Cutaneous amyloidosis mimicking basal cell carcinoma: a case series and literature review.

BACKGROUND: Amyloidosis is characterized by extracellular amyloid protein deposition. When amyloidosis intersects with basal cell carcinoma (BCC), it introduces complex diagnostic challenges. This study explored the overlap between primary localized cutaneous amyloidosis (PLCA) and BCC, examining amyloid deposits in BCC, systemic amyloidosis risk in PLCA, and various treatment methods. METHODS: Two case studies were discussed, followed by a literature review, in which PubMed, Web of Science, EMBASE, and the Cochrane Library databases were utilized. The search, covering studies from infinity up to January 2024, focused on "cutaneous amyloidosis," "basal cell carcinoma," and related terms. Articles in English detailing the clinical presentation, diagnostic methods, treatment, and outcomes of cutaneous amyloidosis mimicking BCC were included. Data extraction and synthesis were performed by two independent reviewers. CASE SERIES: This study highlighted two cases exemplifying the complexity of diagnosing BCC and PLCA. The first case (a 64-year-old with a nodule on the cheek) and the second (a 67-year-old with a nodular lesion on the upper lip cheek) were initially suspected as BCC and were later identified as PLCA upon histopathological examination. DISCUSSION: The diagnosis of amyloidosis within BCC nodules remains a diagnostic challenge. Although their coexistence is relatively prevalent, their local recurrence rates remain debatable. Various diagnostic and therapeutic approaches have been suggested, such as topical creams and phototherapy. However, none have garnered conclusive and consistent evidence to establish reliable clinical application. CONCLUSION: The findings emphasized the importance of considering alternative pathologies in differential diagnoses. Future research should focus on understanding systemic amyloidosis risks and optimizing care for both conditions.

Response to "Letter to the Editor-Exploring the Unknown: Evaluating ChatGPT's Performance in Uncovering Novel Aspects of Plastic Surgery and Identifying Areas for Future Innovation".

We appreciate Dr. Qi and Dr. Niu for their insightful comments on our study, "Exploring the Unknown: Evaluating ChatGPT's Performance in Uncovering Novel Aspects of Plastic Surgery and Identifying Areas for Future Innovation." Their observations underscore significant considerations in the application of artificial intelligence (AI) in plastic surgery. We agree with their concern about potential biases in ChatGPT's responses. The AI's frequent attribution of the title "parent of plastic surgery" to Sir Harold Delf Gillies, despite gender-neutral terminology, highlights underlying biases from training data. These biases often reflect historical texts and contemporary writings. Addressing them requires refining training datasets for balanced representation and developing algorithms that adjust dynamically to diverse inputs. The authors also question the criteria ChatGPT uses to identify key contributions to plastic surgery. The AI's focus on microsurgery, minimally invasive techniques, and tissue engineering, while significant, may prioritize keyword prevalence over a holistic evaluation. Enhancing ChatGPT's capabilities through targeted training and input from subject matter experts could improve the AI's ability to generate more balanced outputs. The identified bias favoring reconstructive over cosmetic procedures is another critical point. While reconstructive advancements are transformative, cosmetic surgery also has significant innovations. Ensuring ChatGPT presents a balanced view of both reconstructive and cosmetic advancements is essential. This can be achieved by diversifying training data and calibrating the AI to give equitable weight to different subspecialties within plastic surgery. AI models like ChatGPT are proficient in processing and generating information but lack the human elements of creativity, intuition, and emotional depth critical for groundbreaking innovations. AI should complement, not replace, the expert judgment and innovative thinking of skilled plastic surgeons. Ensuring the accuracy of AI-generated responses is crucial. Clinicians must verify AIgenerated information against established medical literature and clinical guidelines to maintain accuracy in medical practice. Continuous feedback and improvement mechanisms are vital to enhance AI's clinical utility. The improvement of AI in plastic surgery will be driven by active involvement from surgeons, providing comprehensive and balanced data for training to ensure AI systems evolve to support and enhance clinical practice effectively.Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors  www.springer.com/00266 .

