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BACKGROUNDS: The use of large language models (LLMs) in medicine can help physicians improve the quality and effectiveness of health care by increasing the efficiency of medical information management, patient care, medical research, and clinical decision-making. METHODS: We collected 34 frequently asked questions about glucocorticoid-induced osteoporosis (GIOP), covering topics related to the disease's clinical manifestations, pathogenesis, diagnosis, treatment, prevention, and risk factors. We also generated 25 questions based on the 2022 American College of Rheumatology Guideline for the Prevention and Treatment of Glucocorticoid-Induced Osteoporosis (2022 ACR-GIOP Guideline). Each question was posed to the LLM (ChatGPT-3.5, ChatGPT-4, and Google Gemini), and three senior orthopedic surgeons independently rated the responses generated by the LLMs. Three senior orthopedic surgeons independently rated the answers based on responses ranging between 1 and 4 points. A total score (TS) > 9 indicated 'good' responses, 6 ≤ TS ≤ 9 indicated 'moderate' responses, and TS < 6 indicated 'poor' responses. RESULTS: In response to the general questions related to GIOP and the 2022 ACR-GIOP Guidelines, Google Gemini provided more concise answers than the other LLMs. In terms of pathogenesis, ChatGPT-4 had significantly higher total scores (TSs) than ChatGPT-3.5. The TSs for answering questions related to the 2022 ACR-GIOP Guideline by ChatGPT-4 were significantly higher than those for Google Gemini. ChatGPT-3.5 and ChatGPT-4 had significantly higher self-corrected TSs than pre-corrected TSs, while Google Gemini self-corrected for responses that were not significantly different than before. CONCLUSIONS: Our study showed that Google Gemini provides more concise and intuitive responses than ChatGPT-3.5 and ChatGPT-4. ChatGPT-4 performed significantly better than ChatGPT3.5 and Google Gemini in terms of answering general questions about GIOP and the 2022 ACR-GIOP Guidelines. ChatGPT3.5 and ChatGPT-4 self-corrected better than Google Gemini.

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BACKGROUND: Large language models (LLMs) such as ChatGPT-4 and Google Gemini show potential for patient health education, but concerns about their accuracy require careful evaluation. This study evaluates the readability and accuracy of ChatGPT-4 and Google Gemini in answering questions about retinal detachment. METHODS: Comparative study analyzing responses from ChatGPT-4 and Google Gemini to 13 retinal detachment questions, categorized by difficulty levels (D1, D2, D3). Masked responses were reviewed by ten vitreoretinal specialists and rated on correctness, errors, thematic accuracy, coherence, and overall quality grading. Analysis included Flesch Readability Ease Score, word and sentence counts. RESULTS: Both Artificial Intelligence tools required college-level understanding for all difficulty levels. Google Gemini was easier to understand (p = 0.03), while ChatGPT-4 provided more correct answers for the more difficult questions (p = 0.0005) with fewer serious errors. ChatGPT-4 scored highest on most challenging questions, showing superior thematic accuracy (p = 0.003). ChatGPT-4 outperformed Google Gemini in 8 of 13 questions, with higher overall quality grades in the easiest (p = 0.03) and hardest levels (p = 0.0002), showing a lower grade as question difficulty increased. CONCLUSIONS: ChatGPT-4 and Google Gemini effectively address queries about retinal detachment, offering mostly accurate answers with few critical errors, though patients require higher education for comprehension. The implementation of AI tools may contribute to improving medical care by providing accurate and relevant healthcare information quickly.

