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Medical researchers are increasingly utilizing advanced LLMs like ChatGPT-4 and Gemini to enhance diagnostic processes in the medical field. This research focuses on their ability to comprehend and apply complex medical classification systems for breast conditions, which can significantly aid plastic surgeons in making informed decisions for diagnosis and treatment, ultimately leading to improved patient outcomes. Fifty clinical scenarios were created to evaluate the classification accuracy of each LLM across five established breast-related classification systems. Scores from 0 to 2 were assigned to LLM responses to denote incorrect, partially correct, or completely correct classifications. Descriptive statistics were employed to compare the performances of ChatGPT-4 and Gemini. Gemini exhibited superior overall performance, achieving 98% accuracy compared to ChatGPT-4's 71%. While both models performed well in the Baker classification for capsular contracture and UTSW classification for gynecomastia, Gemini consistently outperformed ChatGPT-4 in other systems, such as the Fischer Grade Classification for gender-affirming mastectomy, Kajava Classification for ectopic breast tissue, and Regnault Classification for breast ptosis. With further development, integrating LLMs into plastic surgery practice will likely enhance diagnostic support and decision making.
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BACKGROUND: Breast cancer is one of the most common types of cancer, with around 2.3 million cases diagnosed in 2020. One in five cancer patients develops chronic lymphedema caused by multifactorial triggers and treatment-related factors. This can lead to swelling, skin infections, and limb dysfunction, negatively affecting the patient's quality of life. This retrospective cohort study aimed to determine the associations between demographic and breast cancer characteristics and postoperative cellulitis in breast cancer survivors who underwent lymphovenous bypass surgery (LVB) at Mayo Clinic, Florida. METHODS: We performed a retrospective chart review. Data were collected retrospectively from 2016 to 2022. Sixty adult breast cancer survivors who underwent LVB were included in the final analysis based on specific inclusion and exclusion criteria. Patients were excluded if they did not meet the inclusion criteria or had incomplete follow-up data. Demographic and surgical data were extracted, including body mass index (BMI), type of anastomosis, number of anastomoses, and preoperative cellulitis status. Lymphedema measurements were performed using tape measurements. Fisher's exact test was used to determine statistically significant associations between variables and postoperative cellulitis. RESULTS: Postoperative cellulitis was more common in patients aged 60 to 69 years (43.2%), whites (75.0%), overweight or obese (90.9%), with one to four anastomoses (81.8%), and nonsmokers (79.5%). The mean International Society of Lymphology (ISL) criteria for both postoperative cellulitis and no postoperative cellulitis was 1.93. Statistically significant associations with postoperative cellulitis were found for the number of anastomoses (p = 0.021), smoking status (p = 0.049), preoperative cellulitis (p = 0.04), and the length of years with lymphedema diagnosis variable (p = 0.004). CONCLUSION: Our results suggest that a greater number of anastomoses, smoking, preoperative cellulitis, and years with lymphedema are significantly associated with an increased risk of postoperative cellulitis. Awareness of these risk factors is crucial for monitoring and early treatment of infections following surgery.
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Since their release, the medical community has been actively exploring large language models' (LLMs) capabilities, which show promise in providing accurate medical knowledge. One potential application is as a patient resource. This study analyzes and compares the ability of the currently available LLMs, ChatGPT-3.5, GPT-4, and Gemini, to provide postoperative care recommendations to plastic surgery patients. We presented each model with 32 questions addressing common patient concerns after surgical cosmetic procedures and evaluated the medical accuracy, readability, understandability, and actionability of the models' responses. The three LLMs provided equally accurate information, with GPT-3.5 averaging the highest on the Likert scale (LS) (4.18 ± 0.93) (p = 0.849), while Gemini provided significantly more readable (p = 0.001) and understandable responses (p = 0.014; p = 0.001). There was no difference in the actionability of the models' responses (p = 0.830). Although LLMs have shown their potential as adjunctive tools in postoperative patient care, further refinement and research are imperative to enable their evolution into comprehensive standalone resources.