Can AI Answer My Questions? Utilizing Artificial Intelligence in the Perioperative Assessment for Abdominoplasty Patients.

BACKGROUND: Abdominoplasty is a common operation, used for a range of cosmetic and functional issues, often in the context of divarication of recti, significant weight loss, and after pregnancy. Despite this, patient-surgeon communication gaps can hinder informed decision-making. The integration of large language models (LLMs) in healthcare offers potential for enhancing patient information. This study evaluated the feasibility of using LLMs for answering perioperative queries. METHODS: This study assessed the efficacy of four leading LLMs-OpenAI's ChatGPT-3.5, Anthropic's Claude, Google's Gemini, and Bing's CoPilot-using fifteen unique prompts. All outputs were evaluated using the Flesch-Kincaid, Flesch Reading Ease score, and Coleman-Liau index for readability assessment. The DISCERN score and a Likert scale were utilized to evaluate quality. Scores were assigned by two plastic surgical residents and then reviewed and discussed until a consensus was reached by five plastic surgeon specialists. RESULTS: ChatGPT-3.5 required the highest level for comprehension, followed by Gemini, Claude, then CoPilot. Claude provided the most appropriate and actionable advice. In terms of patient-friendliness, CoPilot outperformed the rest, enhancing engagement and information comprehensiveness. ChatGPT-3.5 and Gemini offered adequate, though unremarkable, advice, employing more professional language. CoPilot uniquely included visual aids and was the only model to use hyperlinks, although they were not very helpful and acceptable, and it faced limitations in responding to certain queries. CONCLUSION: ChatGPT-3.5, Gemini, Claude, and Bing's CoPilot showcased differences in readability and reliability. LLMs offer unique advantages for patient care but require careful selection. Future research should integrate LLM strengths and address weaknesses for optimal patient education. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Evaluating Artificial Intelligence's Role in Teaching the Reporting and Interpretation of Computed Tomographic Angiography for Preoperative Planning of the Deep Inferior Epigastric Artery Perforator Flap.

Background: Artificial intelligence (AI) has the potential to transform preoperative planning for breast reconstruction by enhancing the efficiency, accuracy, and reliability of radiology reporting through automatic interpretation and perforator identification. Large language models (LLMs) have recently advanced significantly in medicine. This study aimed to evaluate the proficiency of contemporary LLMs in interpreting computed tomography angiography (CTA) scans for deep inferior epigastric perforator (DIEP) flap preoperative planning. Methods: Four prominent LLMs, ChatGPT-4, BARD, Perplexity, and BingAI, answered six questions on CTA scan reporting. A panel of expert plastic surgeons with extensive experience in breast reconstruction assessed the responses using a Likert scale. In contrast, the responses' readability was evaluated using the Flesch Reading Ease score, the Flesch-Kincaid Grade level, and the Coleman-Liau Index. The DISCERN score was utilized to determine the responses' suitability. Statistical significance was identified through a t-test, and P-values < 0.05 were considered significant. Results: BingAI provided the most accurate and useful responses to prompts, followed by Perplexity, ChatGPT, and then BARD. BingAI had the greatest Flesh Reading Ease (34.7±5.5) and DISCERN (60.5±3.9) scores. Perplexity had higher Flesch-Kincaid Grade level (20.5±2.7) and Coleman-Liau Index (17.8±1.6) scores than other LLMs. Conclusion: LLMs exhibit limitations in their capabilities of reporting CTA for preoperative planning of breast reconstruction, yet the rapid advancements in technology hint at a promising future. AI stands poised to enhance the education of CTA reporting and aid preoperative planning. In the future, AI technology could provide automatic CTA interpretation, enhancing the efficiency, accuracy, and reliability of CTA reports.