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INTRODUCTION: The rapid advancement of artificial intelligence (AI), particularly in large language models like ChatGPT and Google's Gemini AI, marks a transformative era in technological innovation. This study explores the potential of AI in ophthalmology, focusing on the capabilities of ChatGPT and Gemini AI. While these models hold promise for medical education and clinical support, their integration requires comprehensive evaluation. This research aims to bridge a gap in the literature by comparing Gemini AI and ChatGPT, assessing their performance against ophthalmology residents using a dataset derived from ophthalmology board exams. METHODS: A dataset comprising 600 questions across 12 subspecialties was curated from Israeli ophthalmology residency exams, encompassing text and image-based formats. Four AI models - ChatGPT-3.5, ChatGPT-4, Gemini, and Gemini Advanced - underwent testing on this dataset. The study includes a comparative analysis with Israeli ophthalmology residents, employing specific metrics for performance assessment. RESULTS: Gemini Advanced demonstrated superior performance with a 66% accuracy rate. Notably, ChatGPT-4 exhibited improvement at 62%, Gemini at 58%, and ChatGPT-3.5 served as the reference at 46%. Comparative analysis with residents offered insights into AI models' performance relative to human-level medical knowledge. Further analysis delved into yearly performance trends, topic-specific variations, and the impact of images on chatbot accuracy. CONCLUSION: The study unveils nuanced AI model capabilities in ophthalmology, emphasizing domain-specific variations. The superior performance of Gemini Advanced superior performance indicates significant advancements, while ChatGPT-4's improvement is noteworthy. Both Gemini and ChatGPT-3.5 demonstrated commendable performance. The comparative analysis underscores AI's evolving role as a supplementary tool in medical education. This research contributes vital insights into AI effectiveness in ophthalmology, highlighting areas for refinement. As AI models evolve, targeted improvements can enhance adaptability across subspecialties, making them valuable tools for medical professionals and enriching patient care. KEY MESSAGES: What is known AI breakthroughs, like ChatGPT and Google's Gemini AI, are reshaping healthcare. In ophthalmology, AI integration has overhauled clinical workflows, particularly in analyzing images for diseases like diabetic retinopathy and glaucoma. What is new This study presents a pioneering comparison between Gemini AI and ChatGPT, evaluating their performance against ophthalmology residents using a meticulously curated dataset derived from real-world ophthalmology board exams. Notably, Gemini Advanced demonstrates superior performance, showcasing substantial advancements, while the evolution of ChatGPT-4 also merits attention. Both models exhibit commendable capabilities. These findings offer crucial insights into the efficacy of AI in ophthalmology, shedding light on areas ripe for further enhancement and optimization.

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Surfactants are hailed as "industrial monosodium glutamate", and are widely used as emulsifiers, demulsifiers, water treatment agents, etc., in the petroleum industry. However, due to the unidirectivity of conventional surfactants, the difficulty in demulsifying petroleum emulsions generated after emulsification with such surfactants increases sharply. Therefore, it is of great significance and application value to design and develop a novel switchable surfactant for oil exploitation. In this study, a CO2-switchable Gemini surfactant of N,N'-dimethyl-N,N'-didodecyl butylene diamine (DMDBA) was synthesized from 1, 4-dibromobutane, dodecylamine, formic acid, and formaldehyde. Then, the synthesized surfactant was structurally characterized by infrared (IR) spectroscopy, hydrogen nuclear magnetic resonance (1H NMR) spectroscopy, and electrospray ionization mass spectrometry (ESI-MS); the changes in conductivity and Zeta potential of DMDBA before and after CO2/N2 injection were also studied. The results show that DMDBA had a good CO2 response and cycle reversibility. The critical micelle concentration (CMC) of cationic surfactant obtained from DMDBA by injecting CO2 was 1.45 × 10-4 mol/L, the surface tension at CMC was 33.4 mN·m-1, and the contact angle with paraffin was less than 90°, indicating that it had a good surface activity and wettability. In addition, the kinetic law of the process of producing surfactant by injecting CO2 was studied, and it was found that the process was a second-order reaction. The influence of temperature and gas velocity on the reaction dynamics was explored. The calculated values from the equation were in good agreement with the measured values, with a correlation coefficient greater than 0.9950. The activation energy measured during the formation of surfactant was Ea = 91.16 kJ/mol.