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Background and Objectives: Large language models (LLMs) are emerging as valuable tools in plastic surgery, potentially reducing surgeons' cognitive loads and improving patients' outcomes. This study aimed to assess and compare the current state of the two most common and readily available LLMs, Open AI's ChatGPT-4 and Google's Gemini Pro (1.0 Pro), in providing intraoperative decision support in plastic and reconstructive surgery procedures. Materials and Methods: We presented each LLM with 32 independent intraoperative scenarios spanning 5 procedures. We utilized a 5-point and a 3-point Likert scale for medical accuracy and relevance, respectively. We determined the readability of the responses using the Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) score. Additionally, we measured the models' response time. We compared the performance using the Mann-Whitney U test and Student's t-test. Results: ChatGPT-4 significantly outperformed Gemini in providing accurate (3.59 ± 0.84 vs. 3.13 ± 0.83, p-value = 0.022) and relevant (2.28 ± 0.77 vs. 1.88 ± 0.83, p-value = 0.032) responses. Alternatively, Gemini provided more concise and readable responses, with an average FKGL (12.80 ± 1.56) significantly lower than ChatGPT-4's (15.00 ± 1.89) (p < 0.0001). However, there was no difference in the FRE scores (p = 0.174). Moreover, Gemini's average response time was significantly faster (8.15 ± 1.42 s) than ChatGPT'-4's (13.70 ± 2.87 s) (p < 0.0001). Conclusions: Although ChatGPT-4 provided more accurate and relevant responses, both models demonstrated potential as intraoperative tools. Nevertheless, their performance inconsistency across the different procedures underscores the need for further training and optimization to ensure their reliability as intraoperative decision-support tools.
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Surgery, Plastic , Humans , Surgery, Plastic/methods , Language , Plastic Surgery Procedures/methods , Decision Support Systems, ClinicalABSTRACT
In the U.S., diagnostic errors are common across various healthcare settings due to factors like complex procedures and multiple healthcare providers, often exacerbated by inadequate initial evaluations. This study explores the role of Large Language Models (LLMs), specifically OpenAI's ChatGPT-4 and Google Gemini, in improving emergency decision-making in plastic and reconstructive surgery by evaluating their effectiveness both with and without physical examination data. Thirty medical vignettes covering emergency conditions such as fractures and nerve injuries were used to assess the diagnostic and management responses of the models. These responses were evaluated by medical professionals against established clinical guidelines, using statistical analyses including the Wilcoxon rank-sum test. Results showed that ChatGPT-4 consistently outperformed Gemini in both diagnosis and management, irrespective of the presence of physical examination data, though no significant differences were noted within each model's performance across different data scenarios. Conclusively, while ChatGPT-4 demonstrates superior accuracy and management capabilities, the addition of physical examination data, though enhancing response detail, did not significantly surpass traditional medical resources. This underscores the utility of AI in supporting clinical decision-making, particularly in scenarios with limited data, suggesting its role as a complement to, rather than a replacement for, comprehensive clinical evaluation and expertise.
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Background: Large language models (LLMs) represent a recent advancement in artificial intelligence with medical applications across various healthcare domains. The objective of this review is to highlight how LLMs can be utilized by clinicians and surgeons in their everyday practice. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six databases were searched to identify relevant articles. Eligibility criteria emphasized articles focused primarily on clinical and surgical applications of LLMs. Results: The literature search yielded 333 results, with 34 meeting eligibility criteria. All articles were from 2023. There were 14 original research articles, four letters, one interview, and 15 review articles. These articles covered a wide variety of medical specialties, including various surgical subspecialties. Conclusions: LLMs have the potential to enhance healthcare delivery. In clinical settings, LLMs can assist in diagnosis, treatment guidance, patient triage, physician knowledge augmentation, and administrative tasks. In surgical settings, LLMs can assist surgeons with documentation, surgical planning, and intraoperative guidance. However, addressing their limitations and concerns, particularly those related to accuracy and biases, is crucial. LLMs should be viewed as tools to complement, not replace, the expertise of healthcare professionals.