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.

Evaluating Extended Curettage and Adjuvant Therapy Against Wide Resection and Reconstruction in the Management of Distal Radius Giant Cell Tumors: A Systematic Review and Meta-analysis.

BACKGROUND: The management of distal radius giant cell tumors (GCTs) remains challenging, and the optimal approach is still a matter of debate. This systematic review and meta-analysis aimed to compare the outcomes of extended curettage and wide resection, the mainstays of treatment. METHODS: Medline (via PubMed), Cochrane Library, Web of Science, Google Scholar, ClinicalTrials.gov, and Embase databases were searched for comparative studies that assessed extended curettage with adjuvant therapy and wide resection with reconstruction in patients with GCTs of the distal radius up to April 2023. Data were collected and analyzed on rates of local recurrence, metastasis, overall complications, and functional outcomes. The Newcastle-Ottawa scale was used to appraise the risk of bias within each study. RESULTS: Fifteen studies (n = 373 patients) were included and analyzed. Patients who underwent curettage were more likely to develop recurrence (risk ratio [RR] = 3.02 [95% confidence interval; CI, 1.87-4.89], P < .01), showed fewer complications (RR = 0.32 [95% CI, 0.21-0.49], P < .01), and showed greater improvement in Visual Analog Scale and lower Disabilities of the Arm, Shoulder, and Hand scores (P < .00001) than those who underwent wide resection. No significant difference was found regarding metastasis (RR = 1.03 [95% CI, 0.38-2.78], P = .95). CONCLUSIONS: Regarding the surgical approach to GCT of the distal radius, curettage with adjuvant therapy was associated with a higher likelihood of recurrence compared with wide resection with reconstruction. Nevertheless, the curettage approach resulted in significantly lower rates of operative complications, decreased pain scores, and better functional outcomes in comparison to the resection group.

Exploring the Unknown: Evaluating ChatGPT's Performance in Uncovering Novel Aspects of Plastic Surgery and Identifying Areas for Future Innovation.

BACKGROUND: Artificial intelligence (AI) has emerged as a powerful tool in various medical fields, including plastic surgery. This study aims to evaluate the performance of ChatGPT, an AI language model, in elucidating historical aspects of plastic surgery and identifying potential avenues for innovation. METHODS: A comprehensive analysis of ChatGPT's responses to a diverse range of plastic surgery-related inquiries was performed. The quality of the AI-generated responses was assessed based on their relevance, accuracy, and novelty. Additionally, the study examined the AI's ability to recognize gaps in existing knowledge and propose innovative solutions. ChatGPT's responses were analysed by specialist plastic surgeons with extensive research experience, and quantitatively analysed with a Likert scale. RESULTS: ChatGPT demonstrated a high degree of proficiency in addressing a wide array of plastic surgery-related topics. The AI-generated responses were found to be relevant and accurate in most cases. However, it demonstrated convergent thinking and failed to generate genuinely novel ideas to revolutionize plastic surgery. Instead, it suggested currently popular trends that demonstrate great potential for further advancements. Some of the references presented were also erroneous as they cannot be validated against the existing literature. CONCLUSION: Although ChatGPT requires major improvements, this study highlights its potential as an effective tool for uncovering novel aspects of plastic surgery and identifying areas for future innovation. By leveraging the capabilities of AI language models, plastic surgeons may drive advancements in the field. Further studies are needed to cautiously explore the integration of AI-driven insights into clinical practice and to evaluate their impact on patient outcomes. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Comparison of large language models in management advice for melanoma: Google's AI BARD, BingAI and ChatGPT.

