Large language models (LLMs) are advanced artificial intelligence (AI) systems that excel in recognizing and generating human-like language, possibly serving as valuable tools for neurology-related information tasks. Although LLMs have shown remarkable potential in various areas, their performance in the dynamic environment of daily clinical practice remains uncertain. This article outlines multiple limitations and challenges of using LLMs in clinical settings that need to be addressed, including limited clinical reasoning, variable reliability and accuracy, reproducibility bias, self-serving bias, sponsorship bias, and potential for exacerbating health care disparities. These challenges are further compounded by practical business considerations and infrastructure requirements, including associated costs. To overcome these hurdles and harness the potential of LLMs effectively, this article includes considerations for health care organizations, researchers, and neurologists contemplating the use of LLMs in clinical practice. It is essential for health care organizations to cultivate a culture that welcomes AI solutions and aligns them seamlessly with health care operations. Clear objectives and business plans should guide the selection of AI solutions, ensuring they meet organizational needs and budget considerations. Engaging both clinical and nonclinical stakeholders can help secure necessary resources, foster trust, and ensure the long-term sustainability of AI implementations. Testing, validation, training, and ongoing monitoring are pivotal for successful integration. For neurologists, safeguarding patient data privacy is paramount. Seeking guidance from institutional information technology resources for informed, compliant decisions, and remaining vigilant against biases in LLM outputs are essential practices in responsible and unbiased utilization of AI tools. In research, obtaining institutional review board approval is crucial when dealing with patient data, even if deidentified, to ensure ethical use. Compliance with established guidelines like SPIRIT-AI, MI-CLAIM, and CONSORT-AI is necessary to maintain consistency and mitigate biases in AI research. In summary, the integration of LLMs into clinical neurology offers immense promise while presenting formidable challenges. Awareness of these considerations is vital for harnessing the potential of AI in neurologic care effectively and enhancing patient care quality and safety. The article serves as a guide for health care organizations, researchers, and neurologists navigating this transformative landscape.

Colorectal diseases are increasing due to altered lifestyle, genetic, and environmental factors. Colonoscopy plays an important role in diagnosis. Advances in colonoscope (ultrathin scope, magnetic scope, capsule) and technological gadgets (Balloon assisted scope, third eye retroscope, NaviAid G-EYE, dye-based chromoendoscopy, virtual chromoendoscopy, narrow band imaging, i-SCAN, etc. ) have made colonoscopy more comfortable and efficient. Now in-vivo microscopy can be performed using confocal laser endomicroscopy, optical coherence tomography, spectroscopy, etc. Besides developments in diagnostic colonoscopy, therapeutic colonoscopy has improved to manage lower gastrointestinal tract bleeding, obstruction, perforations, resection polyps, and early colorectal cancers. The introduction of combined endo-laparoscopic surgery and robotic endoscopic surgery has made these interventions feasible. The role of artificial intelligence in the diagnosis and management of colorectal diseases is also increasing day by day. Hence, this article is to review cutting-edge developments in endoscopic principles for the management of colorectal diseases.

The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.

The sphere of artificial intelligence (AI) is ever expanding. Applications for clinical practice have been emerging over recent years. Although its uptake has been most prominent in endoscopy, this represents only one aspect of holistic patient care. There are a multitude of other potential avenues in which gastrointestinal care may be involved. We aim to review the role of AI in colorectal cancer as a whole. We performed broad scoping and focused searches of the applications of AI in the field of colorectal cancer. All trials including qualitative research were included from the year 2000 onwards. Studies were grouped into pre-operative, intra-operative and post-operative aspects. Pre-operatively, the major use is with endoscopic recognition. Colonoscopy has embraced the use for human derived classifications such as Narrow-band Imaging International Colorectal Endoscopic, Japan Narrow-band Imaging Expert Team, Paris and Kudo. However, novel detection and diagnostic methods have arisen from advances in AI classification. Intra-operatively, adjuncts such as image enhanced identification of structures and assessment of perfusion have led to improvements in clinical outcomes. Post-operatively, monitoring and surveillance have taken strides with potential socioeconomic and environmental savings. The uses of AI within the umbrella of colorectal surgery are multiple. We have identified existing technologies which are already augmenting cancer care. The future applications are exciting and could at least match, if not surpass human standards.

