Use of artificial intelligence in breast surgery: a narrative review.

Background and Objective: We have witnessed tremendous advances in artificial intelligence (AI) technologies. Breast surgery, a subspecialty of general surgery, has notably benefited from AI technologies. This review aims to evaluate how AI has been integrated into breast surgery practices, to assess its effectiveness in improving surgical outcomes and operational efficiency, and to identify potential areas for future research and application. Methods: Two authors independently conducted a comprehensive search of PubMed, Google Scholar, EMBASE, and Cochrane CENTRAL databases from January 1, 1950, to September 4, 2023, employing keywords pertinent to AI in conjunction with breast surgery or cancer. The search focused on English language publications, where relevance was determined through meticulous screening of titles, abstracts, and full-texts, followed by an additional review of references within these articles. The review covered a range of studies illustrating the applications of AI in breast surgery encompassing lesion diagnosis to postoperative follow-up. Publications focusing specifically on breast reconstruction were excluded. Key Content and Findings: AI models have preoperative, intraoperative, and postoperative applications in the field of breast surgery. Using breast imaging scans and patient data, AI models have been designed to predict the risk of breast cancer and determine the need for breast cancer surgery. In addition, using breast imaging scans and histopathological slides, models were used for detecting, classifying, segmenting, grading, and staging breast tumors. Preoperative applications included patient education and the display of expected aesthetic outcomes. Models were also designed to provide intraoperative assistance for precise tumor resection and margin status assessment. As well, AI was used to predict postoperative complications, survival, and cancer recurrence. Conclusions: Extra research is required to move AI models from the experimental stage to actual implementation in healthcare. With the rapid evolution of AI, further applications are expected in the coming years including direct performance of breast surgery. Breast surgeons should be updated with the advances in AI applications in breast surgery to provide the best care for their patients.

Impact of nutrition on skin wound healing and aesthetic outcomes: A comprehensive narrative review.

Background: While current wound treatment strategies often focus on antimicrobials and topical agents, the role of nutrition in wound healing and aesthetic outcomes is crucial but frequently overlooked. This review assesses the impact of specific nutrients and preoperative nutritional status on surgical outcomes. Methods: A comprehensive search was conducted in PubMed, Scopus, Web of Science, and the Cochrane Library, from the inception of the study to October 2023. The study focused on the influence of macronutrients and micronutrients on aesthetic outcomes, the optimization of preoperative nutritional status, and the association between nutritional status and postoperative complications. Inclusion criteria were English language peer-reviewed articles, systematic reviews, meta-analyses, and clinical trials related to the impact of nutrition on skin wound healing and aesthetic outcomes. Exclusion criteria included non-English publications, non-peer-reviewed articles, opinion pieces, and animal studies. Results: Omega-3 fatty acids and specific amino acids were linked to enhanced wound-healing and immune function. Vitamins A, B, and C and zinc positively influenced healing stages, while vitamin E showed variable results. Polyphenolic compounds showed anti-inflammatory effects beneficial for recovery. Malnutrition was associated with increased postoperative complications and infections, whereas preoperative nutritional support correlated with reduced hospital stays and complications. Conclusion: Personalized nutritional plans are essential in surgical care, particularly for enhanced recovery after surgery protocols. Despite the demonstrated benefits of certain nutrients, gaps in research, particularly regarding elements such as iron, necessitate further studies. Nutritional assessments and interventions are vital for optimal preoperative care, underscoring the need for more comprehensive guidelines and research in nutritional management for surgical patients.

Exploring the role of an artificial intelligence chatbot on appendicitis management: an experimental study on ChatGPT.

BACKGROUND: Appendicitis is a common surgical condition that requires urgent medical attention. Recent advancements in artificial intelligence and large language processing, such as ChatGPT, have demonstrated potential in supporting healthcare management and scientific research. This study aims to evaluate the accuracy and comprehensiveness of ChatGPT's knowledge on appendicitis management. METHODS: Six questions related to appendicitis management were created by experienced RACS qualified general surgeons to assess ChatGPT's ability to provide accurate information. The criteria of ChatGPT answers' accuracy were compared with current healthcare guidelines for appendicitis and subjective evaluation by two RACS qualified General Surgeons. Additionally, ChatGPT was then asked to provide five high level evidence references to support its responses. RESULTS: ChatGPT provided clinically relevant information on appendicitis management, however, was inconsistent in doing so and often provided superficial information. Further to this, ChatGPT encountered difficulties in generating relevant references, with some being either non-existent or incorrect. CONCLUSION: ChatGPT has the potential to provide timely and comprehensible medical information on appendicitis management to laypersons. However, its issue of inaccuracy in information and production of non-existent or erroneous references presents a challenge for researchers and clinicians who may inadvertently employ such information in their research or healthcare. Therefore, clinicians should exercise caution when using ChatGPT for these purposes.