Large language models (LLMs) are emerging artificial intelligence (AI) technology refining research and healthcare. Their use in medicine has seen numerous recent applications. One area where LLMs have shown particular promise is in the provision of medical information and guidance to practitioners. This study aims to assess three prominent LLMs-Google's AI BARD, BingAI and ChatGPT-4 in providing management advice for melanoma by comparing their responses to current clinical guidelines and existing literature. Five questions on melanoma pathology were prompted to three LLMs. A panel of three experienced Board-certified plastic surgeons evaluated the responses for reliability using reliability matrix (Flesch Reading Ease Score, the Flesch-Kincaid Grade Level and the Coleman-Liau Index), suitability (modified DISCERN score) and comparing them to existing guidelines. t-Test was performed to calculate differences in mean readability and reliability scores between LLMs and p value <0.05 was considered statistically significant. The mean readability scores across three LLMs were same. ChatGPT exhibited superiority with a Flesch Reading Ease Score of 35.42 (±21.02), Flesch-Kincaid Grade Level of 11.98 (±4.49) and Coleman-Liau Index of 12.00 (±5.10), however all of these were insignificant (p > 0.05). Suitability-wise using DISCERN score, ChatGPT 58 (±6.44) significantly (p = 0.04) outperformed BARD 36.2 (±34.06) and was insignificant to BingAI's 49.8 (±22.28). This study demonstrates that ChatGPT marginally outperforms BARD and BingAI in providing reliable, evidence-based clinical advice, but they still face limitations in depth and specificity. Future research should improve LLM performance by integrating specialized databases and expert knowledge to support patient-centred care.

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.

Breaking NGF-TrkA immunosuppression in melanoma sensitizes immunotherapy for durable memory T cell protection.

Melanoma cells, deriving from neuroectodermal melanocytes, may exploit the nervous system's immune privilege for growth. Here we show that nerve growth factor (NGF) has both melanoma cell intrinsic and extrinsic immunosuppressive functions. Autocrine NGF engages tropomyosin receptor kinase A (TrkA) on melanoma cells to desensitize interferon γ signaling, leading to T and natural killer cell exclusion. In effector T cells that upregulate surface TrkA expression upon T cell receptor activation, paracrine NGF dampens T cell receptor signaling and effector function. Inhibiting NGF, either through genetic modification or with the tropomyosin receptor kinase inhibitor larotrectinib, renders melanomas susceptible to immune checkpoint blockade therapy and fosters long-term immunity by activating memory T cells with low affinity. These results identify the NGF-TrkA axis as an important suppressor of anti-tumor immunity and suggest larotrectinib might be repurposed for immune sensitization. Moreover, by enlisting low-affinity T cells, anti-NGF reduces acquired resistance to immune checkpoint blockade and prevents melanoma recurrence.

Uncovering the magnitude of African pangolin poaching with extensive nanopore DNA genotyping of seized scales.

Trade in pangolins is illegal, and yet tons of their scales and products are seized at various ports. These large seizures are challenging to process and comprehensively genotype for upstream provenance tracing and species identification for prosecution. We implemented a scalable DNA barcoding pipeline in which rapid DNA extraction and MinION sequencing were used to genotype a substantial proportion of pangolin scales subsampled from 2 record shipments seized in Singapore in 2019 (37.5 t). We used reference sequences to match the scales to phylogeographical regions of origin. In total, we identified 2346 cytochrome b (cytb) barcodes of white-bellied (Phataginus tricuspis) (from 1091 scales), black-bellied (Phataginus tetradactyla) (227 scales), and giant (Smutsia gigantea) (1028 scales) pangolins. Haplotype diversity was higher for P. tricuspis scales (121 haplotypes, 66 novel) than that for P. tetradactyla (22 haplotypes, 15 novel) and S. gigantea (25 haplotypes, 21 novel) scales. Of the novel haplotypes, 74.2% were likely from western and west-central Africa, suggesting potential resurgence of poaching and newly exploited populations in these regions. Our results illustrate the utility of extensively subsampling large seizures and outline an efficient molecular approach for rapid genetic screening that should be accessible to most forensic laboratories and enforcement agencies.

