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As advancements in artificial intelligence (AI) continue, large language models (LLMs) have emerged as promising tools for generating medical information. Their rapid adaptation and potential benefits in health care require rigorous assessment in terms of the quality, accuracy, and safety of the generated information across diverse medical specialties. This study aimed to evaluate the performance of 4 prominent LLMs, namely, Claude-instant-v1.0, GPT-3.5-Turbo, Command-xlarge-nightly, and Bloomz, in generating medical content spanning the clinical specialties of ophthalmology, orthopedics, and dermatology. Three domain-specific physicians evaluated the AI-generated therapeutic recommendations for a diverse set of 60 diseases. The evaluation criteria involved the mDISCERN score, correctness, and potential harmfulness of the recommendations. ANOVA and pairwise t tests were used to explore discrepancies in content quality and safety across models and specialties. Additionally, using the capabilities of OpenAI’s most advanced model, GPT-4, an automated evaluation of each model’s responses to the diseases was performed using the same criteria and compared to the physicians’ assessments through Pearson correlation analysis. Claude-instant-v1.0 emerged with the highest mean mDISCERN score (3.35, 95% CI 3.23-3.46). In contrast, Bloomz lagged with the lowest score (1.07, 95% CI 1.03-1.10). Our analysis revealed significant differences among the models in terms of quality (P<.001). Evaluating their reliability, the models displayed strong contrasts in their falseness ratings, with variations both across models (P<.001) and specialties (P<.001). Distinct error patterns emerged, such as confusing diagnoses; providing vague, ambiguous advice; or omitting critical treatments, such as antibiotics for infectious diseases. Regarding potential harm, GPT-3.5-Turbo was found to be the safest, with the lowest harmfulness rating. All models lagged in detailing the risks associated with treatment procedures, explaining the effects of therapies on quality of life, and offering additional sources of information. Pearson correlation analysis underscored a substantial alignment between physician assessments and GPT-4’s evaluations across all established criteria (P<.01). This study, while comprehensive, was limited by the involvement of a select number of specialties and physician evaluators. The straightforward prompting strategy (“How to treat…”) and the assessment benchmarks, initially conceptualized for human-authored content, might have potential gaps in capturing the nuances of AI-driven information. The LLMs evaluated showed a notable capability in generating valuable medical content; however, evident lapses in content quality and potential harm signal the need for further refinements. Given the dynamic landscape of LLMs, this study’s findings emphasize the need for regular and methodical assessments, oversight, and fine-tuning of these AI tools to ensure they produce consistently trustworthy and clinically safe medical advice. Notably, the introduction of an auto-evaluation mechanism using GPT-4, as detailed in this study, provides a scalable, transferable method for domain-agnostic evaluations, extending beyond therapy recommendation assessments.

Steam methane (CH4–H2O) reforming in the presence of a catalyst, usually nickel, is the most common technology for generating synthesis gas as a feedstock in chemical synthesis and a source of pure H2 and CO. What is essential from the perspective of further gas use is the parameter describing a ratio of equilibrium concentration of hydrogen to carbon monoxide (H/C=xH2/xCO). The parameter is determined by operating temperature and the initial ratio of steam concentration to methane SC=xH2O0/xCH40. In this paper, the author presents a thermodynamic analysis of the effect of green hydrogen addition to a fuel mixture on the steam methane reforming process of gaseous phase (CH4/H2)–H2O. The thermodynamic analysis of conversion of hydrogen-enriched methane (CH4/H2)–H2O has been performed using parametric equation formalism, allowing for determining the equilibrium composition of the process in progress. A thermodynamic condition of carbon precipitation in methane reforming (CH4/H2) with the gaseous phase of H2O has been interpreted. The ranges of substrate concentrations creating carbon deposition for temperature T = 1000 K have been determined, based on the technologies used. The results obtained can serve as a model basis for describing the properties of steam reforming of methane and hydrogen mixture (CH4/H2)–H2O.