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Background: Medication review and reconciliation is essential for optimizing drug therapy and minimizing medication errors. Large language models (LLMs) have been recently shown to possess a lot of potential applications in healthcare field due to their abilities of deductive, abductive, and logical reasoning. The present study assessed the abilities of LLMs in medication review and medication reconciliation processes. Methods: Four LLMs were prompted with appropriate queries related to dosing regimen errors, drug-drug interactions, therapeutic drug monitoring, and genomics-based decision-making process. The veracity of the LLM outputs were verified from validated sources using pre-validated criteria (accuracy, relevancy, risk management, hallucination mitigation, and citations and guidelines). The impacts of the erroneous responses on the patients' safety were categorized either as major or minor. Results: In the assessment of four LLMs regarding dosing regimen errors, drug-drug interactions, and suggestions for dosing regimen adjustments based on therapeutic drug monitoring and genomics-based individualization of drug therapy, responses were generally consistent across prompts with no clear pattern in response quality among the LLMs. For identification of dosage regimen errors, ChatGPT performed well overall, except for the query related to simvastatin. In terms of potential drug-drug interactions, all LLMs recognized interactions with warfarin but missed the interaction between metoprolol and verapamil. Regarding dosage modifications based on therapeutic drug monitoring, Claude-Instant provided appropriate suggestions for two scenarios and nearly appropriate suggestions for the other two. Similarly, for genomics-based decision-making, Claude-Instant offered satisfactory responses for four scenarios, followed by Gemini for three. Notably, Gemini stood out by providing references to guidelines or citations even without prompting, demonstrating a commitment to accuracy and reliability in its responses. Minor impacts were noted in identifying appropriate dosing regimens and therapeutic drug monitoring, while major impacts were found in identifying drug interactions and making pharmacogenomic-based therapeutic decisions. Conclusion: Advanced LLMs hold significant promise in revolutionizing the medication review and reconciliation process in healthcare. Diverse impacts on patient safety were observed. Integrating and validating LLMs within electronic health records and prescription systems is essential to harness their full potential and enhance patient safety and care quality.

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Surfactant-enhanced aquifer remediation (SEAR) has effectively removed dense nonaqueous phase liquids (DNAPLs) from the contaminated aquifers. However, restricted by structural defects, typical monomeric surfactants undergo precipitation, high adsorption loss, and poor solubilization in aquifers, resulting in low remediation efficiency. In this study, a novel sugar-based anionic and non-ionic Gemini surfactant (SANG) was designed and synthesized for SEAR. Glucose was introduced into SANG as a non-ionic group to overcome the interference of low temperature and ions in groundwater. Sodium sulfonate was introduced as an anionic group to overcome aquifer adsorption loss. Two long-straight carbon chains were introduced as hydrophobic groups to provide high surface activity and solubilizing capacity. Even with low temperature or high salt content, its solution did not precipitate in aquifer conditions. The adsorption loss was as low as 0.54 and 0.90 mg/g in medium and fine sand, respectively. Compared with typical surfactants used for SEAR, SANG had the highest solubilization and desorption abilities for perchloroethylene (PCE) without emulsification, a crucial negative that Tween80 and other non-ionic surfactants exhibit. After flushing the contaminated aquifer using SANG, > 99 % of PCE was removed. Thus, with low potential environmental risk, SANG is effectively applicable in subsurface remediation, making it a better surfactant choice for SEAR.