BACKGROUND: Time in range (TIR) is a promising indicator of glycemic control used for evaluation of continuous glucose monitoring (CGM) for patients with diabetes mellitus (DM). The current problem is the assessment and prediction of TIR for patients who use self-monitoring of blood glucose (SМBG) corresponding low CGM availability for the majority of diabetic patients.AIM: To develop a predictive model of TIR for patients with T1DM based on data of the experimental mobile application.MATERIALS AND METHODS: An analysis of 1253 professional CGM profiles of patients with T1DM was performed. On the base of included records, TIR(CGM) was calculated and training models of 7-point SMBG profiles were generated. SMBG profiles’re loaded into the developed experimental mobile application that calculated standard glycemic control parameters. The dataset was divided into main and test samples (80 and 20%). For the main sample, the following methods’re used to develop predictive models: simple linear regression (SLR), multiple linear regression (MLR), artificial neural network (ANN). The effectiveness of the developed models was assessed on the test sample with the calculation of the mean absolute error (MAE), the root mean square error (RMSE).RESULTS: The 568 CGM profiles’re included in the study. TIR in the main group (n=454) — 45 [33; 65]%, in the test group (n=114) — 43 [33; 58]%. The most significant predictors of the regression models were the derived TIR (dTIR), p<0,001; derived time below range level 1 (dTBR1), p<0,001; standard deviation of blood glucose (SD), p=0,007. Determination coefficient for SLR (predictor: dTIR) — 0,844; for MLR (predictors: dTIR, dTBR1, SD) — 0,907. ANN multilayer perceptron models with two and one hidden layers’re developed, with the RMSE on the validation set 4,617 and 6,639%, respectively. The results of the forecast efficiency on the test sample were: dTIR: MAE — 6,82%, RMSE — 8,60%; SLR: MAE — 5,66%, RMSE — 7,34%; MLR: MAE — 4,18%, RMSE — 5,28%; ANN (2 layers): MAE — 4,14%, RMSE — 5,19%; ANN (1 layer): MAE — 4,44%, RMSE — 5,52%.CONCLUSION: ANN with two hidden layers and MLR demonstrated the best ability for TIR prediction. Further studies are required for clinical validation of developed prognostic models.

Purpose – The connection between participants’ scores on a resiliency instrument and their scores on the Schutte Self Report Emotional Intelligence Test (SSEIT) were examined. This is significant to human resource professionals because they can use the findings to develop and strengthen employees’ levels of resilience. The reasons for writing the paper are to provide data to human resource professionals so that they may develop workplace programs that build on emotional intelligence and resilience. Employees high in emotional intelligence are engaged employees. There is a gap in prior literature on the relationship between resilience and emotional intelligence and how this knowledge impacts human resource professionals. Aims(s) – The aims of this paper were to evaluate the relationship between resiliency and emotional intelligence. The study explored gender, age and GPA on resiliency and emotional intelligence. Design/methodology/approach –This was a quantitative research study. Participants answered questions on a six-point Likert-type scale ranging from strongly agree to strongly disagree relating to resiliency and emotional intelligence. There were two total scores. The convenient sample consisted of 266 undergraduate and graduate students. 197 females and 68 males, ages ranging from 18 to 65 years old. Findings – The data were analysed using SPSS (Statistical Package for the Social Sciences) 28.0 version. A Pearson correlation revealed a strong correlation between the scores on the SSEIT and the Resiliency scale (r=.599). Limitations of the study – Convenient sampling was used for this study. Participants self-reported. The implications for future research are to gather data from other industries and more globally. Practical implications – Findings suggest resilience and emotional intelligence are related and these skills can be developed through workplace training. Originality/value – Human resource practitioners can build a workforce equipped with the skills to develop relationships and a sense of self-awareness, they can lean on this knowledge to develop their employees and organizations.