Background:Large language models (LLMs) have demonstrated significant potential in diverse domains, including medicine. Nonetheless, there is a scarcity of studies examining their performance in medical examinations, especially those conducted in languages other than English, and in direct comparison with medical students. Analyzing the performance of LLMs in state medical examinations can provide insights into their capabilities and limitations and evaluate their potential role in medical education and examination preparation.Objective:This study aimed to assess and compare the performance of 3 LLMs, GPT-4, Bing, and GPT-3.5-Turbo, in the German Medical State Examinations of 2022 and to evaluate their performance relative to that of medical students.Methods:The LLMs were assessed on a total of 630 questions from the spring and fall German Medical State Examinations of 2022. The performance was evaluated with and without media-related questions. Statistical analyses included 1-way ANOVA and independent samples t tests for pairwise comparisons. The relative strength of the LLMs in comparison with that of the students was also evaluated.Results:GPT-4 achieved the highest overall performance, correctly answering 88.1% of questions, closely followed by Bing (86.0%) and GPT-3.5-Turbo (65.7%). The students had an average correct answer rate of 74.6%. Both GPT-4 and Bing significantly outperformed the students in both examinations. When media questions were excluded, Bing achieved the highest performance of 90.7%, closely followed by GPT-4 (90.4%), while GPT-3.5-Turbo lagged (68.2%). There was a significant decline in the performance of GPT-4 and Bing in the fall 2022 examination, which was attributed to a higher proportion of media-related questions and a potential increase in question difficulty.Conclusions:LLMs, particularly GPT-4 and Bing, demonstrate potential as valuable tools in medical education and for pretesting examination questions. Their high performance, even relative to that of medical students, indicates promising avenues for further development and integration into the educational and clinical landscape.

The assumptions of contemporary energy policies are increasing the share of renewable energy sources. Biomass combustion is developing as an alternative to fossil fuels. However, it faces challenges such as limited corrosion resistance of steel boiler components due to chloride compounds in flue gases and fly ash. This paper provides a comprehensive thermodynamic analysis of chloride-induced corrosion in steel in the Fe-O-Cl-Na environment, focusing on the influence of steam concentration in the gas phase. The study was performed by using the general thermodynamic rules, the thermodynamic properties of the pure components involved in the reaction, and the properties of the solutions formed in the liquid and gas phases. The study also examined the impact of alkali metal chlorides, particularly NaCl, on the formation of NaFeO2 in the passive oxide scale layer Fe3O4/Fe2O3. Furthermore, it investigated the condensation of NaCl vapour formation of low-melting eutectic mixtures in deposits and the resulting consequences on the corrosion process. The role of HCl in the chlorination and oxidation process of steel in melted ash deposits was also discussed. The presented thermodynamic analysis was compared with assumptions of an “active oxidation” model. This study can be a valuable resource for experimental research planning and a guide for preventing corrosion in industrial settings.

Digital health technologies hold promise to enhance patient-related outcomes, to support health care staff by reducing their workload, and to improve the coordination of care. As key users of digital health technologies, health care workers are crucial to enable a meaningful digital transformation of health care. Digital health literacy and digital skills should become prerequisite competencies for health professionals to facilitate the implementation and leverage the potential of digital technologies to improve health. We aimed to assess European medical students' perceived knowledge and opinions toward digital health, the status of digital health implementation in medical education, and the students' most pressing needs. The explanatory design of our mixed methods study was based on an online, anonymous, self-administered survey targeted toward European medical students. A linear regression analysis was used to identify the influence of the year of medical studies on the responses. Additional analysis was performed by grouping the responses by the self-evaluated frequency of eHealth technology use. Written responses to four qualitative questions in the survey were analyzed using an inductive approach. The survey received a total of 451 responses from 39 European countries, and there were respondents for every year of medical studies. The majority of respondents saw advantages in the use of digital health. While 40.6% (183/451) felt prepared to work in a digitized health care system, more than half (240/451, 53.2%) evaluated their eHealth skills as poor or very poor. Medical students considered lack of education to be the reason for this, with 84.9% (383/451) agreeing or strongly agreeing that more digital health education should be implemented in the medical curriculum. Students demanded introductory and specific eHealth courses covering data management, ethical aspects, legal frameworks, research and entrepreneurial opportunities, role in public health and health systems, communication skills, and practical training. The emphasis lay on tailoring learning to future job requirements and interprofessional education. This study shows a lack of digital health-related formats in medical education and a perceived lack of digital health literacy among European medical students. Our findings indicate a gap between the willingness of medical students to take an active role by becoming key players in the digital transformation of health care and the education that they receive through their faculties.