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INTRODUCTION: GPT-4, GPT-4o and Gemini advanced, which are among the well-known large language models (LLMs), have the capability to recognize and interpret visual data. When the literature is examined, there are a very limited number of studies examining the ECG performance of GPT-4. However, there is no study in the literature examining the success of Gemini and GPT-4o in ECG evaluation. The aim of our study is to evaluate the performance of GPT-4, GPT-4o, and Gemini in ECG evaluation, assess their usability in the medical field, and compare their accuracy rates in ECG interpretation with those of cardiologists and emergency medicine specialists. METHODS: The study was conducted from May 14, 2024, to June 3, 2024. The book "150 ECG Cases" served as a reference, containing two sections: daily routine ECGs and more challenging ECGs. For this study, two emergency medicine specialists selected 20 ECG cases from each section, totaling 40 cases. In the next stage, the questions were evaluated by emergency medicine specialists and cardiologists. In the subsequent phase, a diagnostic question was entered daily into GPT-4, GPT-4o, and Gemini Advanced on separate chat interfaces. In the final phase, the responses provided by cardiologists, emergency medicine specialists, GPT-4, GPT-4o, and Gemini Advanced were statistically evaluated across three categories: routine daily ECGs, more challenging ECGs, and the total number of ECGs. RESULTS: Cardiologists outperformed GPT-4, GPT-4o, and Gemini Advanced in all three groups. Emergency medicine specialists performed better than GPT-4o in routine daily ECG questions and total ECG questions (p = 0.003 and p = 0.042, respectively). When comparing GPT-4o with Gemini Advanced and GPT-4, GPT-4o performed better in total ECG questions (p = 0.027 and p < 0.001, respectively). In routine daily ECG questions, GPT-4o also outperformed Gemini Advanced (p = 0.004). Weak agreement was observed in the responses given by GPT-4 (p < 0.001, Fleiss Kappa = 0.265) and Gemini Advanced (p < 0.001, Fleiss Kappa = 0.347), while moderate agreement was observed in the responses given by GPT-4o (p < 0.001, Fleiss Kappa = 0.514). CONCLUSION: While GPT-4o shows promise, especially in more challenging ECG questions, and may have potential as an assistant for ECG evaluation, its performance in routine and overall assessments still lags behind human specialists. The limited accuracy and consistency of GPT-4 and Gemini suggest that their current use in clinical ECG interpretation is risky.

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The persistent presence of covalently closed circular DNA (cccDNA) in hepatocyte nuclei poses a significant obstacle to achieving a comprehensive cure for hepatitis B virus (HBV). Current applications of CRISPR/Cas9 for targeting and eliminating cccDNA have been confined to in vitro studies due to challenges in stable cccDNA expression in animal models and the limited non-immunogenicity of delivery systems. This study addresses these limitations by introducing a novel non-viral gene delivery system utilizing Gemini Surfactant (GS). The developed system creates stable and targeted CRISPR/Cas9 nanodrugs with a negatively charged surface through modification with red blood cell membranes (RBCM) or hepatocyte membranes (HCM), resulting in GS-pDNA@Cas9-CMs complexes. These GS-pDNA complexes demonstrated complete formation at a 4:1 w/w ratio. The in vitro transfection efficiency of GS-pDNA-HCM reached 54.61%, showing homotypic targeting and excellent safety. Additionally, the study identified the most effective single-guide RNA (sgRNA) from six sequences delivered by GS-pDNA@Cas9-HCM. Using GS-pDNA@Cas9-HCM, a significant reduction of 96.47% in in vitro HBV cccDNA and a 52.34% reduction in in vivo HBV cccDNA were observed, along with a notable decrease in other HBV-related markers. The investigation of GS complex uptake by AML-12 cells under varied time and temperature conditions revealed clathrin-mediated endocytosis (CME) for GS-pDNA and caveolin-mediated endocytosis (CVME) for GS-pDNA-HCM and GS-pDNA-RBCM. In summary, this research presents biomimetic gene-editing nanovectors based on GS (GS-pDNA@Cas9-CMs) and explores their precise and targeted clearance of cccDNA using CRISPR/Cas9, demonstrating good biocompatibility both in vitro and in vivo. This innovative approach provides a promising therapeutic strategy for advancing the cure of HBV.