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Background: OpenAI's ChatGPT (San Francisco, CA, USA) and Google's Gemini (Mountain View, CA, USA) are two large language models that show promise in improving and expediting medical decision making in hand surgery. Evaluating the applications of these models within the field of hand surgery is warranted. This study aims to evaluate ChatGPT-4 and Gemini in classifying hand injuries and recommending treatment. Methods: Gemini and ChatGPT were given 68 fictionalized clinical vignettes of hand injuries twice. The models were asked to use a specific classification system and recommend surgical or nonsurgical treatment. Classifications were scored based on correctness. Results were analyzed using descriptive statistics, a paired two-tailed t-test, and sensitivity testing. Results: Gemini, correctly classifying 70.6% hand injuries, demonstrated superior classification ability over ChatGPT (mean score 1.46 vs. 0.87, p-value < 0.001). For management, ChatGPT demonstrated higher sensitivity in recommending surgical intervention compared to Gemini (98.0% vs. 88.8%), but lower specificity (68.4% vs. 94.7%). When compared to ChatGPT, Gemini demonstrated greater response replicability. Conclusions: Large language models like ChatGPT and Gemini show promise in assisting medical decision making, particularly in hand surgery, with Gemini generally outperforming ChatGPT. These findings emphasize the importance of considering the strengths and limitations of different models when integrating them into clinical practice.
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This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI's role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI's role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers' effectiveness and well-being.
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In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA's responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes.
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Primary Care Physicians (PCPs) are the first point of contact in healthcare. Because PCPs face the challenge of managing diverse patient populations while maintaining up-to-date medical knowledge and updated health records, this study explores the current outcomes and effectiveness of implementing Artificial Intelligence-based Clinical Decision Support Systems (AI-CDSSs) in Primary Healthcare (PHC). Following the PRISMA-ScR guidelines, we systematically searched five databases, PubMed, Scopus, CINAHL, IEEE, and Google Scholar, and manually searched related articles. Only CDSSs powered by AI targeted to physicians and tested in real clinical PHC settings were included. From a total of 421 articles, 6 met our criteria. We found AI-CDSSs from the US, Netherlands, Spain, and China whose primary tasks included diagnosis support, management and treatment recommendations, and complication prediction. Secondary objectives included lessening physician work burden and reducing healthcare costs. While promising, the outcomes were hindered by physicians' perceptions and cultural settings. This study underscores the potential of AI-CDSSs in improving clinical management, patient satisfaction, and safety while reducing physician workload. However, further work is needed to explore the broad spectrum of applications that the new AI-CDSSs have in several PHC real clinical settings and measure their clinical outcomes.
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There is uncertainty whether postoperative application of paper tape (PT) improves scar aesthetics and reduces wound closure complications. This study aimed to review and assess the quality of applicable findings from studies investigating PT's efficacy. We queried PubMed and SCOPUS using the search terms "(("paper tape") AND (wound OR closure OR heal* OR complication OR skin OR prevent* OR scar*))." We excluded articles that were duplicates, basic science, or not clinically relevant. We assessed the level of evidence for each article using the American Society of Plastic Surgeons (ASPS) Rating Levels of Evidence and Grading Recommendations for Therapeutic Studies, ranging from I (highest) to V (lowest). Of 186 publications reviewed, we included eight studies in the literature review. Five of these studies reported statistically significant positive outcomes on scar aesthetics and wound closure associated with using PT. Using the ASPS rating system, we found that two studies were Level I, three studies were Level II, two studies were Level IV, and one study was Level V. Notably, heterogeneity in the study designs limited outcome comparison. The data from the studies included in this literature review support using PT to optimize scar and wound management. The lack of higher levels of evidence, however, suggests the need for additional randomized controlled trials to rigorously evaluate patient outcomes when using PT compared with other forms of adhesive dressings.