Revelación de la magnitud de la caza furtiva del pangolín africano mediante el genotipo extenso de nanoporos de ADN de escamas incautadas Resumen Aunque el mercado de pangolines es ilegal, se incautan toneladas de sus escamas y productos derivados en varios puertos comerciales. Es un reto procesar estas magnas incautaciones y obtener el genotipo completo para usarlo en la trazabilidad logística ascendente e identificación de la especie y así imponer sanciones. Implementamos una canalización escalable del código de barras de ADN en el cual usamos la extracción rápida de ADN y la secuenciación MinION para obtener el genotipo de una proporción sustancial de las escamas de pangolín submuestreadas en dos cargamentos incautados en 2019 en Singapur (37.5 t). Usamos secuencias referenciales para emparejar las escamas con las regiones filogeográficas de origen. Identificamos en total 2,346 códigos de citocromo b (cytb) del pangolín de vientre blanco (Phataginus tricuspis) (de 1,091 escamas), de vientre negro (P. tetradactyla) (227 escamas) y del pangolín gigante (Smutsia gigantea) (1,028 escamas). La diversidad de haplotipos fue mayor en las escamas de P. tricuspis (121 haplotipos, 66 nuevos) que en las de P. tetradactyla (22 haplotipos, 15 nuevos) y S. gigantea (25 haplotipos, 21 nuevos). De los haplotipos nuevos, el 74.2% probablemente provenía del occidente y centro­occidente de África, lo que sugiere un resurgimiento potencial de la caza furtiva y poblaciones recién explotadas en estas regiones. Nuestros resultados demuestran la utilidad de submuestrear extensivamente las grandes incautaciones y esboza una estrategia molecular eficiente para un análisis genético rápido que debería ser accesible para la mayoría de los laboratorios forenses y las autoridades de aplicación.

Delving into New Frontiers: assessing ChatGPT's proficiency in revealing uncharted dimensions of general surgery and pinpointing innovations for future advancements.

PURPOSE: The advent of artificial intelligence (AI) has significantly influenced various medical domains, including general surgery. This research aims to assess ChatGPT, an AI language model, in its ability to shed light on the historical facets of general surgery and pinpoint opportunities for innovation. METHODS: A series of 7 pertinent questions on field of general surgery was posed to ChatGPT. The AI-generated responses were meticulously examined for their relevance, accuracy, and novelty. Additionally, the study explored the AI's ability to recognize knowledge gaps and propose inventive solutions. Expert general surgeons and general surgical residents possessing comprehensive research experience assessed ChatGPT's answers by comparing them to established guidelines and existing literature. RESULTS: ChatGPT presented information that was relevant and accurate, albeit superficial. However, it exhibited convergent thinking and was unable to produce truly groundbreaking ideas to transform general surgery. Instead, it pointed to current popular trends with significant potential for further development. It failed to provide references when prompted and even created references that could not be verified in exhibiting databases. CONCLUSION: While ChatGPT demonstrated a comprehensive understanding of existing general surgical knowledge and the capacity to generate relevant, evidence-based material, it displayed limitations in producing truly groundbreaking concepts or discoveries beyond current knowledge. These results highlight the necessity of enhancing AI-driven models to facilitate the emergence of new insights and promote synergistic, human-AI partnerships for expediting advancements within the general surgery domain.

Detection of a novel Babesia sp. in Amblyomma javanense, an ectoparasite of Sunda pangolins.