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Introduction The latest generation of large language models (LLMs) features multimodal capabilities, allowing them to interpret graphics, images, and videos, which are crucial in medical fields. This study investigates the vision capabilities of the next-generation Generative Pre-trained Transformer 4 (GPT-4) and Google's Gemini. Methods To establish a comparative baseline, we used GPT-3.5, a model limited to text processing, and evaluated the performance of both GPT-4 and Gemini on questions from the Taiwan Specialist Board Exams in Pulmonary and Critical Care Medicine. Our dataset included 1,100 questions from 2012 to 2023, with 100 questions per year. Of these, 1,059 were in pure text and 41 were text with images, with the majority in a non-English language and only six in pure English. Results For each annual exam consisting of 100 questions from 2013 to 2023, GPT-4 achieved scores of 66, 69, 51, 64, 72, 64, 66, 64, 63, 68, and 67, respectively. Gemini scored 45, 48, 45, 45, 46, 59, 54, 41, 53, 45, and 45, while GPT-3.5 scored 39, 33, 35, 36, 32, 33, 43, 28, 32, 33, and 36. Conclusions These results demonstrate that the newer LLMs with vision capabilities significantly outperform the text-only model. When a passing score of 60 was set, GPT-4 passed most exams and approached human performance.

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Cationic gemini surfactants have emerged as potential gene delivery agents as they can co-assemble with DNA due to a strong electrostatic association. Commonly, DNA complexation is enhanced by the inclusion of a helper lipid (HL), which also plays a key role in transfection efficiency. The formation of lipoplexes, used as non-viral vectors for transfection, through electrostatic and hydrophobic interactions is affected by various physicochemical parameters, such as cationic surfactant:HL molar ratio, (+/-) charge ratio, and the morphological structure of the lipoplexes. Herein, we investigated the DNA complexation ability of mixtures of serine-based gemini surfactants, (nSer)2N5, and monoolein (MO) as a helper lipid. The micelle-forming serine surfactants contain long lipophilic chains (12 to 18 C atoms) and a five CH2 spacer, both linked to the nitrogen atoms of the serine residues by amine linkages. The (nSer)2N5:MO aggregates are non-cytotoxic up to 35-90 µM, depending on surfactant and surfactant/MO mixing ratio, and in general, higher MO content and longer surfactant chain length tend to promote higher cell viability. All systems efficaciously complex DNA, but the (18Ser)2N5:MO one clearly stands as the best-performing one. Incorporating MO into the serine surfactant system affects the morphology and size distribution of the formed mixed aggregates. In the low concentration regime, gemini-MO systems aggregate in the form of vesicles, while at high concentrations the formation of a lamellar liquid crystalline phase is observed. This suggests that lipoplexes might share a similar bilayer-based structure.

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Background: End-of-life care (EOLC) is a critical aspect of healthcare, yet accessing reliable information remains challenging, particularly in culturally diverse contexts like India. Objective: This study investigates the potential of artificial intelligence (AI) in addressing the informational gap by analyzing patient information leaflets (PILs) generated by AI chatbots on EOLC. Methodology: Using a comparative research design, PILs generated by ChatGPT and Google Gemini were evaluated for readability, sentiment, accuracy, completeness, and suitability. Readability was assessed using established metrics, sentiment analysis determined emotional tone, accuracy, and completeness were rated by subject experts, and suitability was evaluated using the Patient Education Materials Assessment Tool (PEMAT). Results: Google Gemini PILs exhibited superior readability and actionability compared to ChatGPT PILs. Both conveyed positive sentiments and high levels of accuracy and completeness, with Google Gemini PILs showing slightly lower accuracy scores. Conclusion: The findings highlight the promising role of AI in enhancing patient education in EOLC, with implications for improving care outcomes and promoting informed decision-making in diverse cultural settings. Ongoing refinement and innovation in AI-driven patient education strategies are needed to ensure compassionate and culturally sensitive EOLC. How to cite this article: Gondode PG, Khanna P, Sharma P, Duggal S, Garg N. End-of-life Care Patient Information Leaflets-A Comparative Evaluation of Artificial Intelligence-generated Content for Readability, Sentiment, Accuracy, Completeness, and Suitability: ChatGPT vs Google Gemini. Indian J Crit Care Med 2024;28(6):561-568.