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Cicatrix , Wound Healing , Humans , Cicatrix/prevention & control , Bandages , Adhesives , EstheticsABSTRACT
The purpose of the current study was to investigate the nasal colonization and drug resistance profile of Staphylococcus aureus, methicillin-resistant S. aureus (MRSA), and vancomycin-resistant S. aureus (VRSA) in donkeys (n =63), mules (n = 42), and horses (n = 98). MRSA and VRSA were confirmed based on phenotypic and molecular methods, followed by phylogenetic analysis. Furthermore, the association of various animal and management-based risk factors with S. aureus colonization was also evaluated. The presence of nuc gene on polymerase chain reaction showed an overall prevalence of 42.86% of S. aureus in equines. Based on Kirby-Bauer disc diffusion test, 26.44% of isolates showed resistance to vancomycin. Further, mecA and vanB genes were targeted which showed an overall 14.94% and 13.79% prevalence of methicillin and vancomycin-resistant isolates, respectively. The phylogenetic analysis revealed a significant variation of the study isolates with each other and with already reported sequences of mecA and vanB genes. Risk factor analysis revealed that raising purpose (P = .011), work intensity (P < .001), stocking density (P = .006), presence of other livestock animals in surroundings (P = .043), and common drinking water source (P = .023) as significant. Antimicrobial susceptibility testing of MRSA and VRSA isolates showed high resistance to various commonly used antibiotics. Furthermore, all the tested isolates showing resistance to three or more than three antibiotics were considered multiple drug-resistant. The current study manifests the molecular evidence of MRSA and VRSA isolated from equines in Pakistan which will help to address the emerging issue of multidrug resistance in S. aureus in equines and emphasizes the need for possible measures to tackle this issue.
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Horse Diseases , Methicillin-Resistant Staphylococcus aureus , Staphylococcal Infections , Horses , Animals , Staphylococcus aureus/genetics , Methicillin-Resistant Staphylococcus aureus/genetics , Vancomycin , Pakistan/epidemiology , Phylogeny , Microbial Sensitivity Tests/veterinary , Anti-Bacterial Agents/pharmacology , Staphylococcal Infections/epidemiology , Staphylococcal Infections/veterinary , Equidae , Horse Diseases/epidemiologyABSTRACT
Bacteriocins are gaining immense importance in therapeutics since they show significant antibacterial potential. This study reports the bacteriocin KAE01 from Enterococcus faecium, along with its characterization, molecular modeling, and antibacterial potency, by targeting the matrix protein of Pseudomonas aeruginosa. The bacteriocin was purified by using ammonium sulfate precipitation and fast protein liquid chromatography (FPLC), and its molecular weight was estimated as 55 kDa by means of SDS-PAGE. The bacteriocin was found to show stability in a wide range of pH values (2.0-10.0) and temperatures (100 °C for 1 h and 121 °C for 15 min). Antimicrobial screening of the purified peptide against different strains of P. aeruginosa showed its significant antibacterial potential. Scanning electron microscopy of bacteriocin-induced bacterial cultures revealed significant changes in the cellular morphology of the pathogens. In silico molecular modeling of KAE01, followed by molecular docking of the matrix protein (qSA) of P. aeruginosa and KAE01, supported the antibacterial potency and SEM findings of this study.