This study evaluates multimodal AI models’ accuracy and responsiveness in answering NEJM Image Challenge questions, juxtaposed with human collective intelligence, underscoring AI’s potential and current limitations in clinical diagnostics. Anthropic’s Claude 3 family demonstrated the highest accuracy among the evaluated AI models, surpassing the average human accuracy, while collective human decision-making outperformed all AI models. GPT-4 Vision Preview exhibited selectivity, responding more to easier questions with smaller images and longer questions.

From the chemical point of view the reforming process of heavy hydrocarbons such as Associated Petroleum Gas (APG) is very complex. One of the main issue is a set of undesired chemical reactions that causes deposition of solid carbon and consequently block catalytic property of a reactor. The experimental investigation is crucial to design APG reforming reactors. However, the experiment needs to be preceded by careful thermodynamical analysis to design safe operation conditions. In case of small number of reactants and reactions such as in case of steam reforming of pure methane, the problem can be solved by treating each equilibrium reaction constant as an element of the system of non-linear equations. The system of equations can be solved by Newton-Raphson method. However in case of large number of reactants and reaction, such as in case of APG reforming this method is inefficient. A large number of strongly non-linear equations leads often to converge problem. In this paper the authors suggest to use different approach called Parametric Equation Method. In this method a system of non-linear equations is replaced by a set of single non-linear equations solved separately. The methods were used to simulate steam reforming of methane-ethane rich fuel. The results of computations from both methods were juxtaposed and comparative study were conducted. Finally safe operation conditions for steam reforming of methane-ethane fuel were calculated and presented.

As the digital health landscape continues to evolve, analyzing the progress and direction of the field can yield valuable insights. The Journal of Medical Internet Research (JMIR) has been at the forefront of disseminating digital health research since 1999. A comprehensive network analysis of JMIR publications can help illuminate the evolution and trends in digital medicine over the past 25 years. This study aims to conduct a detailed network analysis of JMIR's publications to uncover the growth patterns, dominant themes, and potential future trajectories in digital health research. We retrieved 8068 JMIR papers from PubMed using the Biopython library. Keyword metrics were assessed using accuracy, recall, and F -scores to evaluate the effectiveness of keyword identification from Claude 3 Opus and Gemini 1.5 Pro in addition to 2 conventional natural language processing methods using key bidirectional encoder representations from transformers. Future trends for 2024-2026 were predicted using Claude 3 Opus, Google's Time Series Foundation Model, autoregressive integrated moving average, exponential smoothing, and Prophet. Network visualization techniques were used to represent and analyze the complex relationships between collaborating countries, paper types, and keyword co-occurrence. JMIR's publication volume showed consistent growth, with a peak in 2020. The United States dominated country contributions, with China showing a notable increase in recent years. Keyword analysis from 1999 to 2023 showed significant thematic shifts, from an early internet and digital health focus to the dominance of COVID-19 and advanced technologies such as machine learning. Predictions for 2024-2026 suggest an increased focus on artificial intelligence, digital health, and mental health. Network analysis of JMIR publications provides a macroscopic view of the evolution of the digital health field. The journal's trajectory reflects broader technological advances and shifting research priorities, including the impact of the COVID-19 pandemic. The predicted trends underscore the growing importance of computational technology in future health care research and practice. The findings from JMIR provide a glimpse into the future of digital medicine, suggesting a robust integration of artificial intelligence and continued emphasis on mental health in the postpandemic era.