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During the war time dysregulation of negative emotions such as fear, anger, hatred, frustration, sadness, humiliation, and hopelessness can overrule normal societal values, culture, and endanger global peace and security, and mental health in affected societies. Therefore, it is understandable that the range and power of negative emotions may play important roles in consideration of human behavior in any armed conflict. The estimation and assessment of dominant negative emotions during war time are crucial but are challenged by the complexity of emotions' neuro-psycho-physiology. Currently available natural language processing (NLP) tools have comprehensive computational methods to analyze and understand the emotional content of related textual data in war-inflicted societies. Innovative AI-driven technologies incorporating machine learning, neuro-linguistic programming, cloud infrastructure, and novel digital therapeutic tools and applications present an immense potential to enhance mental health care worldwide. This advancement could make mental health services more cost-effective and readily accessible. Due to the inadequate number of psychiatrists and limited psychiatric resources in coping with mental health consequences of war and traumas, new digital therapeutic wearable devices supported by AI tools and means might be promising approach in psychiatry of future. Transformation of negative dominant emotional maps might be undertaken by the simultaneous combination of online cognitive behavioral therapy (CBT) on individual level, as well as usage of emotionally based strategic communications (EBSC) on a public level. The proposed positive emotional transformation by means of CBT and EBSC may provide important leverage in efforts to protect mental health of civil population in war-inflicted societies. AI-based tools that can be applied in design of EBSC stimuli, like Open AI Chat GPT or Google Gemini may have great potential to significantly enhance emotionally based strategic communications by more comprehensive understanding of semantic and linguistic analysis of available text datasets of war-traumatized society. Human in the loop enhanced by Chat GPT and Gemini can aid in design and development of emotionally annotated messages that resonate among targeted population, amplifying the impact of strategic communications in shaping human dominant emotional maps into a more positive by CBT and EBCS.

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BACKGROUND: We aimed to evaluate the baseline performance and improvement of ChatGPT-4 "omni" (ChatGPT-4o) and Gemini 1.5 Flash (Gemini 1.5) in answering multiple-choice questions related to pediatric nephrology after specific training. METHODS: Using questions from the "Educational Review" articles published by Pediatric Nephrology between January 2014 and April 2024, the models were tested both before and after specific training with Portable Data Format (PDF) and text (TXT) file formats of the Educational Review articles removing the last page containing the correct answers using a Python script. The number of correct answers was recorded. RESULTS: Before training, ChatGPT-4o correctly answered 75.2% of the 1395 questions, outperforming Gemini 1.5, which answered 64.9% correctly (p < 0.001). After training with PDF files, ChatGPT-4o's accuracy increased to 77.8%, while Gemini 1.5 improved significantly to 84.7% (p < 0.001). Training with TXT files showed similar results, with ChatGPT-4o maintaining 77.8% accuracy and Gemini 1.5 further improving to 87.6% (p < 0.001). CONCLUSIONS: The study highlights that while ChatGPT-4o has strong baseline performance, specific training does not significantly enhance its accuracy. Conversely, Gemini 1.5, despite its lower initial performance, shows substantial improvement with training, particularly with TXT files. These findings suggest Gemini 1.5's superior ability to store and retrieve information, making it potentially more effective in clinical applications, albeit with a dependency on additional data for optimal performance.

