient Understanding: The Role of Large Language Models in Automated Generation of Layperson's Summaries

INTRODUCTION

This study assessed ChatGPT's ability to generate readable, accurate, and clear layperson summaries of urological studies, and compared the performance of ChatGPT-generated summaries with original abstracts and author-written patient summaries to determine its effectiveness as a potential solution for creating accessible medical literature for the public.

METHODS

Articles from the top 5 ranked urology journals were selected. A ChatGPT prompt was developed following guidelines to maximize readability, accuracy, and clarity, minimizing variability. Readability scores and grade-level indicators were calculated for the ChatGPT summaries, original abstracts, and patient summaries. Two MD physicians independently rated the accuracy and clarity of the ChatGPT-generated layperson summaries. Statistical analyses were conducted to compare readability scores. Cohen's κ coefficient was used to assess interrater reliability for correctness and clarity evaluations.

RESULTS

A total of 256 journal articles were included. The ChatGPT-generated summaries were created with an average time of 17.5 (SD 15.0) seconds. The readability scores of the ChatGPT-generated summaries were significantly better than the original abstracts, with Global Readability Score 54.8 (12.3) vs 29.8 (18.5), Flesch Kincade Reading Ease 54.8 (12.3) vs 29.8 (18.5), Flesch Kincaid Grade Level 10.4 (2.2) vs 13.5 (4.0), Gunning Fog Score 12.9 (2.6) vs 16.6 (4.1), Smog Index 9.1 (2.0) vs 12.0 (3.0), Coleman Liau Index 12.9 (2.1) vs 14.9 (3.7), and Automated Readability Index 11.1 (2.5) vs 12.0 (5.7; P < .0001 for all except Automated Readability Index, which was P = .037). The correctness rate of ChatGPT outputs was >85% across all categories assessed, with interrater agreement (Cohen's κ) between 2 independent physician reviewers ranging from 0.76-0.95.

CONCLUSIONS

ChatGPT can create accurate summaries of scientific abstracts for patients, with well-crafted prompts enhancing user-friendliness. Although the summaries are satisfactory, expert verification is necessary for improved accuracy.

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.

Rivaroxaban with Aspirin Versus Aspirin for Peripheral Arterial Disease and Intermittent Claudication. Rationale and Design of the COMPASS CLAUDICATION Trial

BACKGROUND

The COMPASS trial demonstrated that in patients with peripheral arterial disease, the combination of rivaroxaban and aspirin compared with aspirin reduces the risk of major adverse limb events, but it is not known whether this combination can also improve symptoms in patients with intermittent claudication. The primary objective of this study is to evaluate the effect of the combination on claudication distance.

STUDY DESIGN

Eighty-eight patients with intermittent claudication will be randomized to receive rivaroxaban 2.5 mg twice daily plus aspirin 100 mg once daily or aspirin 100 mg once daily for 24 weeks. The primary outcome is the change in claudication distance from the baseline to 24 weeks, measured by 6 min walking test and treadmill test. The primary safety outcome is the incidence of major bleeding and clinically relevant non-major bleeding according to the International Society on Thrombosis and Hemostasis criteria.

SUMMARY

The COMPASS CLAUDICATION trial will provide high-quality evidence regarding the effect of the combination of rivaroxaban and aspirin on claudication distance in patients with peripheral arterial disease.

Multiparametric ultrasound of prostate: role in prostate cancer diagnosis

Recent advances in ultrasonography (US) technology established modalities, such as Doppler-US, HistoScanning, contrast-enhanced ultrasonography (CEUS), elastography, and micro-ultrasound. The early results of these US modalities have been promising, although there are limitations including the need for specialized equipment, inconsistent results, lack of standardizations, and external validation. In this review, we identified studies evaluating multiparametric ultrasonography (mpUS), the combination of multiple US modalities, for prostate cancer (PCa) diagnosis. In the past 5 years, a growing number of studies have shown that use of mpUS resulted in high PCa and clinically significant prostate cancer (CSPCa) detection performance using radical prostatectomy histology as the reference standard. Recent studies have demonstrated the role mpUS in improving detection of CSPCa and guidance for prostate biopsy and therapy. Furthermore, some aspects including lower costs, real-time imaging, applicability for some patients who have contraindication for magnetic resonance imaging (MRI) and availability in the office setting are clear advantages of mpUS. Interobserver agreement of mpUS was overall low; however, this limitation can be improved using standardized and objective evaluation systems such as the machine learning model. Whether mpUS outperforms MRI is unclear. Multicenter randomized controlled trials directly comparing mpUS and multiparametric MRI are warranted.

Reversal of Factor Xa Inhibitors by Andexanet Alfa May Increase Thrombogenesis Compared to Pretreatment Values

Recombinant coagulation factor Xa (FXa), inactivated Zh-zo, also known as andexanet alfa (AA), is a modified version of human FXa that has been developed to neutralize FXa inhibitors. We studied the reversal effect of AA for these inhibitors in various anticoagulant and thrombin generation (TG) assays. Individual aliquots of normal human plasma containing 1 µg/mL of apixaban, betrixaban, edoxaban, and rivaroxaban, were supplemented with saline or AA at a concentration of 100 µg/mL. Clotting profiles include prothrombinase-induced clotting time, activated partial thromboplastin time, and prothrombin time. Factor Xa activity was measured using an amidolytic method. Thrombin generation was measured using a calibrated automated thrombogram. Differential neutralization of all 4 anticoagulants was noted in the activated clotting time and other clotting tests. The FXa activity reversal profile varied with an observed decrease in apixaban (22%), betrixaban (56%), edoxaban (28%), and rivaroxaban (49%). Andexanet alfa also led to an increased TG in comparison to saline. The peak thrombin was higher (40%), area under the curve (AUC) increased (15%), whereas the lag time (LT) decreased (17%). Andexanet alfa added at 100 µg/mL to various FXa supplemented systems resulted in reversal of the inhibitory effects, restoring the TG profile; AUC, LT, and peak thrombin levels were comparable to those of unsupplemented samples. Andexanet alfa is capable of reversing anti-Xa activity of different oral FXa inhibitors but overshoots thrombogenesis in both the saline and FXa inhibitor supplemented systems. The degree of neutralization of Xa inhibitor is specific to each agent.