Engineering companies, which are concerned with manufacturing activities in forming and welding technologies, consider with such a degree of an automation of the production process that will bring production improvement, lower production time and also economic benefits with return of investment. In the present time, businesses depend on a market economy conditions. Therefore, it is necessary to design production systems as very flexible with easy response to changes in a product range. In forming and welding technologies, which belong mostly to an area of wasteless technologies, an application of systems with a certain level of intelligence means a wide use of robots that replace the physical potential of people. They have a positive effect on a production quality and also on an ecology. [Hasse 2020]

Wireless energy transfer is emerging as a promising technology for mobile devices because it enhances rapid charging without requiring conventional cables. In this paper, a wireless mobile charger circuit was designed and simulated, the data obtained thereof was used to train an artificial neural network (ANN) using Levenberg-Marquardt (LM) algorithm. The result obtained was validated against that obtained when trained with regular scaled conjugate algorithm. Analysis of the results showed that the proposed technique remains a viable technique for rapidly analyzing several parts of the wireless mobile charger circuit for design and educational purposes, without always executing computationally intensive and time-consuming simulations.

Throughout history, interactions between humans and robots, and robots themselves have experienced profound shifts, generating a plethora of literature within the domains of manufacturing and services. Despite the abundance of scientific publications, consensus regarding these relationships remains elusive, fostering ambiguity within and between these domains. This article aims to present an encompassing and widely accepted perspective on the existing body of literature, seeking to unravel the nature of these relational paradigms. Our objective revolves around analyzing that zeroes in on the enhancement of Artificial Intelligence (AI) within Human-Robot relationships, particularly in the context of services. Henceforth, our research pivots toward a specialized body of literature exploring the domain of technical-biosocial systems, which encompasses human-technology-social structures. By doing so, we aim to analyze AI within a more delimited context, thereby mitigating the critique that a gap persists in scholarly discourse owing to the predominant analysis of AI within a global purview. Mindful of the potential drawbacks associated with a purely holistic and aggregated conceptual theoretical model, we have opted to subject our model to empirical validation. To achieve this, a case study was employed, with a specific focus on the avant-garde Henn na Cafe in Japan. The findings underline that currently, Henn na Cafe employs Human-Robot Interaction and Collaboration (HRI-C). However, there is an expectation that service companies will embrace an evolving HRI-C by integrating AI to enhance service delivery activities. Alternatively, they may opt for a Robot-Robot Interaction and Collaboration (RRI-C) approach with improved social capabilities or multi-human/multi-robot systems. The empirical findings are consistent with existing literature, which indicates a contemporary scholarly interest in investigating complex and emotional systems within service-oriented environments. These environments, characterized by elevated levels of AI, present challenges for AI systems in executing service functions effectively. Recent research endeavors aim to synthesize and assess the prevailing understanding and research directions within this domain. This convergence of findings not only reinforces theoretical research but also provides supplementary perspectives on human-robot relations and the complexities involved in service delivery within technologically sophisticated settings. Subsequent research endeavors could pivot towards a heightened focus on the services sector, exploring potential relationships and how AI might either fortify the services domain or pivot towards a vantage point centered on multi-robot systems.

<div class="WordSection1"><p>Depression is a mental illness that usually goes untreated in people and can have catastrophic consequences, including suicidal thoughts. Counselling services are widely available, but because depression is a stigmatized illness, many people who are depressed decide not to seek help. Therefore, it is essential to develop an automated system that can recognize depression in individuals before it worsens. In this study, a novel approach is proposed for identifying depression using a combination of visual and vocal emotions. Long short-term memory (LSTM) is used to assess verbal expressions and convolutional neural networks (CNN) to analyze facial expressions. The proposed system is trained using features of depression from the distress analysis interview corpus (DAIC) dataset and tested on videos of college students with frontal faces. The proposed approach is effective in detecting depression in individuals, with high accuracy and reliability.</p></div>