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BACKGROUND: Artificial intelligence (AI) is a burgeoning new field that has increased in popularity over the past couple of years, coinciding with the public release of large language model (LLM)-driven chatbots. These chatbots, such as ChatGPT, can be engaged directly in conversation, allowing users to ask them questions or issue other commands. Since LLMs are trained on large amounts of text data, they can also answer questions reliably and factually, an ability that has allowed them to serve as a source for medical inquiries. This study seeks to assess the readability of patient education materials on cardiac catheterization across four of the most common chatbots: ChatGPT, Microsoft Copilot, Google Gemini, and Meta AI. METHODOLOGY: A set of 10 questions regarding cardiac catheterization was developed using website-based patient education materials on the topic. We then asked these questions in consecutive order to four of the most common chatbots: ChatGPT, Microsoft Copilot, Google Gemini, and Meta AI. The Flesch Reading Ease Score (FRES) was used to assess the readability score. Readability grade levels were assessed using six tools: Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Coleman-Liau Index (CLI), Simple Measure of Gobbledygook (SMOG) Index, Automated Readability Index (ARI), and FORCAST Grade Level. RESULTS: The mean FRES across all four chatbots was 40.2, while overall mean grade levels for the four chatbots were 11.2, 13.7, 13.7, 13.3, 11.2, and 11.6 across the FKGL, GFI, CLI, SMOG, ARI, and FORCAST indices, respectively. Mean reading grade levels across the six tools were 14.8 for ChatGPT, 12.3 for Microsoft Copilot, 13.1 for Google Gemini, and 9.6 for Meta AI. Further, FRES values for the four chatbots were 31, 35.8, 36.4, and 57.7, respectively. CONCLUSIONS: This study shows that AI chatbots are capable of providing answers to medical questions regarding cardiac catheterization. However, the responses across the four chatbots had overall mean reading grade levels at the 11th-13th-grade level, depending on the tool used. This means that the materials were at the high school and even college reading level, which far exceeds the recommended sixth-grade level for patient education materials. Further, there is significant variability in the readability levels provided by different chatbots as, across all six grade-level assessments, Meta AI had the lowest scores and ChatGPT generally had the highest.

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Applications of large language models (LLMs) in the healthcare field have shown promising results in processing and summarizing multidisciplinary information. This study evaluated the ability of three publicly available LLMs (GPT-3.5, GPT-4, and Google Gemini-then called Bard) to answer 60 multiple-choice questions (29 sourced from public databases, 31 newly formulated by experienced breast radiologists) about different aspects of breast cancer care: treatment and prognosis, diagnostic and interventional techniques, imaging interpretation, and pathology. Overall, the rate of correct answers significantly differed among LLMs (p = 0.010): the best performance was achieved by GPT-4 (95%, 57/60) followed by GPT-3.5 (90%, 54/60) and Google Gemini (80%, 48/60). Across all LLMs, no significant differences were observed in the rates of correct replies to questions sourced from public databases and newly formulated ones (p ≥ 0.593). These results highlight the potential benefits of LLMs in breast cancer care, which will need to be further refined through in-context training.

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BACKGROUND: In recent years, the integration of large language models (LLMs) into healthcare has emerged as a revolutionary approach to enhancing doctor-patient communication, particularly in the management of diseases such as prostate cancer. METHODS: Our paper evaluated the effectiveness of three prominent LLMs-ChatGPT (3.5), Gemini (Pro), and Co-Pilot (the free version)-against the official Romanian Patient's Guide on prostate cancer. Employing a randomized and blinded method, our study engaged eight medical professionals to assess the responses of these models based on accuracy, timeliness, comprehensiveness, and user-friendliness. RESULTS: The primary objective was to explore whether LLMs, when operating in Romanian, offer comparable or superior performance to the Patient's Guide, considering their potential to personalize communication and enhance the informational accessibility for patients. Results indicated that LLMs, particularly ChatGPT, generally provided more accurate and user-friendly information compared to the Guide. CONCLUSIONS: The findings suggest a significant potential for LLMs to enhance healthcare communication by providing accurate and accessible information. However, variability in performance across different models underscores the need for tailored implementation strategies. We highlight the importance of integrating LLMs with a nuanced understanding of their capabilities and limitations to optimize their use in clinical settings.