A thermodynamical analysis of steam reforming of Associated Petroleum Gas (APG) was conducted in the presented research. The reforming process of heavy hydrocarbons for small scale power generation is a complex issue. One of the main issues is that a set of undesired chemical reactions deposit solid carbon and, consequently, block the reactor’s catalytic property. The experimental investigation is crucial to design an APG reforming reactor. However, a numerical simulation is a key tool to design a safe operating condition. Designing the next generation of reactors requires a complex coupling of mathematical models, kinetics, and thermodynamic analysis. In practice, the thermodynamic analysis should be applied in each control volume to assure realistic results. This is not easy to apply in practice since both thermodynamic analysis and CFD modeling can be time-consuming. In this paper, the authors suggest using a mathematical formalism called Parametric Equation Formalism to calculate the equilibrium composition. The novelty lies in the mathematical approach in which any complex system at equilibrium can be reduced to the problem of solving one non-linear equation at a time. This approach allows implementing a thermodynamic analysis easily into CFD models to assure the reasonability of obtained results and can be used for research and development of solid oxide fuel cells as a part of hybrid energy systems.

The rapid development of large language models (LLMs) such as OpenAI's ChatGPT has significantly impacted medical research and education. These models have shown potential in fields ranging from radiological imaging interpretation to medical licensing examination assistance. Recently, LLMs have been enhanced with image recognition capabilities. This study aims to critically examine the effectiveness of these LLMs in medical diagnostics and training by assessing their accuracy and utility in answering image-based questions from medical licensing examinations. This study analyzed 1070 image-based multiple-choice questions from the AMBOSS learning platform, divided into 605 in English and 465 in German. Customized prompts in both languages directed the models to interpret medical images and provide the most likely diagnosis. Student performance data were obtained from AMBOSS, including metrics such as the \"student passed mean\" and \"majority vote.\" Statistical analysis was conducted using Python (Python Software Foundation), with key libraries for data manipulation and visualization. GPT-4 1106 Vision Preview (OpenAI) outperformed Bard Gemini Pro (Google), correctly answering 56.9% (609/1070) of questions compared to Bard's 44.6% (477/1070), a statistically significant difference (χ2₁=32.1, P<.001). However, GPT-4 1106 left 16.1% (172/1070) of questions unanswered, significantly higher than Bard's 4.1% (44/1070; χ2₁=83.1, P<.001). When considering only answered questions, GPT-4 1106's accuracy increased to 67.8% (609/898), surpassing both Bard (477/1026, 46.5%; χ2₁=87.7, P<.001) and the student passed mean of 63% (674/1070, SE 1.48%; χ2₁=4.8, P=.03). Language-specific analysis revealed both models performed better in German than English, with GPT-4 1106 showing greater accuracy in German (282/465, 60.65% vs 327/605, 54.1%; χ2₁=4.4, P=.04) and Bard Gemini Pro exhibiting a similar trend (255/465, 54.8% vs 222/605, 36.7%; χ2₁=34.3, P<.001). The student majority vote achieved an overall accuracy of 94.5% (1011/1070), significantly outperforming both artificial intelligence models (GPT-4 1106: χ2₁=408.5, P<.001; Bard Gemini Pro: χ2₁=626.6, P<.001). Our study shows that GPT-4 1106 Vision Preview and Bard Gemini Pro have potential in medical visual question-answering tasks and to serve as a support for students. However, their performance varies depending on the language used, with a preference for German. They also have limitations in responding to non-English content. The accuracy rates, particularly when compared to student responses, highlight the potential of these models in medical education, yet the need for further optimization and understanding of their limitations in diverse linguistic contexts remains critical.