BACKGROUND: Babesia is a protozoal, tick-borne parasite that can cause life-threatening disease in humans, wildlife and domestic animals worldwide. However, in Southeast Asia, little is known about the prevalence and diversity of Babesia species present in wildlife and the tick vectors responsible for its transmission. Recently, a novel Babesia species was reported in confiscated Sunda pangolins (Manis javanica) in Thailand. To investigate the presence of this parasite in Singapore, we conducted a molecular survey of Babesia spp. in free-roaming Sunda pangolins and their main ectoparasite, the Amblyomma javanense tick. METHODS: Ticks and tissue samples were opportunistically collected from live and dead Sunda pangolins and screened using a PCR assay targeting the 18S rRNA gene of Babesia spp. DNA barcoding of the cytochrome oxidase subunit I (COI) mitochondrial gene was used to confirm the species of ticks that were Babesia positive. RESULTS: A total of 296 ticks and 40 tissue samples were obtained from 21 Sunda pangolins throughout the 1-year study period. Babesia DNA was detected in five A. javanense ticks (minimum infection rate = 1.7%) and in nine different pangolins (52.9%) located across the country. Phylogenetic analysis revealed that the Babesia 18S sequences obtained from these samples grouped into a single monophyletic clade together with those derived from Sunda pangolins in Thailand and that this evolutionarily distinct species is basal to the Babesia sensu stricto clade, which encompasses a range of Babesia species that infect both domestic and wildlife vertebrate hosts. CONCLUSIONS: This is the first report documenting the detection of a Babesia species in A. javanense ticks, the main ectoparasite of Sunda pangolins. While our results showed that A. javanense can carry this novel Babesia sp., additional confirmatory studies are required to demonstrate vector competency. Further studies are also necessary to investigate the role of other transmission pathways given the low infection rate of ticks in relation to the high infection rate of Sunda pangolins. Although it appears that this novel Babesia sp. is of little to no pathogenicity to Sunda pangolins, its potential to cause disease in other animals or humans cannot be ruled out.

Comparing the Efficacy of Large Language Models ChatGPT, BARD, and Bing AI in Providing Information on Rhinoplasty: An Observational Study.

Background: Large language models (LLMs) are emerging artificial intelligence (AI) technologies refining research and healthcare. However, the impact of these models on presurgical planning and education remains under-explored. Objectives: This study aims to assess 3 prominent LLMs-Google's AI BARD (Mountain View, CA), Bing AI (Microsoft, Redmond, WA), and ChatGPT-3.5 (Open AI, San Francisco, CA) in providing safe medical information for rhinoplasty. Methods: Six questions regarding rhinoplasty were prompted to ChatGPT, BARD, and Bing AI. A Likert scale was used to evaluate these responses by a panel of Specialist Plastic and Reconstructive Surgeons with extensive experience in rhinoplasty. To measure reliability, the Flesch Reading Ease Score, the Flesch-Kincaid Grade Level, and the Coleman-Liau Index were used. The modified DISCERN score was chosen as the criterion for assessing suitability and reliability. A t test was performed to calculate the difference between the LLMs, and a double-sided P-value <.05 was considered statistically significant. Results: In terms of reliability, BARD and ChatGPT demonstrated a significantly (P < .05) greater Flesch Reading Ease Score of 47.47 (±15.32) and 37.68 (±12.96), Flesch-Kincaid Grade Level of 9.7 (±3.12) and 10.15 (±1.84), and a Coleman-Liau Index of 10.83 (±2.14) and 12.17 (±1.17) than Bing AI. In terms of suitability, BARD (46.3 ± 2.8) demonstrated a significantly greater DISCERN score than ChatGPT and Bing AI. In terms of Likert score, ChatGPT and BARD demonstrated similar scores and were greater than Bing AI. Conclusions: BARD delivered the most succinct and comprehensible information, followed by ChatGPT and Bing AI. Although these models demonstrate potential, challenges regarding their depth and specificity remain. Therefore, future research should aim to augment LLM performance through the integration of specialized databases and expert knowledge, while also refining their algorithms.

Using Generative Artificial Intelligence Tools in Cosmetic Surgery: A Study on Rhinoplasty, Facelifts, and Blepharoplasty Procedures.