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Gemini surfactants have become the research focus of novel excellent inhibitors because of their special structure (two amphiphilic moieties covalently connected at head group by a spacer) and excellent surface properties. It is proved by theoretical calculations that 1, 3-bis (dodecyl dimethyl ammonium chloride) propane (BDDACP) molecules can perform electron transfer with Fe (110). And it has a small fraction free volume, thus greatly reducing the diffusion and migration degree of corrosive particles. The potentiodynamic polarization curve showed that coefficients of cathodic and anodic reaction less than 1 and polarization resistance increased to 1602.9 Ω cm-2 after added BDDACP, confirming that BDDACP significantly inhibited the corrosion reaction by occupying the active site. The electrochemical impedance spectrum of imperfect semi-circle shows that the system resistance increases and double layer capacitance after added BDDACP. Weight loss tests also confirmed that BDDACP forms protective film by occupying the active sites on steel surface, and the maximum inhibition efficiency is 92 %. Comparison of the microscopic morphology showed that steel surface roughness was significantly reduced after added BDDACP. The results of time-of-flight secondary ion mass spectrometry show that steel surface contains some elements from BDDACP, which confirms the adsorption of BDDACP on steel surface.

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Large language models (LLM) have been widely used to provide information in many fields, including obstetrics and gynecology. Which model performs best in providing answers to commonly asked pregnancy questions is unknown. A qualitative analysis of Chat Generative Pre-Training Transformer Version 3.5 (ChatGPT-3.5) (OpenAI, Inc., San Francisco, California, United States) and Bard, recently renamed Google Gemini (Google LLC, Mountain View, California, United States), was performed in August of 2023. Each LLM was queried on 12 commonly asked pregnancy questions and asked for their references. Review and grading of the responses and references for both LLMs were performed by the co-authors individually and then as a group to formulate a consensus. Query responses were graded as "acceptable" or "not acceptable" based on correctness and completeness in comparison to American College of Obstetricians and Gynecologists (ACOG) publications, PubMed-indexed evidence, and clinical experience. References were classified as "verified," "broken," "irrelevant," "non-existent," and "no references." Grades of "acceptable" were given to 58% of ChatGPT-3.5 responses (seven out of 12) and 83% of Bard responses (10 out of 12). In regard to references, ChatGPT-3.5 had reference issues in 100% of its references, and Bard had discrepancies in 8% of its references (one out of 12). When comparing ChatGPT-3.5 responses between May 2023 and August 2023, a change in "acceptable" responses was noted: 50% versus 58%, respectively. Bard answered more questions correctly than ChatGPT-3.5 when queried on a small sample of commonly asked pregnancy questions. ChatGPT-3.5 performed poorly in terms of reference verification. The overall performance of ChatGPT-3.5 remained stable over time, with approximately one-half of responses being "acceptable" in both May and August of 2023. Both LLMs need further evaluation and vetting before being accepted as accurate and reliable sources of information for pregnant women.

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OBJECTIVE: Large language models (LLMs) are gaining popularity due to their ability to communicate in a human-like manner. Their potential for science, including urology, is increasingly recognized. However, unresolved concerns regarding transparency, accountability, and the accuracy of LLM results still exist. RESEARCH QUESTION: This review examines the ethical, technical, and practical challenges as well as the potential applications of LLMs in urology and science. MATERIALS AND METHODS: A selective literature review was conducted to analyze current findings and developments in the field of LLMs. The review considered studies on technical aspects, ethical considerations, and practical applications in research and practice. RESULTS: LLMs, such as GPT from OpenAI and Gemini from Google, show great potential for processing and analyzing text data. Applications in urology include creating patient information and supporting administrative tasks. However, for purely clinical and scientific questions, the methods do not yet seem mature. Currently, concerns about ethical issues and the accuracy of results persist. CONCLUSION: LLMs have the potential to support research and practice through efficient data processing and information provision. Despite their advantages, ethical concerns and technical challenges must be addressed to ensure responsible and trustworthy use. Increased implementation could reduce the workload of urologists and improve communication with patients.

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