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Bacteriocins , Lactobacillales , Pseudomonas aeruginosa , Lactobacillales/metabolism , Molecular Docking Simulation , Anti-Bacterial Agents/pharmacology , Anti-Bacterial Agents/metabolism , Bacteriocins/pharmacologyABSTRACT
Sustainable management of iron (Fe) deficiency through the microbial association is highly desirable to ensure crop yield. This study elucidates whether and how arbuscular mycorrhizal fungi (AMF) ameliorate Fe deficiency symptoms in sorghum. AMF inoculation showed a significant improvement in plant biomass, chlorophyll score, Fv/Fm (quantum efficiency of photosystem II), and Pi_ABS (photosynthesis performance index), suggesting its potentiality to diminish Fe deficiency symptoms in sorghum. This AMF-driven prevention of Fe deficiency was further supported by the improvement of biochemical stress indicators, such as cell death, electrolyte leakage, hydrogen peroxide, and superoxide anion. In this study, AMF showed a significant increase in phytosiderophore (PS) release as well as Fe and S concentrations in sorghum under Fe deficiency. Quantitative real-time PCR analysis demonstrated the consistent upregulation of SbDMAS2 (deoxymugineic acid synthase 2), SbNAS2 (nicotianamine synthase 2), and SbYS1 (Fe-phytosiderophore transporter yellow stripe) in roots due to AMF with Fe deficiency. It suggests that the enhancement of Fe due to AMF is related to the mobilization of Fe(III)-PS in the rhizosphere supported by the long-distance transport of Fe by SbYS1 transporter in sorghum. Our study further showed that the elevation of S mainly in the presence of AMF possibly enhances the S-containing antioxidant metabolites (Met, Cys, and GSH) as well as enzymes (CAT, SOD, and GR) to counteract H2O2 and O2- for the restoration of redox status in Fe-deprived sorghum. Moreover, S possibly participates in Strategy II responses revealing its crucial role as a signaling molecule for Fe homeostasis in sorghum.
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Iron Deficiencies , Mycorrhizae/chemistry , Sorghum/metabolism , Oxidation-ReductionABSTRACT
Insulinomas are rare, functional pancreatic neuroendocrine tumors arising from the pancreatic multipotent stem cells or neuroendocrine islet, occurring with a higher proportion in females. Majority of insulinomas have a sporadic etiology; however, only 5%-10% develop as a part of multiple endocrine neoplasm type 1 syndrome. They usually present with symptoms of hypoglycemia including disturbance in orientation, tremors, diaphoresis, altered mental state, seizures and visual changes among others. The diagnosis is based on appreciation of the classic Whipple triad, i.e. neuroglycopenic symptoms and sympathetic drive along with low serum glucose levels (<50 mg/dL) and a complete reversibility of these symptoms with prompt administration of glucose. The gold standard treatment for insulinoma involves complete surgical excision (i.e. enucleation), which is curative in 90% of the patients. Health care physicians should have a high index of suspicion for this tumor in patients presenting with neurological and sympathetic symptoms, particularly if they are resolved after eating. Here, we report the case of a 48-year-old female with the history of multiple episodes of hypoglycemic symptoms for the past two years which improved on glucose intake. Furthermore, we also summarized the discussion regarding diagnosis and management of pancreatic insulinoma.
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With the elderly population increasing in numbers, their demand of adequate medical and psychological care is also rising up. The key goal of promoting physical and mental health in elderly is maintenance of adequate health-related quality of life (QOL). A cross-sectional, descriptive study was conducted from December 2016 to February 2017 among 100 elderly living in nursing facilities and 100 in homes. Older people's quality of Life Questionnaire (OPQOL-35) was utilized to assess their QOL. In the nursing facility dwelling, 17% elderly reported good QOL with highest standardized score in "home and neighbourhood" and lowest in "health" domain. Of the home dwelling elderly, 74% reported good QOL with highest standardized score in "psychological and emotional wellbeing" and lowest in "health" domain.
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Health Status , Homes for the Aged , Independent Living , Mental Health , Nursing Homes , Quality of Life , Aged , Aged, 80 and over , Case-Control Studies , Cross-Sectional Studies , Female , Humans , Male , Pakistan , Residence CharacteristicsABSTRACT
Pheochromocytoma, a rare tumour, arises mainly in the adrenal gland. It consists of chromaffin cells or sympathetic para-ganglia if extra-adrenal. It is an occurrence of rare nature with an incidence of two to eight cases per million annually. Incidence of paediatric pheochromocytoma is estimated at 0.11 benign and 0.02 malignant pheochromocytomas per million children. Children with it present with sustained hypertension in contrast to the adult triad of tachycardia, headache, and diaphoresis. We report a case in which the patient initially presented with right iliac fossa pain indicating appendicitis but later when evaluated and assessed, pheochromocytoma was diagnosed as well. The incidental nature of the finding, keeping in mind the rare occurrence makes the outcome interesting and intriguing at the same time.