Artificial intelligence (AI), notably Generative Adversarial Networks, has the potential to transform medical and patient education. Leveraging GANs in medical fields, especially cosmetic surgery, provides a plethora of benefits, including upholding patient confidentiality, ensuring broad exposure to diverse patient scenarios, and democratizing medical education. This study investigated the capacity of AI models, DALL-E 2, Midjourney, and Blue Willow, to generate realistic images pertinent to cosmetic surgery. We combined the generative powers of ChatGPT-4 and Google's BARD with these GANs to produce images of various noses, faces, and eyelids. Four board-certified plastic surgeons evaluated the generated images, eliminating the need for real patient photographs. Notably, generated images predominantly showcased female faces with lighter skin tones, lacking representation of males, older women, and those with a body mass index above 20. The integration of AI in cosmetic surgery offers enhanced patient education and training but demands careful and ethical incorporation to ensure comprehensive representation and uphold medical standards.

ChatGPT-4: Transforming Medical Education and Addressing Clinical Exposure Challenges in the Post-pandemic Era.

Background: The COVID-19 pandemic has affected medical education, constraining clinical exposure and posing unprecedented challenges for students and junior doctors. This research explores the potential of artificial intelligence (AI), specifically the ChatGPT-4 language model, to transform medical education and address the deficiencies in clinical exposure during the post-pandemic era. Research Questions/Purpose: What is the potential of AI large language models in delivering safe and coherent medical advice to junior doctors for clinical orthopaedic scenarios? Patients and Methods: A series of diverse orthopaedic questions was presented to ChatGPT-4, from general medicine to highly specialised fields. The questions were based on a variety of common orthopaedic presentations including neck of femur fracture, compartment syndrome, pulmonary embolism, and a motor vehicle accident. A validated questionnaire (Likert Scale) was implemented to evaluate the answers produced by ChatGPT-4. Results: Our results indicate that ChatGPT-4 exhibits exceptional proficiency in delivering accurate and coherent medical advice. Its intuitive interface, accessibility, and sophisticated algorithm render it an ideal supplementary tool for medical students and junior doctors. Despite certain limitations, such as its inability to fully address highly specialised areas, this study highlights the potential of AI and ChatGPT-4 to revolutionise medical education and fill the clinical exposure void generated by the pandemic. Future research should concentrate on the practical application of ChatGPT-4 in real-world medical environments and its integration with other emerging technologies to optimise its influence on the education and training of healthcare professionals. Conclusions: ChatGPT-4's integration into orthopaedic education and practice can mitigate pandemic-related experience gaps, promoting self-directed, personalised learning and decision-making support for interns and residents. Future advancements may address limitations to enhance healthcare professionals' learning and expertise. Level of Evidence: Level III evidence-observational study.

Antagonizing the irreversible thrombomodulin-initiated proteolytic signaling alleviates age-related liver fibrosis via senescent cell killing.

Cellular senescence is a stress-induced, stable cell cycle arrest phenotype which generates a pro-inflammatory microenvironment, leading to chronic inflammation and age-associated diseases. Determining the fundamental molecular pathways driving senescence instead of apoptosis could enable the identification of senolytic agents to restore tissue homeostasis. Here, we identify thrombomodulin (THBD) signaling as a key molecular determinant of the senescent cell fate. Although normally restricted to endothelial cells, THBD is rapidly upregulated and maintained throughout all phases of the senescence program in aged mammalian tissues and in senescent cell models. Mechanistically, THBD activates a proteolytic feed-forward signaling pathway by stabilizing a multi-protein complex in early endosomes, thus forming a molecular basis for the irreversibility of the senescence program and ensuring senescent cell viability. Therapeutically, THBD signaling depletion or inhibition using vorapaxar, an FDA-approved drug, effectively ablates senescent cells and restores tissue homeostasis in liver fibrosis models. Collectively, these results uncover proteolytic THBD signaling as a conserved pro-survival pathway essential for senescent cell viability, thus providing a pharmacologically exploitable senolytic target for senescence-associated diseases.