Exploring the Role of a Large Language Model on Carpal Tunnel Syndrome Management: An Observation Study of ChatGPT.

PURPOSE: Recently, large language models, such as ChatGPT, have emerged as promising tools to facilitate scientific research and health care management. The present study aimed to explore the extent of knowledge possessed by ChatGPT concerning carpal tunnel syndrome (CTS), a compressive neuropathy that may lead to impaired hand function and that is frequently encountered in the field of hand surgery. METHODS: Six questions pertaining to diagnosis and management of CTS were posed to ChatGPT. The responses were subsequently analyzed and evaluated based on their accuracy, coherence, and comprehensiveness. In addition, ChatGPT was requested to provide five high-level evidence references in support of its answers. A simulated doctor-patient consultation was also conducted to assess whether ChatGPT could offer safe medical advice. RESULTS: ChatGPT supplied clinically relevant information regarding CTS, although at a relatively superficial level. In the context of doctor-patient interaction, ChatGPT suggested a diagnostic pathway that deviated from the widely accepted clinical consensus on CTS diagnosis. Nevertheless, it incorporated differential diagnoses and valuable management options for CTS. Although ChatGPT demonstrated the ability to retain and recall information from previous patient conversations, it infrequently produced pertinent references, many of which were either nonexistent or incorrect. CONCLUSIONS: ChatGPT displayed the capability to deliver validated medical information on CTS to nonmedical individuals. However, the generation of nonexistent and inaccurate references by ChatGPT presents a challenge to academic integrity. CLINICAL RELEVANCE: To increase their utility in medicine and academia, large language models must go through specialized reputable data set training and validation from experts. It is essential to note that at present, large language models cannot replace the expertise of health care professionals and may act as a supportive tool.

The effect of chemotherapy on the complication rates of breast reconstruction: A systematic review and meta-analysis.

BACKGROUND: The impact of chemotherapy on complications following breast reconstruction surgery (BRS) is currently inconclusive. This meta-analysis investigates the impact of chemotherapy on complication rates in BRS. METHODS: Preferred Reporting in Systematic Review and Meta-Analysis (PRISMA) guidelines was used to search relevant studies published from January 2006 to March 2022. The complication rates of neoadjuvant systemic therapy (NST) and adjuvant systemic therapy (AST) were analyzed via RevMan software 5.4, and a P value of< 0.05 was considered significant. The quality of selected studies was performed using the Newcastle-Ottawa scale for quality assessment. RESULTS: A total of 18 studies comprising 49,217 patients were included. There was no significant difference in the total complications rate, major complications, or minor complications between NST and BRS or control. The rate of wound dehiscence was higher in the NST group compared with the BRS only group [RR= 1.54, 95% CI, (1.08, 2.18), P = 0.02], and the rate of infection was lower in the NST group compared with the BRS only group, [RR= 0.75, 95% CI, (0.61, 0.94), P = 0.01]. No significant difference in the rates of hematoma, seroma, skin necrosis, and implant loss was detected between NST and AST, or NST with BRS only. No statistically significant differences in total complication rates were observed between flap and implant BRS types (P = 0.88). CONCLUSION: No significant differences between AST and NST were detected for complications. Significantly, NST had more wound dehiscence and less infection rates compared with BRS only groups, possibly reflecting selection bias or issues in the design of reported studies. LEVEL OF EVIDENCE: I.

Utility of urinary biomarkers in primary haematuria: Systematic review and meta-analysis.

Objectives: To evaluate the diagnostic performance of FDA-approved urinary biomarkers in the evaluation of primary haematuria for investigation of bladder cancer. Methods: The scientific databases MEDLINE, EMBASE, Pubmed and Web of Science were searched to collect studies. Studies that evaluated the diagnostic performance of FDA-approved urinary biomarkers in investigating patients with primary haematuria without a prior history of bladder cancer were included. Quality of studies was assessed using the JBI Criteria. Bivariate mixed-effects regression model was used to calculate pooled sensitivities and specificities for each biomarker. Results: Eighteen studies were included in the analysis. The biomarkers assessed in these studies were CxBladder, AssureMDx, Bladder Tumour Antigen (BTA), NMP22, UroVysion and Immunocyt/uCyt+. Several biomarkers, such as AssureMDx, CxBladder and Immunocyt, were shown to have better diagnostic performance based on their sensitivity, specificity and diagnostic odds ratio, as well as positive and negative likelihood ratios. Across the six biomarkers, sensitivity ranged from 0.659 to 0.973, and the specificity ranged between 0.577 and 0.833. Conclusion: Despite certain biomarkers demonstrated better performance, current diagnostic abilities of the FDA-approved biomarkers remain insufficient for their general application as a rule out test for bladder cancer diagnosis and as a triage test for cystoscopy in patients with primary haematuria. High-quality prospective studies are required to further analyse this and also analyse the correct scenario in which urinary biomarkers may be best utilised.